diff --git a/.buildinfo b/.buildinfo index 1026bf1..5dca3bd 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 393e8502f0ffee289868197a589b549d +config: ea01e99488d072d1b3173ff7b2dd2721 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_modules/index.html b/_modules/index.html index 0b6dc09..3070092 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -5,9 +5,10 @@ - Overview: module code - SummarizedExperiment 0.4.4 documentation + Overview: module code - SummarizedExperiment 0.4.5 documentation + @@ -178,7 +179,7 @@
-
SummarizedExperiment 0.4.4 documentation
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SummarizedExperiment 0.4.5 documentation
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- SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
-
+
diff --git a/_modules/summarizedexperiment/BaseSE.html b/_modules/summarizedexperiment/BaseSE.html index 75dd662..aa49c71 100644 --- a/_modules/summarizedexperiment/BaseSE.html +++ b/_modules/summarizedexperiment/BaseSE.html @@ -5,9 +5,10 @@ - summarizedexperiment.BaseSE - SummarizedExperiment 0.4.4 documentation + summarizedexperiment.BaseSE - SummarizedExperiment 0.4.5 documentation + @@ -178,7 +179,7 @@
-
SummarizedExperiment 0.4.4 documentation
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- SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
    -
  • Overview
    • -
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    • +
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    • +
  • Extending Classes
  • Module Reference @@ -396,6 +397,8 @@

    Source code for summarizedexperiment.BaseSE

         names.
     """
 
+
    
+[docs]
     def __init__(
         self,
         assays: Dict[str, Any] = None,
@@ -496,7 +499,8 @@ 
    Source code for summarizedexperiment.BaseSE
     
             _validate_rows(self._rows, self._row_names, self._shape)
             _validate_cols(self._cols, self._column_names, self._shape)
-            _validate_metadata(self._metadata)
+            _validate_metadata(self._metadata)
    
+
 
     def _define_output(self, in_place: bool = False) -> "BaseSE":
         if in_place is True:
@@ -1590,7 +1594,7 @@ 
    Source code for summarizedexperiment.BaseSE
           
     
   
    
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+
    diff --git a/_modules/summarizedexperiment/RangedSummarizedExperiment.html b/_modules/summarizedexperiment/RangedSummarizedExperiment.html index 828b669..e89f4eb 100644 --- a/_modules/summarizedexperiment/RangedSummarizedExperiment.html +++ b/_modules/summarizedexperiment/RangedSummarizedExperiment.html @@ -5,9 +5,10 @@ - summarizedexperiment.RangedSummarizedExperiment - SummarizedExperiment 0.4.4 documentation + summarizedexperiment.RangedSummarizedExperiment - SummarizedExperiment 0.4.5 documentation + @@ -178,7 +179,7 @@
-
SummarizedExperiment 0.4.4 documentation
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- SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
    -
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    • -
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    • +
  • Extending Classes
  • Module Reference @@ -357,6 +358,8 @@

    Source code for summarizedexperiment.RangedSummarizedExperiment

    :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment` instead. """ +
    +[docs] def __init__( self, assays: Dict[str, Any] = None, @@ -436,7 +439,8 @@

    Source code for summarizedexperiment.RangedSummarizedExperiment

    self._row_ranges = _sanitize_ranges_frame(row_ranges, self._shape[0]) if validate: - _validate_rowranges(self._row_ranges, self._shape) + _validate_rowranges(self._row_ranges, self._shape)
    + ######################### ######>> Copying <<###### @@ -1467,7 +1471,7 @@

    Source code for summarizedexperiment.RangedSummarizedExperiment

    -
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    diff --git a/_modules/summarizedexperiment/SummarizedExperiment.html b/_modules/summarizedexperiment/SummarizedExperiment.html index 379547a..e54e3af 100644 --- a/_modules/summarizedexperiment/SummarizedExperiment.html +++ b/_modules/summarizedexperiment/SummarizedExperiment.html @@ -5,9 +5,10 @@ - summarizedexperiment.SummarizedExperiment - SummarizedExperiment 0.4.4 documentation + summarizedexperiment.SummarizedExperiment - SummarizedExperiment 0.4.5 documentation + @@ -178,7 +179,7 @@
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- SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
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    Source code for summarizedexperiment.SummarizedExperiment

    SummarizedExperiment follows the R/Bioconductor specification; rows are features, columns are samples. """ +
    +[docs] def __init__( self, assays: Dict[str, Any] = None, @@ -363,6 +366,7 @@

    Source code for summarizedexperiment.SummarizedExperiment

    metadata=metadata, validate=validate, )
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    Source code for summarizedexperiment.SummarizedExperiment

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    diff --git a/_modules/summarizedexperiment/type_checks.html b/_modules/summarizedexperiment/type_checks.html index fdbaf2b..bd1e500 100644 --- a/_modules/summarizedexperiment/type_checks.html +++ b/_modules/summarizedexperiment/type_checks.html @@ -5,9 +5,10 @@ - summarizedexperiment.type_checks - SummarizedExperiment 0.4.4 documentation + summarizedexperiment.type_checks - SummarizedExperiment 0.4.5 documentation + @@ -178,7 +179,7 @@
    -
    SummarizedExperiment 0.4.4 documentation
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    - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
    +
    diff --git a/_sources/changelog.md.txt b/_sources/changelog.md.txt index b8ada44..56ed93f 100644 --- a/_sources/changelog.md.txt +++ b/_sources/changelog.md.txt @@ -1,5 +1,11 @@ # Changelog +## Version 0.4.2 - 0.4.5 + +- Fix issue coercing `SummarizedExperiments` to `AnnData` objects and vice-versa. +- Handling coercions when matrices are delayed arrays (for SE/RSE) or backed (for `AnnData`). +- Update sphinx configuration to run snippets in the documentation. + ## Version 0.4.0 to 0.4.1 This is a complete rewrite of the package, following the functional paradigm from our [developer notes](https://github.com/BiocPy/developer_guide#use-functional-discipline). diff --git a/_sources/extend_se.md.txt b/_sources/extend_se.md.txt new file mode 100644 index 0000000..73a0593 --- /dev/null +++ b/_sources/extend_se.md.txt @@ -0,0 +1,372 @@ +# Extending summarized experiments + +`SummarizedExperiment` and `RangedSummarizedExperiment` serve as the foundational container classes for representing experimental data and its metadata. Developers can extend these classes to create specialized data structures tailored for specific scientific applications. In fact, our implementation of `SingleCellExperiment` extends the `RangedSummarizedExperiment` class. + +## Define the new class + +As a simple example, let's create a new class called `BioSampleSE` that stores biosample information on which the experiment was conducted. This may contain anonymized information about the patient(s) or sample(s). First, we extend the `SummarizedExperiment` class: + + +```python +from summarizedexperiment import SummarizedExperiment + +class BioSampleSE(SummarizedExperiment): + pass +``` + +## Add a new slot or attribute + +To add a new slot to this class, we accept a new parameter `bio_sample_information` when the class is initialized through the __init__ method. We also provide type hints to set expectations on the accepted types for these arguments. Type hints are helpful for users and are automatically annotated in the documentation. More information on type hints can be found in our [developer guide](https://github.com/BiocPy/developer_guide). + +We forward the default parameters to the base `SummarizedExperiment` class (using the `super` method) and store the new attribute. + +```python +from summarizedexperiment import SummarizedExperiment +import biocframe +from typing import Dict, Any, List, Optional + +class BioSampleSE(SummarizedExperiment): + + def __init__( + self, + assays: Dict[str, Any] = None, + row_data: Optional[biocframe.BiocFrame] = None, + column_data: Optional[biocframe.BiocFrame] = None, + row_names: Optional[List[str]] = None, + column_names: Optional[List[str]] = None, + metadata: Optional[dict] = None, + bio_sample_information: Optional[biocframe.BiocFrame] = None, # NEW SLOT + validate: bool = True, + ) -> None: + super().__init__( + assays, + row_data=row_data, + column_data=column_data, + row_names=row_names, + column_names=column_names, + metadata=metadata, + validate=validate, + ) + + self._bio_sample_information = bio_sample_information +``` + +The new slot can be validated using a dedicated validator: + +```python +def _validate_bio_sample_information(bio_sample_info): + if not isinstance(bio_sample_info, biocframe.BiocFrame): + raise Exception("Biosample information must be a BiocFrame object.") + + # any other validations. for example, if you have expectations on the columns + # of this frame or the number of rows. +``` + +Our class now validates the new slot: + +```python +class BioSampleSE(SummarizedExperiment): + + def __init__( + self, + assays: Dict[str, Any] = None, + row_data: Optional[biocframe.BiocFrame] = None, + column_data: Optional[biocframe.BiocFrame] = None, + row_names: Optional[List[str]] = None, + column_names: Optional[List[str]] = None, + metadata: Optional[dict] = None, + bio_sample_information: Optional[biocframe.BiocFrame] = None, # NEW SLOT + validate: bool = True, + ) -> None: + super().__init__( + assays, + row_data=row_data, + column_data=column_data, + row_names=row_names, + column_names=column_names, + metadata=metadata, + validate=validate, + ) + + self._bio_sample_information = bio_sample_information + + if validate: + _validate_bio_sample_information(self._bio_sample_information) +``` + + +## Define getters/setters + +We need accessors and setters so users can interact with this new property. We provide these accessors in both functional and property-based approaches. More details can be found in our [class design](https://biocpy.github.io/tutorial/chapters/philosophy.html#functional-discipline) document. + +```python +def get_bio_sample_information(self) -> Optional[biocframe.BiocFrame]: + """Get biosample information. + + Returns: + biosample information or None if not availabl. + """ + return self._bio_sample_information + +def set_bio_sample_information( + self, bio_sample_information: Optional[biocframe.BiocFrame], in_place: bool = False +) -> "BioSampleSE": + """Set new biosample information. + + Args: + bio_sample_information: + A new `BiocFrame` object containing biosample information. + + in_place: + Whether to modify the ``BioSampleSE`` in place. + + Returns: + A modified ``BioSampleSE`` object, either as a copy of the original + or as a reference to the (in-place-modified) original. + """ + _validate_bio_sample_information(bio_sample_info) + + output = self._define_output(in_place) # MAKES A SHALLOW COPY + output._bio_sample_information = bio_sample_info + return output +``` + +Additionally, let's provide property-based accessors to the new attribute: + +```python +@property +def bio_sample_information(self) -> biocframe.BiocFrame: + """Alias for :py:meth:`~get_bio_sample_info`.""" + return self.get_bio_sample_info() + +@bio_sample_information.setter +def bio_sample_information(self, bio_sample_info: biocframe.BiocFrame) -> None: + """Alias for :py:meth:`~set_bio_sample_info`.""" + warn( + "Setting property 'bio_sample_information' is an in-place operation, use 'set_bio_sample_info' instead", + UserWarning, + ) + return self.set_bio_sample_information(row_ranges=row_ranges, in_place=True) + +``` + +This allows users to easily access the new property using the **dot** notation on an instance, for example, `obj.bio_sample_info` provides access to the attribute. + +## Define shallow and deep copy methods + +To avoid mutating objects in-place, methods for making shallow and deep copies of the class attributes are implemented. + +```python +def __deepcopy__(self, memo=None, _nil=[]): + """ + Returns: + A deep copy of the current ``BioSampleSE``. + """ + from copy import deepcopy + + _assays_copy = deepcopy(self._assays) + _rows_copy = deepcopy(self._rows) + _cols_copy = deepcopy(self._cols) + _row_names_copy = deepcopy(self._row_names) + _col_names_copy = deepcopy(self._column_names) + _bio_sample_information_copy = deepcopy(self._bio_sample_information) + _metadata_copy = deepcopy(self.metadata) + + current_class_const = type(self) + return current_class_const( + assays=_assays_copy, + row_data=_rows_copy, + column_data=_cols_copy, + row_names=_row_names_copy, + column_names=_col_names_copy, + bio_sample_information=_bio_sample_information_copy, + metadata=_metadata_copy, + ) + +def __copy__(self): + """ + Returns: + A shallow copy of the current ``BioSampleSE``. + """ + current_class_const = type(self) + return current_class_const( + assays=self._assays, + row_data=self._rows, + column_data=self._cols, + row_names=self._row_names, + column_names=self._column_names, + bio_sample_information=self._bio_sample_information, + metadata=self._metadata, + ) + +def copy(self): + """Alias for :py:meth:`~__copy__`.""" + return self.__copy__() +``` + +## Subset operation + +When the experiment is subsetted using the `[]` operator, we may also have to subset the new slots added to the extended class. Developers only need to extend the `get_slice` function from the base class that accepts row and column subsets. The `_generic_slice` function returns a `slicer` object containing normalized row and column indices, and properties from the base class. + +```python +from typing import Sequence + +def get_slice( + self, + rows: Optional[Union[str, int, bool, Sequence]], + columns: Optional[Union[str, int, bool, Sequence]], +) -> "BioSampleSE": + """Alias for :py:attr:`~__getitem__`, for back-compatibility.""" + + slicer = self._generic_slice(rows=rows, columns=columns) + + # An illustrative example + new_bio_sample_info = None + if slicer.row_indices != slice(None): + new_bio_sample_info = self.bio_sample_info[slicer.row_indices] + + current_class_const = type(self) + return current_class_const( + assays=slicer.assays, + row_data=slicer.rows, + column_data=slicer.columns, + row_names=slicer.row_names, + column_names=slicer.column_names, + bio_sample_info=self.bio_sample_info. + metadata=self._metadata, + ) +``` + +## Putting it all together + + +```python + +def _validate_bio_sample_information(bio_sample_info): + if not isinstance(bio_sample_info, biocframe.BiocFrame): + raise Exception("Biosample information must be a BiocFrame object.") + +class BioSampleSE(SummarizedExperiment): + + def __init__( + self, + assays: Dict[str, Any] = None, + row_data: Optional[biocframe.BiocFrame] = None, + column_data: Optional[biocframe.BiocFrame] = None, + row_names: Optional[List[str]] = None, + column_names: Optional[List[str]] = None, + metadata: Optional[dict] = None, + bio_sample_information: Optional[biocframe.BiocFrame] = None, # NEW SLOT + validate: bool = True, + ) -> None: + super().__init__( + assays, + row_data=row_data, + column_data=column_data, + row_names=row_names, + column_names=column_names, + metadata=metadata, + validate=validate, + ) + + self._bio_sample_information = bio_sample_information + + if validate: + _validate_bio_sample_information(self._bio_sample_information) + + def __deepcopy__(self, memo=None, _nil=[]): + """ + Returns: + A deep copy of the current ``BioSampleSE``. + """ + from copy import deepcopy + + _assays_copy = deepcopy(self._assays) + _rows_copy = deepcopy(self._rows) + _cols_copy = deepcopy(self._cols) + _row_names_copy = deepcopy(self._row_names) + _col_names_copy = deepcopy(self._column_names) + _bio_sample_information_copy = deepcopy(self._bio_sample_information) + _metadata_copy = deepcopy(self.metadata) + + current_class_const = type(self) + return current_class_const( + assays=_assays_copy, + row_data=_rows_copy, + column_data=_cols_copy, + row_names=_row_names_copy, + column_names=_col_names_copy, + bio_sample_information=_bio_sample_information_copy, + metadata=_metadata_copy, + ) + + def __copy__(self): + """ + Returns: + A shallow copy of the current ``BioSampleSE``. + """ + current_class_const = type(self) + return current_class_const( + assays=self._assays, + row_data=self._rows, + column_data=self._cols, + row_names=self._row_names, + column_names=self._column_names, + bio_sample_information=self._bio_sample_information, + metadata=self._metadata, + ) + + def copy(self): + """Alias for :py:meth:`~__copy__`.""" + return self.__copy__() + + + def get_bio_sample_information(self) -> Optional[biocframe.BiocFrame]: + """Get biosample information. + + Returns: + biosample information or None if not availabl. + """ + return self._bio_sample_information + + def set_bio_sample_information( + self, bio_sample_information: Optional[biocframe.BiocFrame], in_place: bool = False + ) -> "BioSampleSE": + """Set new biosample information. + + Args: + bio_sample_information: + A new `BiocFrame` object containing biosample information. + + in_place: + Whether to modify the ``BioSampleSE`` in place. + + Returns: + A modified ``BioSampleSE`` object, either as a copy of the original + or as a reference to the (in-place-modified) original. + """ + _validate_bio_sample_information(bio_sample_info) + + output = self._define_output(in_place) # MAKES A SHALLOW COPY + output._bio_sample_information = bio_sample_info + return output + + + @property + def bio_sample_information(self) -> biocframe.BiocFrame: + """Alias for :py:meth:`~get_bio_sample_info`.""" + return self.get_bio_sample_info() + + @bio_sample_information.setter + def bio_sample_information(self, bio_sample_info: biocframe.BiocFrame) -> None: + """Alias for :py:meth:`~set_bio_sample_info`.""" + warn( + "Setting property 'bio_sample_information' is an in-place operation, use 'set_bio_sample_info' instead", + UserWarning, + ) + return self.set_bio_sample_information(row_ranges=row_ranges, in_place=True) + + +``` + +That's the minimum required to extend a `SummarizedExperiment` and adapt it to new use cases. Please follow the [developer guide](https://github.com/BiocPy/developer_guide), which provides information on class design, package setup, and documentation to ensure consistency in how BiocPy-related packages are developed. diff --git a/_sources/index.md.txt b/_sources/index.md.txt index 1a2fc78..804cdda 100644 --- a/_sources/index.md.txt +++ b/_sources/index.md.txt @@ -1,10 +1,12 @@ # SummarizedExperiment -Container class to represent genomic experiments, follows Bioconductor's [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html). +This package provides containers to represent genomic experimental data as 2-dimensional matrices, follows Bioconductor's [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html). In these matrices, the rows typically denote features or genomic regions of interest, while columns represent samples or cells. + +The package currently includes representations for both `SummarizedExperiment` and `RangedSummarizedExperiment`. A distinction lies in the fact `RangedSummarizedExperiment` object provides an additional slot to store genomic regions for each feature and is expected to be `GenomicRanges` (more [here](https://github.com/BiocPy/GenomicRanges/)). ## Install -Package is published to [PyPI](https://pypi.org/project/summarizedexperiment/) +To get started, Install the package from [PyPI](https://pypi.org/project/summarizedexperiment/), ```shell pip install summarizedexperiment @@ -15,8 +17,8 @@ pip install summarizedexperiment ```{toctree} :maxdepth: 2 -Overview -Tutorial +Overview +Extending Classes Module Reference Contributions & Help License diff --git a/_sources/readme.md.txt b/_sources/readme.md.txt index 40866f8..92e2ad9 100644 --- a/_sources/readme.md.txt +++ b/_sources/readme.md.txt @@ -4,11 +4,13 @@ # SummarizedExperiment -Container to represent genomic experiments, follows Bioconductor's [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html). +This package provides containers to represent genomic experimental data as 2-dimensional matrices, follows Bioconductor's [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html). In these matrices, the rows typically denote features or genomic regions of interest, while columns represent samples or cells. + +The package currently includes representations for both `SummarizedExperiment` and `RangedSummarizedExperiment`. A distinction lies in the fact `RangedSummarizedExperiment` object provides an additional slot to store genomic regions for each feature and is expected to be `GenomicRanges` (more [here](https://github.com/BiocPy/GenomicRanges/)). ## Install -Package is published to [PyPI](https://pypi.org/project/summarizedexperiment/), +To get started, Install the package from [PyPI](https://pypi.org/project/summarizedexperiment/), ```shell pip install summarizedexperiment @@ -16,9 +18,13 @@ pip install summarizedexperiment ## Usage -Currently supports `SummarizedExperiment` & `RangedSummarizedExperiment` classes +A `SummarizedExperiment` contains three key attributes, + +- `assays`: A dictionary of matrices with assay names as keys, e.g. counts, logcounts etc. +- `row_data`: Feature information e.g. genes, transcripts, exons, etc. +- `column_data`: Sample information about the columns of the matrices. -First create necessary sample data +First lets mock feature and sample data: ```python from random import random diff --git a/_sources/tutorial.md.txt b/_sources/tutorial.md.txt index 57aff44..203c8e2 100644 --- a/_sources/tutorial.md.txt +++ b/_sources/tutorial.md.txt @@ -1,20 +1,26 @@ -# Tutorial +--- +file_format: mystnb +kernelspec: + name: python +--- + +# Represent genomic experimental data This package provides classes to represent genomic experiments, commonly generated by sequencing experiments. It provides similar interfaces and functionality as the [Bioconductor equivalent](https://github.com/Bioconductor/SummarizedExperiment). A `SummarizedExperiment` contains three slots, +- `assays`: A dictionary of matrices with assay names as keys, e.g. counts, logcounts etc. - `row_data`: Feature information e.g. genes, transcripts, exons, etc. - `column_data`: Sample information about the columns of the matrices. -- `assays`: A dictionary of matrices with assay names as keys, e.g. counts, logcounts etc. -The package currently provides both `SummarizedExperiment` & `RangedSummarizedExperiment` representations. A key difference between these two is the rows of a `RangedSummarizedExperiment` object is a [GenomicRanges](https://github.com/BiocPy/GenomicRanges), representing genomic regions of interest. +The package currently provides both `SummarizedExperiment` & `RangedSummarizedExperiment` representations. A key difference between these two is the `RangedSummarizedExperiment` object provides an additional slot to store genomic regions for each feature and is expected to be `GenomicRanges` (more [here](https://github.com/BiocPy/GenomicRanges/)). ## Construct a `SummarizedExperiment` object -First, lets create random experimental "count" data along with feature and sample information. +To construct a `SummarizedExperiment`, we'll first generate a matrix of read counts, representing the read counts from a series of RNA-seq experiments. Following that, we'll create a `BiocFrame` object to denote feature information and a table for column annotations. This table may include the names for the columns and any other values we wish to represent. -```python +```{code-cell} from random import random import pandas as pd import numpy as np @@ -53,130 +59,213 @@ col_data = pd.DataFrame( ) ``` +Note: The inputs `row_data` and `column_data` are expected to be `BiocFrame` objects and will be coerced to a `BiocFrame` if a pandas `DataFrame` is supplied. + ### `SummarizedExperiment` A `SummarizedExperiment` is a base class to represent any genomic experiment. This class expects features (`row_data`) to be either a pandas `DataFrame` or any variant of `BiocFrame`. -```python +```{code-cell} +from summarizedexperiment import SummarizedExperiment + se = SummarizedExperiment( assays={"counts": counts}, row_data=row_data, column_data=col_data ) + +print(se) ``` ### `RangedSummarizedExperiment` -`RangedSummarizedExperiment` requires features to be a [`GenomicRanges`](https://github.com/BiocPy/GenomicRanges) object. +Similarly, we can use the same information to construct a `RangeSummarizedExperiment`. We convert feature information into a `GenomicRanges` object and provide this as `row_ranges`: -```python -# convert our pandas dataframe to genomic ranges. +```{code-cell} from genomicranges import GenomicRanges +from summarizedexperiment import RangedSummarizedExperiment + gr = GenomicRanges.from_pandas(row_data.to_pandas()) -trse = RangedSummarizedExperiment( +rse = RangedSummarizedExperiment( assays={"counts": counts}, row_data=row_data, row_ranges=gr, column_data=col_data ) +print(rse) ``` -## Accessors +## Delayed or file-backed arrays + +The general idea is that [DelayedArray](https://github.com/biocpy/delayedarray)'s are a drop-in replacement for NumPy arrays, at least for [BiocPy](https://github.com/BiocPy) applications. + +For example, we can use the `DelayedArray` inside a `SummarizedExperiment`: -Properties can be accessed directly from the class instance. +```{code-cell} +import numpy +import delayedarray -```python -tse.assays -tse.row_data or # tse.row_ranges -tse.column_data -tse.metadata +# create a delayed array, can also be file-backed +x = numpy.random.rand(100, 20) +d = delayedarray.wrap(x) -# Access the counts assay -tse.assay("counts") +# operate over delayed arrays +filtered = d[1:100:2,1:8] +total = filtered.sum(axis=0) +normalized = filtered / total +transformed = numpy.log1p(normalized) + +import summarizedexperiment as SE +se_delayed = SE.SummarizedExperiment({ "counts": filtered, "lognorm": transformed }) +print(se_delayed) +``` + +## Interop with `anndata` + +Converting a `SummarizedExperiment` to an `AnnData` representation is straightforward: + +```{code-cell} +adata = se.to_anndata() +print(adata) ``` -## Subset an experiment +To convert an `AnnData` object to a BiocPy representation, utilize the `from_anndata` method in the [SingleCellExperiment](https://github.com/BiocPy/SingleCellExperiment) class. This minimizes the loss of information when converting between these two representations. -You can subset a `SummarizedExperiment` object using the `[]` slice operator. -### Slice by index position +## Getters/Setters -```python -# subset the first 10 rows and the first 3 samples -subset_tse = tse[0:10, 0:3] +Getters are available to access various attributes using either the property notation or functional style. + +```{code-cell} +# access assay names +print("assay names (as property): ", se.assay_names) +print("assay names (functional style): ", se.get_assay_names()) + +# access row data +print(se.row_data) ``` -### Slice by row names or column names +#### Access an assay -Alternatively, we can use a sequence of row (feature) or column (sample) names to subset a `SummarizedExperiment` . To demonstrate this, we create a `SummarizedExperiment` object with index names. +One can access an assay by index or name: -```python -row_data = BiocFrame( - { - "seqnames": ["chr_5", "chr_3", "chr_2"], - "start": [100, 200, 300], - "end": [110, 210, 310] - } -) -col_data = BiocFrame( - { - "sample": ["SAM_1", "SAM_3", "SAM_3"], - "disease": ["True", "True", "True"], +```{code-cell} +se.assay(0) # same as se.assay("counts") +``` + +### Setters + +All property-based setters are `in_place` operations, with further details discussed in [functional paradigm](https://biocpy.github.io/tutorial/chapters/philosophy.html#functional-discipline) section. + +```{code-cell} +modified_column_data = se.column_data.set_column("score", range(10,16)) +modified_se = se.set_column_data(modified_column_data) +print(modified_se) +``` + +Now, lets check the `column_data` on the original object. + +```{code-cell} +print(se.column_data) +``` + +## Subset experiments + +You can subset experimental data by using the subset (`[]`) operator. This operation accepts different slice input types, such as a boolean vector, a `slice` object, a list of indices, or names (if available) to subset. + +In our previous example, we didn't include row or column names. Let's create another `SummarizedExperiment` object that includes names. + +```{code-cell} + +row_data = BiocFrame({ + "seqnames": ["chr_5", "chr_3", "chr_2"], + "start": [100, 200, 300], + "end": [110, 210, 310], +}) + +col_data = BiocFrame({ + "sample": ["SAM_1", "SAM_3", "SAM_3"], + "disease": ["True", "True", "True"], }, - index=["cell_1", "cell_2", "cell_3"], + row_names=["cell_1", "cell_2", "cell_3"], ) -se_with_index_names = SummarizedExperiment( + +se_with_names = SummarizedExperiment( assays={ "counts": np.random.poisson(lam=5, size=(3, 3)), - "lognorm": np.random.lognormal(size=(3, 3)) + "lognorm": np.random.lognormal(size=(3, 3)), }, row_data=row_data, column_data=col_data, row_names=["HER2", "BRCA1", "TPFK"], - column_names=["cell_1", "cell_2", "cell_3"] + column_names=["cell_1", "cell_2", "cell_3"], ) -# subset by name -subset = se_with_index_names[ - ["HER2", "BRCA1"], ["cell_1", "cell_3"] -] +print(se_with_names) ``` -In the case a name does not exist, an error will be thrown: +### Subset by index position -```python -# throws error because "RAND" does not exist -subset_se_with_index_names = se_with_index_names[ - ["HER2", "BRCA1", "RAND"], ["cell_1", "cell_3"] -] +A straightforward slice operation: + +```{code-cell} +subset_se = se_with_names[0:10, 0:3] +print(subset_se) ``` -### Slice by a boolean vector +### Subset by row names or column names -Similarly you could also slice by a boolean array. **_Note, the boolean vectors should contain the same number of features for the row slice and the same number of samples for the column slice._** +Either one or both of the slices can contain names. These names are mapped to `row_names` and `column_names` of the `SummarizedExperiment` object. -```python -# subset with boolean array -subset_se_with_bools = se_with_index_names[ - [True, True, False], [True, False, True] -] +```{code-cell} +subset_se = se_with_names[:2, ["cell_1", "cell_3"]] +print(subset_se) ``` -## Range based operations +An `Exception` is raised if a names does not exist. + +### Subset by boolean vector -`RangedSummarizedExperiment` objects on the other hand supports many interval based operations similar to `GenomicRanges`. +Similarly, you can also slice by a boolean array. -```python -query = pd.DataFrame({"seqnames": ["chr2",], "starts": [4], "ends": [6], "strand": ["+"]}) +Note that the boolean vectors should contain the same number of features for the row slice and the same number of samples for the column slice. -query = GenomicRanges.from_pandas(query) +```{code-cell} +subset_se_with_bools = se_with_names[[True, True, False], [True, False, True]] +print(subset_se_with_bools) +``` + +### Subset by empty list + +This is a feature not a bug ;), you can specify an empty list to completely remove all rows or samples. -tse.subset_by_overlaps(query) +An empty array (`[]`) is not the same as an empty slice (`:`). This helps us avoid unintented operations. + +```{code-cell} +subset = se_with_names[:2, []] +print(subset) ``` -Checkout the [API docs](./api/modules.rst) or `GenomicRanges`` for list of interval based operations. +### Range-based operations + +Additionally, since `RangeSummarizedExperiment` contains `row_ranges`, this allows us to perform a number of range-based operations that are possible on a `GenomicRanges` object. + +For example, to subset `RangeSummarizedExperiment` with a **query** set of regions: + +```{code-cell} +from iranges import IRanges +query = GenomicRanges(seqnames=["chr2"], ranges=IRanges([4], [6]), strand=["+"]) + +result = rse.subset_by_overlaps(query) +print(result) +``` -## Combine operations +Additionally, RSE supports many other interval based operations. Checkout the [documentation](https://biocpy.github.io/SummarizedExperiment/api/modules.html) for more details. -The combine methods in `SummarizedExperiment` are used to merge or combine multiple `SummarizedExperiment` objects, allowing users to aggregate data from different experiments or conditions. First, let's create multiple `SummarizedExperiment` objects for combining later. +## Combining experiments + +`SummarizedExperiment` implements methods for the `combine` generics from [**BiocUtils**](https://github.com/BiocPy/biocutils). + +These methods enable the merging or combining of multiple `SummarizedExperiment` objects, allowing users to aggregate data from different experiments or conditions. To demonstrate, let's create multiple `SummarizedExperiment` objects. + +```{code-cell} -```python rowData1 = pd.DataFrame( { "seqnames": ["chr_5", "chr_3", "chr_2"], @@ -253,13 +342,50 @@ se3 = SummarizedExperiment( column_data=colData3, metadata={"seq_platform": "Illumina NovaSeq 6000"}, ) + +print(se1) +print(se2) +print(se3) ``` -```python -from biocutils import relaxed_combine_columns, combine_columns +The `combine_rows` or `combine_columns` operations, expect all experiments to contain the same assay names. + +To combine experiments by row: + +```{code-cell} +from biocutils import relaxed_combine_columns, combine_columns, combine_rows, relaxed_combine_rows +se_combined = combine_rows(se2, se1) +print(se_combined) +``` + +Similarly to combine by column: + +```{code-cell} se_combined = combine_columns(se2, se1) +print(se_combined) +``` + +You can use `relaxed_combine_columns` or `relaxed_combined_rows` when there's mismatch in the number of features or samples. 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+div.highlight .-Color[class*=-BGC33] { + background-color: #0087FF +} + +div.highlight .-Color[class*=-C34] { + color: #00AF00 +} + +div.highlight .-Color[class*=-BGC34] { + background-color: #00AF00 +} + +div.highlight .-Color[class*=-C35] { + color: #00AF5F +} + +div.highlight .-Color[class*=-BGC35] { + background-color: #00AF5F +} + +div.highlight .-Color[class*=-C36] { + color: #00AF87 +} + +div.highlight .-Color[class*=-BGC36] { + background-color: #00AF87 +} + +div.highlight .-Color[class*=-C37] { + color: #00AFAF +} + +div.highlight .-Color[class*=-BGC37] { + background-color: #00AFAF +} + +div.highlight .-Color[class*=-C38] { + color: #00AFD7 +} + +div.highlight .-Color[class*=-BGC38] { + background-color: #00AFD7 +} + +div.highlight .-Color[class*=-C39] { + color: #00AFFF +} + +div.highlight .-Color[class*=-BGC39] { + background-color: #00AFFF +} + +div.highlight .-Color[class*=-C40] { + color: #00D700 +} + +div.highlight .-Color[class*=-BGC40] { + background-color: #00D700 +} + +div.highlight .-Color[class*=-C41] { + color: #00D75F +} + +div.highlight .-Color[class*=-BGC41] { + background-color: #00D75F +} + +div.highlight .-Color[class*=-C42] { + color: #00D787 +} + +div.highlight .-Color[class*=-BGC42] { + background-color: #00D787 +} + +div.highlight .-Color[class*=-C43] { + color: #00D7AF +} + +div.highlight .-Color[class*=-BGC43] { + background-color: #00D7AF +} + +div.highlight .-Color[class*=-C44] { + color: #00D7D7 +} + +div.highlight .-Color[class*=-BGC44] { + background-color: #00D7D7 +} + +div.highlight .-Color[class*=-C45] { + color: #00D7FF +} + +div.highlight .-Color[class*=-BGC45] { + background-color: #00D7FF +} + +div.highlight .-Color[class*=-C46] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC46] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C47] { + color: #00FF5F +} + +div.highlight .-Color[class*=-BGC47] { + background-color: #00FF5F +} + +div.highlight .-Color[class*=-C48] { + color: #00FF87 +} + +div.highlight .-Color[class*=-BGC48] { + background-color: #00FF87 +} + +div.highlight .-Color[class*=-C49] { + color: #00FFAF +} + +div.highlight .-Color[class*=-BGC49] { + background-color: #00FFAF +} + +div.highlight .-Color[class*=-C50] { + color: #00FFD7 +} + +div.highlight .-Color[class*=-BGC50] { + background-color: #00FFD7 +} + +div.highlight .-Color[class*=-C51] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC51] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C52] { + color: #5F0000 +} + +div.highlight .-Color[class*=-BGC52] { + background-color: #5F0000 +} + +div.highlight .-Color[class*=-C53] { + color: #5F005F +} + +div.highlight .-Color[class*=-BGC53] { + background-color: #5F005F +} + +div.highlight .-Color[class*=-C54] { + color: #5F0087 +} + +div.highlight .-Color[class*=-BGC54] { + background-color: #5F0087 +} + +div.highlight .-Color[class*=-C55] { + color: #5F00AF +} + +div.highlight .-Color[class*=-BGC55] { + background-color: #5F00AF +} + +div.highlight .-Color[class*=-C56] { + color: #5F00D7 +} + +div.highlight .-Color[class*=-BGC56] { + background-color: #5F00D7 +} + +div.highlight .-Color[class*=-C57] { + color: #5F00FF +} + +div.highlight .-Color[class*=-BGC57] { + background-color: #5F00FF +} + +div.highlight .-Color[class*=-C58] { + color: #5F5F00 +} + +div.highlight .-Color[class*=-BGC58] { + background-color: #5F5F00 +} + +div.highlight .-Color[class*=-C59] { + color: #5F5F5F +} + +div.highlight .-Color[class*=-BGC59] { + background-color: #5F5F5F +} + +div.highlight .-Color[class*=-C60] { + color: #5F5F87 +} + +div.highlight .-Color[class*=-BGC60] { + background-color: #5F5F87 +} + +div.highlight .-Color[class*=-C61] { + color: #5F5FAF +} + +div.highlight .-Color[class*=-BGC61] { + background-color: #5F5FAF +} + +div.highlight .-Color[class*=-C62] { + color: #5F5FD7 +} + +div.highlight .-Color[class*=-BGC62] { + background-color: #5F5FD7 +} + +div.highlight .-Color[class*=-C63] { + color: #5F5FFF +} + +div.highlight .-Color[class*=-BGC63] { + background-color: #5F5FFF +} + +div.highlight .-Color[class*=-C64] { + color: #5F8700 +} + +div.highlight .-Color[class*=-BGC64] { + background-color: #5F8700 +} + +div.highlight .-Color[class*=-C65] { + color: #5F875F +} + +div.highlight .-Color[class*=-BGC65] { + background-color: #5F875F +} + +div.highlight .-Color[class*=-C66] { + color: #5F8787 +} + +div.highlight .-Color[class*=-BGC66] { + background-color: #5F8787 +} + +div.highlight .-Color[class*=-C67] { + color: #5F87AF +} + +div.highlight .-Color[class*=-BGC67] { + background-color: #5F87AF +} + +div.highlight .-Color[class*=-C68] { + color: #5F87D7 +} + +div.highlight .-Color[class*=-BGC68] { + background-color: #5F87D7 +} + +div.highlight .-Color[class*=-C69] { + color: #5F87FF +} + +div.highlight .-Color[class*=-BGC69] { + background-color: #5F87FF +} + +div.highlight .-Color[class*=-C70] { + color: #5FAF00 +} + +div.highlight .-Color[class*=-BGC70] { + background-color: #5FAF00 +} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + +div.highlight .-Color[class*=-BGC130] { + background-color: #AF5F00 +} + +div.highlight .-Color[class*=-C131] { + color: #AF5F5F +} + +div.highlight .-Color[class*=-BGC131] { + background-color: #AF5F5F +} + +div.highlight .-Color[class*=-C132] { + color: #AF5F87 +} + +div.highlight .-Color[class*=-BGC132] { + background-color: #AF5F87 +} + +div.highlight .-Color[class*=-C133] { + color: #AF5FAF +} + +div.highlight .-Color[class*=-BGC133] { + background-color: #AF5FAF +} + +div.highlight .-Color[class*=-C134] { + color: #AF5FD7 +} + +div.highlight .-Color[class*=-BGC134] { + background-color: #AF5FD7 +} + +div.highlight .-Color[class*=-C135] { + color: #AF5FFF +} + +div.highlight .-Color[class*=-BGC135] { + background-color: #AF5FFF +} + +div.highlight .-Color[class*=-C136] { + color: #AF8700 +} + +div.highlight .-Color[class*=-BGC136] { + background-color: #AF8700 +} + +div.highlight .-Color[class*=-C137] { + color: #AF875F +} + +div.highlight .-Color[class*=-BGC137] { + background-color: #AF875F +} + +div.highlight .-Color[class*=-C138] { + color: #AF8787 +} + +div.highlight .-Color[class*=-BGC138] { + background-color: #AF8787 +} + +div.highlight .-Color[class*=-C139] { + color: #AF87AF +} + +div.highlight .-Color[class*=-BGC139] { + background-color: #AF87AF +} + +div.highlight .-Color[class*=-C140] { + color: #AF87D7 +} + +div.highlight .-Color[class*=-BGC140] { + background-color: #AF87D7 +} + +div.highlight .-Color[class*=-C141] { + color: #AF87FF +} + +div.highlight .-Color[class*=-BGC141] { + background-color: #AF87FF +} + +div.highlight .-Color[class*=-C142] { + color: #AFAF00 +} + +div.highlight .-Color[class*=-BGC142] { + background-color: #AFAF00 +} + +div.highlight .-Color[class*=-C143] { + color: #AFAF5F +} + +div.highlight .-Color[class*=-BGC143] { + background-color: #AFAF5F +} + +div.highlight .-Color[class*=-C144] { + color: #AFAF87 +} + +div.highlight .-Color[class*=-BGC144] { + background-color: #AFAF87 +} + +div.highlight .-Color[class*=-C145] { + color: #AFAFAF +} + +div.highlight .-Color[class*=-BGC145] { + background-color: #AFAFAF +} + +div.highlight .-Color[class*=-C146] { + color: #AFAFD7 +} + +div.highlight .-Color[class*=-BGC146] { + background-color: #AFAFD7 +} + +div.highlight .-Color[class*=-C147] { + color: #AFAFFF +} + +div.highlight .-Color[class*=-BGC147] { + background-color: #AFAFFF +} + +div.highlight .-Color[class*=-C148] { + color: #AFD700 +} + +div.highlight .-Color[class*=-BGC148] { + background-color: #AFD700 +} + +div.highlight .-Color[class*=-C149] { + color: #AFD75F +} + +div.highlight .-Color[class*=-BGC149] { + background-color: #AFD75F +} + +div.highlight .-Color[class*=-C150] { + color: #AFD787 +} + +div.highlight .-Color[class*=-BGC150] { + background-color: #AFD787 +} + +div.highlight .-Color[class*=-C151] { + color: #AFD7AF +} + +div.highlight .-Color[class*=-BGC151] { + background-color: #AFD7AF +} + +div.highlight .-Color[class*=-C152] { + color: #AFD7D7 +} + +div.highlight .-Color[class*=-BGC152] { + background-color: #AFD7D7 +} + +div.highlight .-Color[class*=-C153] { + color: #AFD7FF +} + +div.highlight .-Color[class*=-BGC153] { + background-color: #AFD7FF +} + +div.highlight .-Color[class*=-C154] { + color: #AFFF00 +} + +div.highlight .-Color[class*=-BGC154] { + background-color: #AFFF00 +} + +div.highlight .-Color[class*=-C155] { + color: #AFFF5F +} + +div.highlight .-Color[class*=-BGC155] { + background-color: #AFFF5F +} + +div.highlight .-Color[class*=-C156] { + color: #AFFF87 +} + +div.highlight .-Color[class*=-BGC156] { + background-color: #AFFF87 +} + +div.highlight .-Color[class*=-C157] { + color: #AFFFAF +} + +div.highlight .-Color[class*=-BGC157] { + background-color: #AFFFAF +} + +div.highlight .-Color[class*=-C158] { + color: #AFFFD7 +} + +div.highlight .-Color[class*=-BGC158] { + background-color: #AFFFD7 +} + +div.highlight .-Color[class*=-C159] { + color: #AFFFFF +} + +div.highlight .-Color[class*=-BGC159] { + background-color: #AFFFFF +} + +div.highlight .-Color[class*=-C160] { + color: #D70000 +} + +div.highlight .-Color[class*=-BGC160] { + background-color: #D70000 +} + +div.highlight .-Color[class*=-C161] { + color: #D7005F +} + +div.highlight .-Color[class*=-BGC161] { + background-color: #D7005F +} + +div.highlight .-Color[class*=-C162] { + color: #D70087 +} + +div.highlight .-Color[class*=-BGC162] { + background-color: #D70087 +} + +div.highlight .-Color[class*=-C163] { + color: #D700AF +} + +div.highlight .-Color[class*=-BGC163] { + background-color: #D700AF +} + +div.highlight .-Color[class*=-C164] { + color: #D700D7 +} + +div.highlight .-Color[class*=-BGC164] { + background-color: #D700D7 +} + +div.highlight .-Color[class*=-C165] { + color: #D700FF +} + +div.highlight .-Color[class*=-BGC165] { + background-color: #D700FF +} + +div.highlight .-Color[class*=-C166] { + color: #D75F00 +} + +div.highlight .-Color[class*=-BGC166] { + background-color: #D75F00 +} + +div.highlight 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background-color: #FF5F5F +} + +div.highlight .-Color[class*=-C204] { + color: #FF5F87 +} + +div.highlight .-Color[class*=-BGC204] { + background-color: #FF5F87 +} + +div.highlight .-Color[class*=-C205] { + color: #FF5FAF +} + +div.highlight .-Color[class*=-BGC205] { + background-color: #FF5FAF +} + +div.highlight .-Color[class*=-C206] { + color: #FF5FD7 +} + +div.highlight .-Color[class*=-BGC206] { + background-color: #FF5FD7 +} + +div.highlight .-Color[class*=-C207] { + color: #FF5FFF +} + +div.highlight .-Color[class*=-BGC207] { + background-color: #FF5FFF +} + +div.highlight .-Color[class*=-C208] { + color: #FF8700 +} + +div.highlight .-Color[class*=-BGC208] { + background-color: #FF8700 +} + +div.highlight .-Color[class*=-C209] { + color: #FF875F +} + +div.highlight .-Color[class*=-BGC209] { + background-color: #FF875F +} + +div.highlight .-Color[class*=-C210] { + color: #FF8787 +} + +div.highlight .-Color[class*=-BGC210] { + background-color: #FF8787 +} + +div.highlight .-Color[class*=-C211] { + color: #FF87AF +} + +div.highlight .-Color[class*=-BGC211] { + background-color: #FF87AF +} + +div.highlight .-Color[class*=-C212] { + color: #FF87D7 +} + +div.highlight .-Color[class*=-BGC212] { + background-color: #FF87D7 +} + +div.highlight .-Color[class*=-C213] { + color: #FF87FF +} + +div.highlight .-Color[class*=-BGC213] { + background-color: #FF87FF +} + +div.highlight .-Color[class*=-C214] { + color: #FFAF00 +} + +div.highlight .-Color[class*=-BGC214] { + background-color: #FFAF00 +} + +div.highlight .-Color[class*=-C215] { + color: #FFAF5F +} + +div.highlight .-Color[class*=-BGC215] { + background-color: #FFAF5F +} + +div.highlight .-Color[class*=-C216] { + color: #FFAF87 +} + +div.highlight .-Color[class*=-BGC216] { + background-color: #FFAF87 +} + +div.highlight .-Color[class*=-C217] { + color: #FFAFAF +} + +div.highlight .-Color[class*=-BGC217] { + background-color: #FFAFAF +} + +div.highlight .-Color[class*=-C218] { + color: #FFAFD7 +} + +div.highlight .-Color[class*=-BGC218] { + background-color: #FFAFD7 +} + +div.highlight .-Color[class*=-C219] { + color: #FFAFFF +} + +div.highlight .-Color[class*=-BGC219] { + background-color: #FFAFFF +} + +div.highlight .-Color[class*=-C220] { + color: #FFD700 +} + +div.highlight .-Color[class*=-BGC220] { + background-color: #FFD700 +} + +div.highlight .-Color[class*=-C221] { + color: #FFD75F +} + +div.highlight .-Color[class*=-BGC221] { + background-color: #FFD75F +} + +div.highlight .-Color[class*=-C222] { + color: #FFD787 +} + +div.highlight .-Color[class*=-BGC222] { + background-color: #FFD787 +} + +div.highlight .-Color[class*=-C223] { + color: #FFD7AF +} + +div.highlight .-Color[class*=-BGC223] { + background-color: #FFD7AF +} + +div.highlight .-Color[class*=-C224] { + color: #FFD7D7 +} + +div.highlight .-Color[class*=-BGC224] { + background-color: #FFD7D7 +} + +div.highlight .-Color[class*=-C225] { + color: #FFD7FF +} + +div.highlight .-Color[class*=-BGC225] { + background-color: #FFD7FF +} + +div.highlight .-Color[class*=-C226] { + color: #FFFF00 +} + +div.highlight .-Color[class*=-BGC226] { + background-color: #FFFF00 +} + +div.highlight .-Color[class*=-C227] { + color: #FFFF5F +} + +div.highlight .-Color[class*=-BGC227] { + background-color: #FFFF5F +} + +div.highlight .-Color[class*=-C228] { + color: #FFFF87 +} + +div.highlight .-Color[class*=-BGC228] { + background-color: #FFFF87 +} + +div.highlight .-Color[class*=-C229] { + color: #FFFFAF +} + +div.highlight .-Color[class*=-BGC229] { + background-color: #FFFFAF +} + +div.highlight .-Color[class*=-C230] { + color: #FFFFD7 +} + +div.highlight .-Color[class*=-BGC230] { + background-color: #FFFFD7 +} + +div.highlight .-Color[class*=-C231] { + color: #FFFFFF +} + +div.highlight .-Color[class*=-BGC231] { + background-color: #FFFFFF +} + +div.highlight .-Color[class*=-C232] { + color: #080808 +} + +div.highlight .-Color[class*=-BGC232] { + background-color: #080808 +} + +div.highlight .-Color[class*=-C233] { + color: #121212 +} + +div.highlight .-Color[class*=-BGC233] { + background-color: #121212 +} + +div.highlight .-Color[class*=-C234] { + color: #1C1C1C +} + +div.highlight .-Color[class*=-BGC234] { + background-color: #1C1C1C +} + +div.highlight .-Color[class*=-C235] { + color: #262626 +} + +div.highlight .-Color[class*=-BGC235] { + background-color: #262626 +} + +div.highlight .-Color[class*=-C236] { + color: #303030 +} + +div.highlight .-Color[class*=-BGC236] { + background-color: #303030 +} + +div.highlight .-Color[class*=-C237] { + color: #3A3A3A +} + +div.highlight .-Color[class*=-BGC237] { + background-color: #3A3A3A +} + +div.highlight .-Color[class*=-C238] { + color: #444444 +} + +div.highlight .-Color[class*=-BGC238] { + background-color: #444444 +} + +div.highlight .-Color[class*=-C239] { + color: #4E4E4E +} + +div.highlight .-Color[class*=-BGC239] { + background-color: #4E4E4E +} + +div.highlight .-Color[class*=-C240] { + color: #585858 +} + +div.highlight .-Color[class*=-BGC240] { + background-color: #585858 +} + +div.highlight .-Color[class*=-C241] { + color: #626262 +} + +div.highlight .-Color[class*=-BGC241] { + background-color: #626262 +} + +div.highlight .-Color[class*=-C242] { + color: #6C6C6C +} + +div.highlight .-Color[class*=-BGC242] { + background-color: #6C6C6C +} + +div.highlight .-Color[class*=-C243] { + color: #767676 +} + +div.highlight .-Color[class*=-BGC243] { + background-color: #767676 +} + +div.highlight .-Color[class*=-C244] { + color: #808080 +} + +div.highlight .-Color[class*=-BGC244] { + background-color: #808080 +} + +div.highlight .-Color[class*=-C245] { + color: #8A8A8A +} + +div.highlight .-Color[class*=-BGC245] { + background-color: #8A8A8A +} + +div.highlight .-Color[class*=-C246] { + color: #949494 +} + +div.highlight .-Color[class*=-BGC246] { + background-color: #949494 +} + +div.highlight .-Color[class*=-C247] { + color: #9E9E9E +} + +div.highlight .-Color[class*=-BGC247] { + background-color: #9E9E9E +} + +div.highlight .-Color[class*=-C248] { + color: #A8A8A8 +} + +div.highlight .-Color[class*=-BGC248] { + background-color: #A8A8A8 +} + +div.highlight .-Color[class*=-C249] { + color: #B2B2B2 +} + +div.highlight .-Color[class*=-BGC249] { + background-color: #B2B2B2 +} + +div.highlight .-Color[class*=-C250] { + color: #BCBCBC +} + +div.highlight .-Color[class*=-BGC250] { + background-color: #BCBCBC +} + +div.highlight .-Color[class*=-C251] { + color: #C6C6C6 +} + +div.highlight .-Color[class*=-BGC251] { + background-color: #C6C6C6 +} + +div.highlight .-Color[class*=-C252] { + color: #D0D0D0 +} + +div.highlight .-Color[class*=-BGC252] { + background-color: #D0D0D0 +} + +div.highlight .-Color[class*=-C253] { + color: #DADADA +} + +div.highlight .-Color[class*=-BGC253] { + background-color: #DADADA +} + +div.highlight .-Color[class*=-C254] { + color: #E4E4E4 +} + +div.highlight .-Color[class*=-BGC254] { + background-color: #E4E4E4 +} + +div.highlight .-Color[class*=-C255] { + color: #EEEEEE +} + +div.highlight .-Color[class*=-BGC255] { + background-color: #EEEEEE +} diff --git a/api/modules.html b/api/modules.html index 8bc134d..d486746 100644 --- a/api/modules.html +++ b/api/modules.html @@ -3,12 +3,13 @@ - + - summarizedexperiment - SummarizedExperiment 0.4.4 documentation + summarizedexperiment - SummarizedExperiment 0.4.5 documentation + @@ -179,7 +180,7 @@
    -
    SummarizedExperiment 0.4.4 documentation
     +
    SummarizedExperiment 0.4.5 documentation
    @@ -203,7 +204,7 @@
    - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
    -
    +
    diff --git a/api/summarizedexperiment.html b/api/summarizedexperiment.html index 5d70b88..eda64b4 100644 --- a/api/summarizedexperiment.html +++ b/api/summarizedexperiment.html @@ -6,9 +6,10 @@ - summarizedexperiment package - SummarizedExperiment 0.4.4 documentation + summarizedexperiment package - SummarizedExperiment 0.4.5 documentation + @@ -179,7 +180,7 @@
    -
    SummarizedExperiment 0.4.4 documentation
     +
    SummarizedExperiment 0.4.5 documentation
    @@ -203,7 +204,7 @@
    - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
      -
    • Overview
      • -
    • Tutorial
      • +
    • Overview
      • +
    • Extending Classes
    • Module Reference @@ -271,7 +272,7 @@

      Submodules

      summarizedexperiment.BaseSE module

      -class summarizedexperiment.BaseSE.BaseSE(assays: Dict[str, Any] | None = None, row_data: BiocFrame | None = None, column_data: BiocFrame | None = None, row_names: List[str] | None = None, column_names: List[str] | None = None, metadata: dict | None = None, validate: bool = True)[source]
      +class summarizedexperiment.BaseSE.BaseSE(assays=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

      Bases: object

      Base class for SummarizedExperiment. This class provides common properties and methods that can be utilized across all derived classes.

      @@ -304,11 +305,11 @@

      Submodules
      -__getitem__(args: int | str | Sequence | tuple) → BaseSE[source]
      +__getitem__(args)[source]

      Subset a SummarizedExperiment.

      Parameters:
      -

      args –

      Integer indices, a boolean filter, or (if the current object is +

      args (Union[int, str, Sequence, tuple]) –

      Integer indices, a boolean filter, or (if the current object is named) names specifying the ranges to be extracted, see normalize_subscript().

      Alternatively a tuple of length 1. The first entry specifies @@ -321,39 +322,92 @@

      SubmodulesRaises:

      ValueError – If too many or too few slices provided.

      -
      Returns:
      -

      Same type as caller with the sliced rows and columns.

      +
      Return type:
      +

      BaseSE

      +
      +
      Returns:
      +

      Same type as caller with the sliced rows and columns.

      +
      +

      +

      + +
      +
      +__init__(assays=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]
      +

      Initialize an instance of BaseSE.

      +
      +
      Parameters:
      +
        +

      • assays (Optional[Dict[str, Any]]) –

        A dictionary containing matrices, with assay names as keys +and 2-dimensional matrices represented as either +ndarray or spmatrix.

        +

        Alternatively, you may use any 2-dimensional matrix that has +the shape property and implements the slice operation +using the __getitem__ dunder method.

        +

        All matrices in assays must be 2-dimensional and have the +same shape (number of rows, number of columns).

        +

        • +

      • row_data (Optional[BiocFrame]) –

        Features, must be the same length as the number of rows of +the matrices in assays.

        +

        Feature information is coerced to a +BiocFrame. Defaults to None.

        +

        • +

      • column_data (Optional[BiocFrame]) –

        Sample data, must be the same length as the number of +columns of the matrices in assays.

        +

        Sample information is coerced to a +BiocFrame. Defaults to None.

        +

        • +

      • row_names (Optional[List[str]]) –

        A list of strings, same as the number of rows.

        +

        If row_names are not provided, these are inferred from +row_data.

        +

        Defaults to None.

        +

        • +

      • column_names (Optional[List[str]]) –

        A list of string, same as the number of columns.

        +

        if column_names are not provided, these are inferred from +column_data.

        +

        Defaults to None.

        +

        • +

      • metadata (Optional[dict]) – Additional experimental metadata describing the methods. +Defaults to None.

        • +

      • validate (bool) – Internal use only.

        • +
      -__len__() → int[source]
      +__len__()[source]
      -
      Returns:
      -

      Number of rows.

      +
      Return type:
      +

      int

      +
      +
      Returns:
      +

      Number of rows.

      -__repr__() → str[source]
      +__repr__()[source]
      -
      Returns:
      -

      A string representation.

      +
      Return type:
      +

      str

      +
      +
      Returns:
      +

      A string representation.

      -assay(assay: int | str) → Any[source]
      +assay(assay)[source]

      Convenience method to access an assays by name or index.

      Parameters:
      -

      assay – Name or index position of the assay.

      +

      assay (Union[int, str]) – Name or index position of the assay.

      Raises:
        @@ -361,8 +415,11 @@

        SubmodulesIndexError – If index is greater than the number of assays.

      -
      Returns:
      -

      Experiment data.

      +
      Return type:
      +

      Any

      +
      +
      Returns:
      +

      Experiment data.

      @@ -387,7 +444,7 @@

      Submodules
      -property col_names: Names | None
      +property col_names: Names | None

      Alias for get_column_names, provided for back-compatibility.

      @@ -399,7 +456,7 @@

      Submodules
      -property colnames: Names | None
      +property colnames: Names | None

      Alias for get_column_names, provided for back-compatibility.

      @@ -411,7 +468,7 @@

      Submodules
      -property column_names: Names | None
      +property column_names: Names | None

      Alias for get_column_names, provided for back-compatibility.

      @@ -423,7 +480,7 @@

      Submodules
      -property columnnames: Names | None
      +property columnnames: Names | None

      Alias for get_column_names, provided for back-compatibility.

      @@ -451,22 +508,28 @@

      Submodules
      -get_assay_names() → List[str][source]
      +get_assay_names()[source]

      Get assay names.

      -
      Returns:
      -

      List of assay names.

      +
      Return type:
      +

      List[str]

      +
      +
      Returns:
      +

      List of assay names.

      -get_assays() → Dict[str, Any][source]
      +get_assays()[source]

      Access assays/experimental data.

      -
      Returns:
      -

      A dictionary with keys as assay names and value +

      Return type:
      +

      Dict[str, Any]

      +
      +
      Returns:
      +

      A dictionary with keys as assay names and value the experimental data.

      @@ -474,72 +537,92 @@

      Submodules
      -get_column_data(replace_row_names: bool = True) → BiocFrame[source]
      +get_column_data(replace_row_names=True)[source]

      Get sample data.

      Parameters:
      -

      replace_row_names –

      Whether to replace column_data’s row_names with the +

      replace_row_names (bool) –

      Whether to replace column_data’s row_names with the row_names from the experiment.

      Defaults to True.

      -
      Returns:
      -

      Sample information.

      +
      Return type:
      +

      BiocFrame

      +
      +
      Returns:
      +

      Sample information.

      -get_column_names() → Names | None[source]
      +get_column_names()[source]
      -
      Returns:
      -

      List of column names, or None if no column names are available.

      +
      Return type:
      +

      Optional[Names]

      +
      +
      Returns:
      +

      List of column names, or None if no column names are available.

      -get_metadata() → dict[source]
      +get_metadata()[source]
      -
      Returns:
      -

      Dictionary of metadata for this object.

      +
      Return type:
      +

      dict

      +
      +
      Returns:
      +

      Dictionary of metadata for this object.

      -get_row_data(replace_row_names: bool = True) → BiocFrame[source]
      +get_row_data(replace_row_names=True)[source]

      Get features, the row_names of row_data are replaced by the row_names from the experiment.

      Parameters:
      -

      replace_row_names –

      Whether to replace row_data’s row_names with the row_names +

      replace_row_names (bool) –

      Whether to replace row_data’s row_names with the row_names from the experiment.

      Defaults to True.

      -
      Returns:
      -

      Feature information.

      +
      Return type:
      +

      BiocFrame

      +
      +
      Returns:
      +

      Feature information.

      -get_row_names() → Names | None[source]
      +get_row_names()[source]
      -
      Returns:
      -

      List of row names, or None if no row names are available.

      +
      Return type:
      +

      Optional[Names]

      +
      +
      Returns:
      +

      List of row names, or None if no row names are available.

      -get_slice(rows: str | int | bool | Sequence | None, columns: str | int | bool | Sequence | None) → BaseSE[source]
      +get_slice(rows, columns)[source]

      Alias for __getitem__, for back-compatibility.

      +
      +
      Return type:
      +

      BaseSE

      +
      +
      @@ -556,7 +639,7 @@

      Submodules
      -property row_names: Names | None
      +property row_names: Names | None

      Alias for get_row_names, provided for back-compatibility.

      @@ -568,23 +651,26 @@

      Submodules
      -property rownames: Names | None
      +property rownames: Names | None

      Alias for get_row_names, provided for back-compatibility.

      -set_assay_names(names: List[str], in_place: bool = False) → BaseSE[source]
      +set_assay_names(names, in_place=False)[source]

      Replace assays’s names.

      Parameters:
        -

      • names – New names.

        • -

      • in_place – Whether to modify the BaseSE in place.

        • +

      • names (List[str]) – New names.

        • +

      • in_place (bool) – Whether to modify the BaseSE in place.

      -
      Returns:
      -

      A modified BaseSE object, either as a copy of the original +

      Return type:
      +

      BaseSE

      +
      +
      Returns:
      +

      A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -592,17 +678,20 @@

      Submodules
      -set_assays(assays: Dict[str, Any], in_place: bool = False) → BaseSE[source]
      +set_assays(assays, in_place=False)[source]

      Set new experiment data (assays).

      Parameters:
        -

      • assays – New assays.

        • -

      • in_place – Whether to modify the BaseSE in place.

        • +

      • assays (Dict[str, Any]) – New assays.

        • +

      • in_place (bool) – Whether to modify the BaseSE in place.

      -
      Returns:
      -

      A modified BaseSE object, either as a copy of the original +

      Return type:
      +

      BaseSE

      +
      +
      Returns:
      +

      A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -610,23 +699,26 @@

      Submodules
      -set_column_data(cols: BiocFrame | None, replace_column_names: bool = False, in_place: bool = False) → BaseSE[source]
      +set_column_data(cols, replace_column_names=False, in_place=False)[source]

      Set sample data.

      Parameters:
        -

      • cols –

        New sample data.

        +

      • cols (Optional[BiocFrame]) –

        New sample data.

        If cols is None, an empty -BiocFrame +BiocFrame object is created.

        • -

      • replace_column_names – Whether to replace experiment’s column_names with the names from the +

      • replace_column_names (bool) – Whether to replace experiment’s column_names with the names from the new object. Defaults to False.

        • -

      • in_place – Whether to modify the BaseSE in place.

        • +

      • in_place (bool) – Whether to modify the BaseSE in place.

      -
      Returns:
      -

      A modified BaseSE object, either as a copy of the original +

      Return type:
      +

      BaseSE

      +
      +
      Returns:
      +

      A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -634,19 +726,22 @@

      Submodules
      -set_column_names(names: List[str] | None, in_place: bool = False) → BaseSE[source]
      +set_column_names(names, in_place=False)[source]

      Set new column names.

      Parameters:
        -

      • names –

        New names, same as the number of columns.

        +

      • names (Optional[List[str]]) –

        New names, same as the number of columns.

        May be None to remove column names.

        • -

      • in_place – Whether to modify the BaseSE in place.

        • +

      • in_place (bool) – Whether to modify the BaseSE in place.

      -
      Returns:
      -

      A modified BaseSE object, either as a copy of the original +

      Return type:
      +

      BaseSE

      +
      +
      Returns:
      +

      A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -654,17 +749,20 @@

      Submodules
      -set_metadata(metadata: dict, in_place: bool = False) → BaseSE[source]
      +set_metadata(metadata, in_place=False)[source]

      Set additional metadata.

      Parameters:
        -

      • metadata – New metadata for this object.

        • -

      • in_place – Whether to modify the BaseSE in place.

        • +

      • metadata (dict) – New metadata for this object.

        • +

      • in_place (bool) – Whether to modify the BaseSE in place.

      -
      Returns:
      -

      A modified BaseSE object, either as a copy of the original +

      Return type:
      +

      BaseSE

      +
      +
      Returns:
      +

      A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -672,23 +770,26 @@

      Submodules
      -set_row_data(rows: BiocFrame | None, replace_row_names: bool = False, in_place: bool = False) → BaseSE[source]
      +set_row_data(rows, replace_row_names=False, in_place=False)[source]

      Set new feature information.

      Parameters:
        -

      • rows –

        New feature information.

        +

      • rows (Optional[BiocFrame]) –

        New feature information.

        If rows is None, an empty -BiocFrame +BiocFrame object is created.

        • -

      • replace_row_names – Whether to replace experiment’s row_names with the names from the +

      • replace_row_names (bool) – Whether to replace experiment’s row_names with the names from the new object. Defaults to False.

        • -

      • in_place – Whether to modify the BaseSE in place.

        • +

      • in_place (bool) – Whether to modify the BaseSE in place.

      -
      Returns:
      -

      A modified BaseSE object, either as a copy of the original +

      Return type:
      +

      BaseSE

      +
      +
      Returns:
      +

      A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -696,19 +797,22 @@

      Submodules
      -set_row_names(names: List[str] | None, in_place: bool = False) → BaseSE[source]
      +set_row_names(names, in_place=False)[source]

      Set new row names.

      Parameters:
        -

      • names –

        New names, same as the number of rows.

        +

      • names (Optional[List[str]]) –

        New names, same as the number of rows.

        May be None to remove row names.

        • -

      • in_place – Whether to modify the BaseSE in place.

        • +

      • in_place (bool) – Whether to modify the BaseSE in place.

      -
      Returns:
      -

      A modified BaseSE object, either as a copy of the original +

      Return type:
      +

      BaseSE

      +
      +
      Returns:
      +

      A modified BaseSE object, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -732,27 +836,30 @@

      Submodules
      -subset_assays(rows: str | int | bool | Sequence | None, columns: str | int | bool | Sequence | None) → Dict[str, Any][source]
      +subset_assays(rows, columns)[source]

      Subset all assays by the slice defined by rows and columns.

      If both row_indices and col_indices are None, a shallow copy of the current assays is returned.

      Parameters:
        -

      • rows –

        Row indices to subset.

        +

      • rows (Union[str, int, bool, Sequence, None]) –

        Row indices to subset.

        Integer indices, a boolean filter, or (if the current object is named) names specifying the ranges to be extracted, see normalize_subscript().

        • -

      • columns –

        Column indices to subset.

        +

      • columns (Union[str, int, bool, Sequence, None]) –

        Column indices to subset.

        Integer indices, a boolean filter, or (if the current object is named) names specifying the ranges to be extracted, see normalize_subscript().

      -
      Returns:
      -

      Sliced experiment data.

      +
      Return type:
      +

      Dict[str, Any]

      +
      +
      Returns:
      +

      Sliced experiment data.

      @@ -781,13 +888,13 @@

      Submodules

      summarizedexperiment.RangedSummarizedExperiment module

      -class summarizedexperiment.RangedSummarizedExperiment.RangedSummarizedExperiment(assays: Dict[str, Any] | None = None, row_ranges: GenomicRanges | GenomicRangesList | None = None, row_data: BiocFrame | None = None, column_data: BiocFrame | None = None, row_names: List[str] | None = None, column_names: List[str] | None = None, metadata: dict | None = None, validate: bool = True)[source]
      +class summarizedexperiment.RangedSummarizedExperiment.RangedSummarizedExperiment(assays=None, row_ranges=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

      Bases: SummarizedExperiment

      RangedSummarizedExperiment class to represent genomic experiment data, genomic features as -GenomicRanges or -GenomicRangesList sample data and any additional experimental metadata.

      +GenomicRanges or +GenomicRangesList sample data and any additional experimental metadata.

      Note: If row_ranges is empty, None or not a -genomicranges.GenomicRanges.GenomicRanges object, use a +genomicranges.GenomicRanges.GenomicRanges object, use a SummarizedExperiment instead.

      @@ -809,12 +916,61 @@

      Submodules +
      +__init__(assays=None, row_ranges=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]
      +

      Initialize a RangedSummarizedExperiment (RSE) object.

      +
      +
      Parameters:
      +
        +

      • assays (Optional[Dict[str, Any]]) –

        A dictionary containing matrices, with assay names as keys +and 2-dimensional matrices represented as either +ndarray or spmatrix.

        +

        Alternatively, you may use any 2-dimensional matrix that has +the shape property and implements the slice operation +using the __getitem__ dunder method.

        +

        All matrices in assays must be 2-dimensional and have the +same shape (number of rows, number of columns).

        +

        • +

      • row_ranges (Union[GenomicRanges, GenomicRangesList, None]) – Genomic features, must be the same length as the number of rows of +the matrices in assays.

        • +

      • row_data (Optional[BiocFrame]) –

        Features, must be the same length as the number of rows of +the matrices in assays.

        +

        Feature information is coerced to a +BiocFrame. Defaults to None.

        +

        • +

      • column_data (Optional[BiocFrame]) –

        Sample data, must be the same length as the number of +columns of the matrices in assays.

        +

        Sample information is coerced to a +BiocFrame. Defaults to None.

        +

        • +

      • row_names (Optional[List[str]]) –

        A list of strings, same as the number of rows.

        +

        If row_names are not provided, these are inferred from +row_data.

        +

        Defaults to None.

        +

        • +

      • column_names (Optional[List[str]]) –

        A list of string, same as the number of columns.

        +

        if column_names are not provided, these are inferred from +column_data.

        +

        Defaults to None.

        +

        • +

      • metadata (Optional[dict]) – Additional experimental metadata describing the methods. +Defaults to None.

        • +

      • validate (bool) – Internal use only.

        • +
      +
      +
      +

      +
      -__repr__() → str[source]
      +__repr__()[source]
      -
      Returns:
      -

      A string representation.

      +
      Return type:
      +

      str

      +
      +
      Returns:
      +

      A string representation.

      @@ -827,19 +983,22 @@

      Submodules
      -coverage(shift: int = 0, width: int | None = None, weight: int = 1) → Dict[str, ndarray][source]
      +coverage(shift=0, width=None, weight=1)[source]

      Calculate coverage for each chromosome.

      Parameters:
        -

      • shift – Shift all genomic positions. Defaults to 0.

        • -

      • width – Restrict the width of all +

      • shift (int) – Shift all genomic positions. Defaults to 0.

        • +

      • width (Optional[int]) – Restrict the width of all chromosomes. Defaults to None.

        • -

      • weight – Weight to use. Defaults to 1.

        • +

      • weight (int) – Weight to use. Defaults to 1.

      -
      Returns:
      -

      A dictionary with chromosome names as keys and the +

      Return type:
      +

      Dict[str, ndarray]

      +
      +
      Returns:
      +

      A dictionary with chromosome names as keys and the coverage vector as value.

      @@ -858,17 +1017,17 @@

      Submodules
      -find_overlaps(query: GenomicRanges | GenomicRangesList | RangedSummarizedExperiment, query_type: str = 'any', select: Literal['all', 'first', 'last', 'arbitrary'] = 'all', max_gap: int = -1, min_overlap: int = 1, ignore_strand: bool = False) → List[List[int]][source]
      +find_overlaps(query, query_type='any', select='all', max_gap=-1, min_overlap=1, ignore_strand=False)[source]

      Find overlaps between subject (self) and query ranges.

      Parameters:
        -

      • query –

        Query intervals to find nearest positions.

        -

        query may be a GenomicRanges or a +

      • query (Union[GenomicRanges, GenomicRangesList, RangedSummarizedExperiment]) –

        Query intervals to find nearest positions.

        +

        query may be a GenomicRanges or a RangedSummarizedExperiment object.

        • -

      • query_type –

        Overlap query type, must be one of

        +

      • query_type (str) –

        Overlap query type, must be one of

        • ”any”: Any overlap is good

        • ”start”: Overlap at the beginning of the intervals

          • @@ -877,19 +1036,22 @@

          SubmodulesLiteral['all', 'first', 'last', 'arbitrary']) – Determine what hit to choose when there are multiple hits for an interval in subject.

          -

        • max_gap – Maximum gap allowed in the overlap. +

        • max_gap (int) – Maximum gap allowed in the overlap. Defaults to -1 (no gap allowed).

          • -

        • min_overlap – Minimum overlap with query. Defaults to 1.

          • -

        • ignore_strand – Whether to ignore strands. Defaults to False.

          • +

        • min_overlap (int) – Minimum overlap with query. Defaults to 1.

          • +

        • ignore_strand (bool) – Whether to ignore strands. Defaults to False.

      Raises:

      TypeError – If query is not a RangedSummarizedExperiment or GenomicRanges.

      -
      Returns:
      -

      A list with the same length as query, +

      Return type:
      +

      List[List[int]]

      +
      +
      Returns:
      +

      A list with the same length as query, containing hits to overlapping indices.

      @@ -897,22 +1059,25 @@

      Submodules
      -flank(width: int, start: bool = True, both: bool = False, ignore_strand: bool = False, in_place: bool = False) → RangedSummarizedExperiment[source]
      +flank(width, start=True, both=False, ignore_strand=False, in_place=False)[source]

      Compute flanking ranges for each range.

      -

      Refer to either flank() or the +

      Refer to either flank() or the Bioconductor documentation for more details.

      Parameters:
        -

      • width – Width to flank by. May be negative.

        • -

      • start – Whether to only flank starts. Defaults to True.

        • -

      • both – Whether to flank both starts and ends. Defaults to False.

        • -

      • ignore_strand – Whether to ignore strands. Defaults to False.

        • -

      • in_place – Whether to modify the GenomicRanges object in place.

        • +

      • width (int) – Width to flank by. May be negative.

        • +

      • start (bool) – Whether to only flank starts. Defaults to True.

        • +

      • both (bool) – Whether to flank both starts and ends. Defaults to False.

        • +

      • ignore_strand (bool) – Whether to ignore strands. Defaults to False.

        • +

      • in_place (bool) – Whether to modify the GenomicRanges object in place.

      -
      Returns:
      -

      A new RangedSummarizedExperiment object with the flanked ranges, +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A new RangedSummarizedExperiment object with the flanked ranges, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -921,19 +1086,19 @@

      Submodules
      -follow(query: GenomicRanges | GenomicRangesList | RangedSummarizedExperiment, select: Literal['all', 'arbitrary'] = 'all', ignore_strand: bool = False) → List[int | None] | None[source]
      +follow(query, select='all', ignore_strand=False)[source]

      Search nearest positions only upstream that overlap with each range in query.

      Parameters:
      Raises:
      @@ -942,8 +1107,11 @@

      SubmodulesReturns: -

      A List with the same length as query, +

      Return type:
      +

      Optional[List[Optional[int]]]

      +
      +
      Returns:
      +

      A List with the same length as query, containing hits to nearest indices.

      @@ -951,37 +1119,48 @@

      Submodules
      -get_row_ranges() → GenomicRanges | GenomicRangesList[source]
      +get_row_ranges()[source]

      Get genomic feature information.

      -
      Returns:
      -

      Genomic feature information.

      +
      Return type:
      +

      Union[GenomicRanges, GenomicRangesList]

      +
      +
      Returns:
      +

      Genomic feature information.

      -get_slice(rows: str | int | bool | Sequence | None, columns: str | int | bool | Sequence | None) → RangedSummarizedExperiment[source]
      +get_slice(rows, columns)[source]

      Alias for __getitem__, for back-compatibility.

      +
      +
      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      -narrow(start: int | List[int] | ndarray | None = None, width: int | List[int] | ndarray | None = None, end: int | List[int] | ndarray | None = None, in_place: bool = False) → RangedSummarizedExperiment[source]
      +narrow(start=None, width=None, end=None, in_place=False)[source]

      Narrow genomic positions by provided start, width and end parameters.

      Important: these parameters are relative shift in positions for each range.

      Parameters:
        -

      • start – Relative start position. Defaults to None.

        • -

      • width – Relative end position. Defaults to None.

        • -

      • end – Relative width of the interval. Defaults to None.

        • -

      • in_place – Whether to modify the GenomicRanges object in place.

        • +

      • start (Union[int, List[int], ndarray, None]) – Relative start position. Defaults to None.

        • +

      • width (Union[int, List[int], ndarray, None]) – Relative end position. Defaults to None.

        • +

      • end (Union[int, List[int], ndarray, None]) – Relative width of the interval. Defaults to None.

        • +

      • in_place (bool) – Whether to modify the GenomicRanges object in place.

      -
      Returns:
      -

      A new RangedSummarizedExperiment object with narrow positions, +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A new RangedSummarizedExperiment object with narrow positions, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -990,27 +1169,30 @@

      Submodules
      -nearest(query: GenomicRanges | GenomicRangesList | RangedSummarizedExperiment, select: Literal['all', 'arbitrary'] = 'all', ignore_strand: bool = False) → List[int | None] | None[source]
      +nearest(query, select='all', ignore_strand=False)[source]

      Search nearest positions both upstream and downstream that overlap with each range in query.

      Parameters:
      Raises:

      TypeError – If query is not a RangedSummarizedExperiment or GenomicRanges.

      -
      Returns:
      -

      A list with the same length as query, +

      Return type:
      +

      Optional[List[Optional[int]]]

      +
      +
      Returns:
      +

      A list with the same length as query, containing hits to nearest indices.

      @@ -1018,41 +1200,47 @@

      Submodules
      -order(decreasing: bool = False) → ndarray[source]
      +order(decreasing=False)[source]

      Get the order of indices to sort.

      Parameters:
      -

      decreasing – Whether to sort in descending order. Defaults to False.

      +

      decreasing (bool) – Whether to sort in descending order. Defaults to False.

      -
      Returns:
      -

      NumPy vector containing index positions in the sorted order.

      +
      Return type:
      +

      ndarray

      +
      +
      Returns:
      +

      NumPy vector containing index positions in the sorted order.

      -precede(query: GenomicRanges | GenomicRangesList | RangedSummarizedExperiment, select: Literal['all', 'arbitrary'] = 'all', ignore_strand: bool = False) → List[int | None] | None[source]
      +precede(query, select='all', ignore_strand=False)[source]

      Search nearest positions only downstream that overlap with each range in query.

      Parameters:
      Raises:

      TypeError – If query is not a RangedSummarizedExperiment or GenomicRanges.

      -
      Returns:
      -

      A List with the same length as query, +

      Return type:
      +

      Optional[List[Optional[int]]]

      +
      +
      Returns:
      +

      A List with the same length as query, containing hits to nearest indices.

      @@ -1060,20 +1248,23 @@

      Submodules
      -promoters(upstream: int = 2000, downstream: int = 200, in_place: bool = False) → RangedSummarizedExperiment[source]
      +promoters(upstream=2000, downstream=200, in_place=False)[source]

      Extend intervals to promoter regions.

      Parameters:
        -

      • upstream – Number of positions to extend in the 5’ direction. +

      • upstream (int) – Number of positions to extend in the 5’ direction. Defaults to 2000.

        • -

      • downstream – Number of positions to extend in the 3’ direction. +

      • downstream (int) – Number of positions to extend in the 3’ direction. Defaults to 200.

        • -

      • in_place – Whether to modify the GenomicRanges object in place.

        • +

      • in_place (bool) – Whether to modify the GenomicRanges object in place.

      -
      Returns:
      -

      A new RangedSummarizedExperiment object with the extended ranges for +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A new RangedSummarizedExperiment object with the extended ranges for promoter regions, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -1082,24 +1273,27 @@

      Submodules
      -resize(width: int | List[int] | ndarray, fix: Literal['start', 'end', 'center'] = 'start', ignore_strand: bool = False, in_place: bool = False) → RangedSummarizedExperiment[source]
      +resize(width, fix='start', ignore_strand=False, in_place=False)[source]

      Resize ranges to the specified width where either the start, end, or center is used as an anchor.

      Parameters:
        -

      • width – Width to resize, cannot be negative!

        • -

      • fix – Fix positions by “start”, “end”, or “center”. +

      • width (Union[int, List[int], ndarray]) – Width to resize, cannot be negative!

        • +

      • fix (Literal['start', 'end', 'center']) – Fix positions by “start”, “end”, or “center”. Defaults to “start”.

        • -

      • ignore_strand – Whether to ignore strands. Defaults to False.

        • -

      • in_place – Whether to modify the GenomicRanges object in place.

        • +

      • ignore_strand (bool) – Whether to ignore strands. Defaults to False.

        • +

      • in_place (bool) – Whether to modify the GenomicRanges object in place.

      Raises:

      ValueError – If fix is neither start, center, or end.

      -
      Returns:
      -

      A new RangedSummarizedExperiment object with the resized ranges, +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A new RangedSummarizedExperiment object with the resized ranges, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -1108,20 +1302,23 @@

      Submodules
      -restrict(start: int | List[int] | ndarray | None = None, end: int | List[int] | ndarray | None = None, keep_all_ranges: bool = False, in_place: bool = False) → RangedSummarizedExperiment[source]
      +restrict(start=None, end=None, keep_all_ranges=False, in_place=False)[source]

      Restrict ranges to a given start and end positions.

      Parameters:
        -

      • start – Start position. Defaults to None.

        • -

      • end – End position. Defaults to None.

        • -

      • keep_all_ranges – Whether to keep intervals that do not overlap with start and end. +

      • start (Union[int, List[int], ndarray, None]) – Start position. Defaults to None.

        • +

      • end (Union[int, List[int], ndarray, None]) – End position. Defaults to None.

        • +

      • keep_all_ranges (bool) – Whether to keep intervals that do not overlap with start and end. Defaults to False.

        • -

      • in_place – Whether to modify the GenomicRanges object in place.

        • +

      • in_place (bool) – Whether to modify the GenomicRanges object in place.

      -
      Returns:
      -

      A new RangedSummarizedExperiment object with restricted intervals, +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A new RangedSummarizedExperiment object with restricted intervals, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -1130,13 +1327,13 @@

      Submodules
      -property row_ranges: GenomicRanges | GenomicRangesList
      +property row_ranges: GenomicRanges | GenomicRangesList

      Alias for get_rowranges().

      -property seq_info: SeqInfo
      +property seq_info: SeqInfo

      Get sequence information object (if available).

      Returns:
      @@ -1158,18 +1355,21 @@

      Submodules
      -set_row_ranges(row_ranges: GenomicRanges | GenomicRangesList | None, in_place: bool = False) → RangedSummarizedExperiment[source]
      +set_row_ranges(row_ranges, in_place=False)[source]

      Set new genomic features.

      Parameters:
        -

      • row_ranges – Genomic features, must be the same length as the number of rows of +

      • row_ranges (Union[GenomicRanges, GenomicRangesList, None]) – Genomic features, must be the same length as the number of rows of the matrices in assays.

        • -

      • in_place – Whether to modify the RangeSummarizedExperiment in place.

        • +

      • in_place (bool) – Whether to modify the RangeSummarizedExperiment in place.

      -
      Returns:
      -

      A modified RangeSummarizedExperiment object, either as a copy of the original +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A modified RangeSummarizedExperiment object, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -1177,19 +1377,22 @@

      Submodules
      -shift(shift: int | List[int] | ndarray = 0, in_place: bool = False) → RangedSummarizedExperiment[source]
      +shift(shift=0, in_place=False)[source]

      Shift all intervals.

      shift may be be negative.

      Parameters:
        -

      • shift – Shift interval. If shift is 0, the current +

      • shift (Union[int, List[int], ndarray]) – Shift interval. If shift is 0, the current object is returned. Defaults to 0.

        • -

      • in_place – Whether to modify the GenomicRanges object in place.

        • +

      • in_place (bool) – Whether to modify the GenomicRanges object in place.

      -
      Returns:
      -

      A new RangedSummarizedExperiment object with the shifted ranges, +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A new RangedSummarizedExperiment object with the shifted ranges, either as a copy of the original or as a reference to the (in-place-modified) original.

      @@ -1198,17 +1401,20 @@

      Submodules
      -sort(decreasing: bool = False, in_place: bool = False) → RangedSummarizedExperiment[source]
      +sort(decreasing=False, in_place=False)[source]

      Sort by ranges.

      Parameters:
        -

      • decreasing – Whether to sort in descending order. Defaults to False.

        • -

      • in_place – Whether to modify the object in place. Defaults to False.

        • +

      • decreasing (bool) – Whether to sort in descending order. Defaults to False.

        • +

      • in_place (bool) – Whether to modify the object in place. Defaults to False.

      -
      Returns:
      -

      A new sorted RangedSummarizedExperiment object.

      +
      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A new sorted RangedSummarizedExperiment object.

      @@ -1237,17 +1443,17 @@

      Submodules
      -subset_by_overlaps(query: GenomicRanges | GenomicRangesList | RangedSummarizedExperiment, query_type: str = 'any', max_gap: int = -1, min_overlap: int = 1, ignore_strand: bool = False) → RangedSummarizedExperiment[source]
      +subset_by_overlaps(query, query_type='any', max_gap=-1, min_overlap=1, ignore_strand=False)[source]

      Subset a RangedSummarizedExperiment by feature overlaps.

      Parameters:
        -

      • query –

        Query GenomicRanges.

        -

        query may be a GenomicRanges or a +

      • query (Union[GenomicRanges, GenomicRangesList, RangedSummarizedExperiment]) –

        Query GenomicRanges.

        +

        query may be a GenomicRanges or a RangedSummarizedExperiment object.

        • -

      • query_type –

        Overlap query type, must be one of

        +

      • query_type (str) –

        Overlap query type, must be one of

        • ”any”: Any overlap is good

        • ”start”: Overlap at the beginning of the intervals

          • @@ -1256,17 +1462,20 @@

          Submodulesint) – Maximum gap allowed in the overlap. Defaults to -1 (no gap allowed).

          -

        • min_overlap – Minimum overlap with query. Defaults to 1.

          • -

        • ignore_strand – Whether to ignore strands. Defaults to False.

          • +

        • min_overlap (int) – Minimum overlap with query. Defaults to 1.

          • +

        • ignore_strand (bool) – Whether to ignore strands. Defaults to False.

      Raises:

      TypeError – If query is not a RangedSummarizedExperiment or GenomicRanges.

      -
      Returns:
      -

      A new RangedSummarizedExperiment +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A new RangedSummarizedExperiment object. None if there are no indices to slice.

      @@ -1287,44 +1496,53 @@

      Submodules
      -summarizedexperiment.RangedSummarizedExperiment.combine_columns(*x: RangedSummarizedExperiment) → RangedSummarizedExperiment[source]
      +summarizedexperiment.RangedSummarizedExperiment.combine_columns(*x)[source]

      Combine multiple RangedSummarizedExperiment objects by column.

      All assays must contain the same assay names. If you need a flexible combine operation, checkout relaxed_combine_columns().

      -
      Returns:
      -

      A combined RangedSummarizedExperiment.

      +
      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A combined RangedSummarizedExperiment.

      -summarizedexperiment.RangedSummarizedExperiment.combine_rows(*x: RangedSummarizedExperiment) → RangedSummarizedExperiment[source]
      +summarizedexperiment.RangedSummarizedExperiment.combine_rows(*x)[source]

      Combine multiple RangedSummarizedExperiment objects by row.

      All assays must contain the same assay names. If you need a flexible combine operation, checkout relaxed_combine_rows().

      -
      Returns:
      -

      A combined RangedSummarizedExperiment.

      +
      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A combined RangedSummarizedExperiment.

      -summarizedexperiment.RangedSummarizedExperiment.relaxed_combine_columns(*x: RangedSummarizedExperiment) → RangedSummarizedExperiment[source]
      +summarizedexperiment.RangedSummarizedExperiment.relaxed_combine_columns(*x)[source]

      A relaxed version of the combine_columns() method for RangedSummarizedExperiment objects. Whereas combine_columns expects that all objects have the same rows, relaxed_combine_columns allows for different rows. Absent columns in any object are filled in with appropriate placeholder values before combining.

      Parameters:
      -

      x – One or more RangedSummarizedExperiment objects, possibly with differences in the +

      x (RangedSummarizedExperiment) – One or more RangedSummarizedExperiment objects, possibly with differences in the number and identity of their rows.

      -
      Returns:
      -

      A RangedSummarizedExperiment that combines all experiments along their columns and contains +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A RangedSummarizedExperiment that combines all experiments along their columns and contains the union of all rows. Rows absent in any x are filled in with placeholders consisting of Nones or masked NumPy values.

      @@ -1333,18 +1551,21 @@

      Submodules
      -summarizedexperiment.RangedSummarizedExperiment.relaxed_combine_rows(*x: RangedSummarizedExperiment) → RangedSummarizedExperiment[source]
      -

      A relaxed version of the combine_rows() method for +summarizedexperiment.RangedSummarizedExperiment.relaxed_combine_rows(*x)[source] +

      A relaxed version of the combine_rows() method for RangedSummarizedExperiment objects. Whereas combine_rows expects that all objects have the same columns, relaxed_combine_rows allows for different columns. Absent columns in any object are filled in with appropriate placeholder values before combining.

      Parameters:
      -

      x – One or more RangedSummarizedExperiment objects, possibly with differences in the +

      x (RangedSummarizedExperiment) – One or more RangedSummarizedExperiment objects, possibly with differences in the number and identity of their columns.

      -
      Returns:
      -

      A RangedSummarizedExperiment that combines all experiments along their rows and contains +

      Return type:
      +

      RangedSummarizedExperiment

      +
      +
      Returns:
      +

      A RangedSummarizedExperiment that combines all experiments along their rows and contains the union of all columns. Columns absent in any x are filled in with placeholders consisting of Nones or masked NumPy values.

      @@ -1356,53 +1577,106 @@

      Submodules

      summarizedexperiment.SummarizedExperiment module

      -class summarizedexperiment.SummarizedExperiment.SummarizedExperiment(assays: Dict[str, Any] | None = None, row_data: BiocFrame | None = None, column_data: BiocFrame | None = None, row_names: List[str] | None = None, column_names: List[str] | None = None, metadata: dict | None = None, validate: bool = True)[source]
      +class summarizedexperiment.SummarizedExperiment.SummarizedExperiment(assays=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]

      Bases: BaseSE

      Container to represents genomic experiment data (assays), features (row_data), sample data (column_data) and any other metadata.

      SummarizedExperiment follows the R/Bioconductor specification; rows are features, columns are samples.

      +
      +
      +__init__(assays=None, row_data=None, column_data=None, row_names=None, column_names=None, metadata=None, validate=True)[source]
      +

      Initialize a Summarized Experiment (SE).

      +
      +
      Parameters:
      +
        +

      • assays (Optional[Dict[str, Any]]) –

        A dictionary containing matrices, with assay names as keys +and 2-dimensional matrices represented as either +ndarray or spmatrix.

        +

        Alternatively, you may use any 2-dimensional matrix that has +the shape property and implements the slice operation +using the __getitem__ dunder method.

        +

        All matrices in assays must be 2-dimensional and have the +same shape (number of rows, number of columns).

        +

        • +

      • row_data (Optional[BiocFrame]) –

        Features, must be the same length as the number of rows of +the matrices in assays.

        +

        Feature information is coerced to a +BiocFrame. Defaults to None.

        +

        • +

      • column_data (Optional[BiocFrame]) –

        Sample data, must be the same length as the number of +columns of the matrices in assays.

        +

        Sample information is coerced to a +BiocFrame. Defaults to None.

        +

        • +

      • row_names (Optional[List[str]]) –

        A list of strings, same as the number of rows.

        +

        If row_names are not provided, these are inferred from +row_data.

        +

        Defaults to None.

        +

        • +

      • column_names (Optional[List[str]]) –

        A list of string, same as the number of columns.

        +

        if column_names are not provided, these are inferred from +column_data.

        +

        Defaults to None.

        +

        • +

      • metadata (Optional[dict]) – Additional experimental metadata describing the methods. +Defaults to None.

        • +

      • validate (bool) – Internal use only.

        • +
      +
      +
      +
      +
      -summarizedexperiment.SummarizedExperiment.combine_columns(*x: SummarizedExperiment) → SummarizedExperiment[source]
      +summarizedexperiment.SummarizedExperiment.combine_columns(*x)[source]

      Combine multiple SummarizedExperiment objects by column.

      All assays must contain the same assay names. If you need a flexible combine operation, checkout relaxed_combine_columns().

      -
      Returns:
      -

      A combined SummarizedExperiment.

      +
      Return type:
      +

      SummarizedExperiment

      +
      +
      Returns:
      +

      A combined SummarizedExperiment.

      -summarizedexperiment.SummarizedExperiment.combine_rows(*x: SummarizedExperiment) → SummarizedExperiment[source]
      +summarizedexperiment.SummarizedExperiment.combine_rows(*x)[source]

      Combine multiple SummarizedExperiment objects by row.

      All assays must contain the same assay names. If you need a flexible combine operation, checkout relaxed_combine_rows().

      -
      Returns:
      -

      A combined SummarizedExperiment.

      +
      Return type:
      +

      SummarizedExperiment

      +
      +
      Returns:
      +

      A combined SummarizedExperiment.

      -summarizedexperiment.SummarizedExperiment.relaxed_combine_columns(*x: SummarizedExperiment) → SummarizedExperiment[source]
      +summarizedexperiment.SummarizedExperiment.relaxed_combine_columns(*x)[source]

      A relaxed version of the combine_columns() method for SummarizedExperiment objects. Whereas combine_columns expects that all objects have the same rows, relaxed_combine_columns allows for different rows. Absent columns in any object are filled in with appropriate placeholder values before combining.

      Parameters:
      -

      x – One or more SummarizedExperiment objects, possibly with differences in the +

      x (SummarizedExperiment) – One or more SummarizedExperiment objects, possibly with differences in the number and identity of their rows.

      -
      Returns:
      -

      A SummarizedExperiment that combines all experiments along their columns and contains +

      Return type:
      +

      SummarizedExperiment

      +
      +
      Returns:
      +

      A SummarizedExperiment that combines all experiments along their columns and contains the union of all rows. Rows absent in any x are filled in with placeholders consisting of Nones or masked NumPy values.

      @@ -1411,18 +1685,21 @@

      Submodules
      -summarizedexperiment.SummarizedExperiment.relaxed_combine_rows(*x: SummarizedExperiment) → SummarizedExperiment[source]
      -

      A relaxed version of the combine_rows() method for +summarizedexperiment.SummarizedExperiment.relaxed_combine_rows(*x)[source] +

      A relaxed version of the combine_rows() method for SummarizedExperiment objects. Whereas combine_rows expects that all objects have the same columns, relaxed_combine_rows allows for different columns. Absent columns in any object are filled in with appropriate placeholder values before combining.

      Parameters:
      -

      x – One or more SummarizedExperiment objects, possibly with differences in the +

      x (SummarizedExperiment) – One or more SummarizedExperiment objects, possibly with differences in the number and identity of their columns.

      -
      Returns:
      -

      A SummarizedExperiment that combines all experiments along their rows and contains +

      Return type:
      +

      SummarizedExperiment

      +
      +
      Returns:
      +

      A SummarizedExperiment that combines all experiments along their rows and contains the union of all columns. Columns absent in any x are filled in with placeholders consisting of Nones or masked NumPy values.

      @@ -1434,16 +1711,19 @@

      Submodules

      summarizedexperiment.type_checks module

      -summarizedexperiment.type_checks.is_matrix_like(x: Any) → bool[source]
      +summarizedexperiment.type_checks.is_matrix_like(x)[source]

      Check if x is a matrix-like object.

      Matrix must support the matrix protocol, has the shape property and allows slicing by implementing the __getitem__ dunder method.

      Parameters:
      -

      x – Any object.

      +

      x (Any) – Any object.

      -
      Returns:
      -

      True if ``x``is matrix-like.

      +
      Return type:
      +

      bool

      +
      +
      Returns:
      +

      True if ``x``is matrix-like.

      @@ -1516,6 +1796,7 @@

      SubmodulesBaseSE.__copy__()

    • BaseSE.__deepcopy__()
    • BaseSE.__getitem__()
      • +
    • BaseSE.__init__()
    • BaseSE.__len__()
    • BaseSE.__repr__()
    • BaseSE.assay()
      • @@ -1563,6 +1844,7 @@

      SubmodulesRangedSummarizedExperiment

    diff --git a/authors.html b/authors.html index 9080d54..de8c36c 100644 --- a/authors.html +++ b/authors.html @@ -6,9 +6,10 @@ - Contributors - SummarizedExperiment 0.4.4 documentation + Contributors - SummarizedExperiment 0.4.5 documentation + @@ -179,7 +180,7 @@
    -
    SummarizedExperiment 0.4.4 documentation
     +
    SummarizedExperiment 0.4.5 documentation
    @@ -203,7 +204,7 @@
    - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
    diff --git a/changelog.html b/changelog.html index d37d524..649f88e 100644 --- a/changelog.html +++ b/changelog.html @@ -6,9 +6,10 @@ - Changelog - SummarizedExperiment 0.4.4 documentation + Changelog - SummarizedExperiment 0.4.5 documentation + @@ -179,7 +180,7 @@
    -
    SummarizedExperiment 0.4.4 documentation
     +
    SummarizedExperiment 0.4.5 documentation
    @@ -203,7 +204,7 @@
    - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
    diff --git a/contributing.html b/contributing.html index b12807b..a9a0795 100644 --- a/contributing.html +++ b/contributing.html @@ -6,9 +6,10 @@ - Contributing - SummarizedExperiment 0.4.4 documentation + Contributing - SummarizedExperiment 0.4.5 documentation + @@ -179,7 +180,7 @@
    -
    SummarizedExperiment 0.4.4 documentation
     +
    SummarizedExperiment 0.4.5 documentation
    @@ -203,7 +204,7 @@
    - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
    -
    +
    diff --git a/extend_se.html b/extend_se.html new file mode 100644 index 0000000..d218cca --- /dev/null +++ b/extend_se.html @@ -0,0 +1,701 @@ + + + + + + + + + Extending summarized experiments - SummarizedExperiment 0.4.5 documentation + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + +
    +
    +
    SummarizedExperiment 0.4.5 documentation
     +
    +
    +
    + +
    + +
    +
    + +
    +
    +
    + + + + + Back to top + +
    +
    + + + + +
    +
    + +
    + +
    +
    +
    +

    Extending summarized experiments

    +

    SummarizedExperiment and RangedSummarizedExperiment serve as the foundational container classes for representing experimental data and its metadata. Developers can extend these classes to create specialized data structures tailored for specific scientific applications. In fact, our implementation of SingleCellExperiment extends the RangedSummarizedExperiment class.

    +
    +

    Define the new class

    +

    As a simple example, let’s create a new class called BioSampleSE that stores biosample information on which the experiment was conducted. This may contain anonymized information about the patient(s) or sample(s). First, we extend the SummarizedExperiment class:

    +
    from summarizedexperiment import SummarizedExperiment
+
+class BioSampleSE(SummarizedExperiment):
+    pass
+
    +
    +
    +
    +

    Add a new slot or attribute

    +

    To add a new slot to this class, we accept a new parameter bio_sample_information when the class is initialized through the init method. We also provide type hints to set expectations on the accepted types for these arguments. Type hints are helpful for users and are automatically annotated in the documentation. More information on type hints can be found in our developer guide.

    +

    We forward the default parameters to the base SummarizedExperiment class (using the super method) and store the new attribute.

    +
    from summarizedexperiment import SummarizedExperiment
+import biocframe
+from typing import Dict, Any, List, Optional
+
+class BioSampleSE(SummarizedExperiment):
+
+    def __init__(
+        self,
+        assays: Dict[str, Any] = None,
+        row_data: Optional[biocframe.BiocFrame] = None,
+        column_data: Optional[biocframe.BiocFrame] = None,
+        row_names: Optional[List[str]] = None,
+        column_names: Optional[List[str]] = None,
+        metadata: Optional[dict] = None,
+        bio_sample_information: Optional[biocframe.BiocFrame] = None, # NEW SLOT
+        validate: bool = True,
+    ) -> None:
+        super().__init__(
+            assays,
+            row_data=row_data,
+            column_data=column_data,
+            row_names=row_names,
+            column_names=column_names,
+            metadata=metadata,
+            validate=validate,
+        )
+
+        self._bio_sample_information = bio_sample_information
+
    +
    +

    The new slot can be validated using a dedicated validator:

    +
    def _validate_bio_sample_information(bio_sample_info):
+    if not isinstance(bio_sample_info, biocframe.BiocFrame):
+        raise Exception("Biosample information must be a BiocFrame object.")
+
+    # any other validations. for example, if you have expectations on the columns
+    # of this frame or the number of rows.
+
    +
    +

    Our class now validates the new slot:

    +
    class BioSampleSE(SummarizedExperiment):
+
+    def __init__(
+        self,
+        assays: Dict[str, Any] = None,
+        row_data: Optional[biocframe.BiocFrame] = None,
+        column_data: Optional[biocframe.BiocFrame] = None,
+        row_names: Optional[List[str]] = None,
+        column_names: Optional[List[str]] = None,
+        metadata: Optional[dict] = None,
+        bio_sample_information: Optional[biocframe.BiocFrame] = None, # NEW SLOT
+        validate: bool = True,
+    ) -> None:
+        super().__init__(
+            assays,
+            row_data=row_data,
+            column_data=column_data,
+            row_names=row_names,
+            column_names=column_names,
+            metadata=metadata,
+            validate=validate,
+        )
+
+        self._bio_sample_information = bio_sample_information
+
+        if validate:
+            _validate_bio_sample_information(self._bio_sample_information)
+
    +
    +
    +
    +

    Define getters/setters

    +

    We need accessors and setters so users can interact with this new property. We provide these accessors in both functional and property-based approaches. More details can be found in our class design document.

    +
    def get_bio_sample_information(self) -> Optional[biocframe.BiocFrame]:
+    """Get biosample information.
+
+    Returns:
+        biosample information or None if not availabl.
+    """
+    return self._bio_sample_information
+
+def set_bio_sample_information(
+    self, bio_sample_information: Optional[biocframe.BiocFrame], in_place: bool = False
+) -> "BioSampleSE":
+    """Set new biosample information.
+
+    Args:
+        bio_sample_information:
+            A new `BiocFrame` object containing biosample information.
+
+        in_place:
+            Whether to modify the ``BioSampleSE`` in place.
+
+    Returns:
+        A modified ``BioSampleSE`` object, either as a copy of the original
+        or as a reference to the (in-place-modified) original.
+    """
+    _validate_bio_sample_information(bio_sample_info)
+
+    output = self._define_output(in_place) # MAKES A SHALLOW COPY
+    output._bio_sample_information = bio_sample_info
+    return output
+
    +
    +

    Additionally, let’s provide property-based accessors to the new attribute:

    +
    @property
+def bio_sample_information(self) -> biocframe.BiocFrame:
+    """Alias for :py:meth:`~get_bio_sample_info`."""
+    return self.get_bio_sample_info()
+
+@bio_sample_information.setter
+def bio_sample_information(self, bio_sample_info: biocframe.BiocFrame) -> None:
+    """Alias for :py:meth:`~set_bio_sample_info`."""
+    warn(
+        "Setting property 'bio_sample_information' is an in-place operation, use 'set_bio_sample_info' instead",
+        UserWarning,
+    )
+    return self.set_bio_sample_information(row_ranges=row_ranges, in_place=True)
+
+
    +
    +

    This allows users to easily access the new property using the dot notation on an instance, for example, obj.bio_sample_info provides access to the attribute.

    +
    +
    +

    Define shallow and deep copy methods

    +

    To avoid mutating objects in-place, methods for making shallow and deep copies of the class attributes are implemented.

    +
    def __deepcopy__(self, memo=None, _nil=[]):
+    """
+    Returns:
+        A deep copy of the current ``BioSampleSE``.
+    """
+    from copy import deepcopy
+
+    _assays_copy = deepcopy(self._assays)
+    _rows_copy = deepcopy(self._rows)
+    _cols_copy = deepcopy(self._cols)
+    _row_names_copy = deepcopy(self._row_names)
+    _col_names_copy = deepcopy(self._column_names)
+    _bio_sample_information_copy = deepcopy(self._bio_sample_information)
+    _metadata_copy = deepcopy(self.metadata)
+
+    current_class_const = type(self)
+    return current_class_const(
+        assays=_assays_copy,
+        row_data=_rows_copy,
+        column_data=_cols_copy,
+        row_names=_row_names_copy,
+        column_names=_col_names_copy,
+        bio_sample_information=_bio_sample_information_copy,
+        metadata=_metadata_copy,
+    )
+
+def __copy__(self):
+    """
+    Returns:
+        A shallow copy of the current ``BioSampleSE``.
+    """
+    current_class_const = type(self)
+    return current_class_const(
+        assays=self._assays,
+        row_data=self._rows,
+        column_data=self._cols,
+        row_names=self._row_names,
+        column_names=self._column_names,
+        bio_sample_information=self._bio_sample_information,
+        metadata=self._metadata,
+    )
+
+def copy(self):
+    """Alias for :py:meth:`~__copy__`."""
+    return self.__copy__()
+
    +
    +
    +
    +

    Subset operation

    +

    When the experiment is subsetted using the [] operator, we may also have to subset the new slots added to the extended class. Developers only need to extend the get_slice function from the base class that accepts row and column subsets. The _generic_slice function returns a slicer object containing normalized row and column indices, and properties from the base class.

    +
    from typing import Sequence
+
+def get_slice(
+    self,
+    rows: Optional[Union[str, int, bool, Sequence]],
+    columns: Optional[Union[str, int, bool, Sequence]],
+) -> "BioSampleSE":
+    """Alias for :py:attr:`~__getitem__`, for back-compatibility."""
+
+    slicer = self._generic_slice(rows=rows, columns=columns)
+
+    # An illustrative example
+    new_bio_sample_info = None
+    if slicer.row_indices != slice(None):
+        new_bio_sample_info = self.bio_sample_info[slicer.row_indices]
+
+    current_class_const = type(self)
+    return current_class_const(
+        assays=slicer.assays,
+        row_data=slicer.rows,
+        column_data=slicer.columns,
+        row_names=slicer.row_names,
+        column_names=slicer.column_names,
+        bio_sample_info=self.bio_sample_info.
+        metadata=self._metadata,
+    )
+
    +
    +
    +
    +

    Putting it all together

    +
    
+def _validate_bio_sample_information(bio_sample_info):
+    if not isinstance(bio_sample_info, biocframe.BiocFrame):
+        raise Exception("Biosample information must be a BiocFrame object.")
+
+class BioSampleSE(SummarizedExperiment):
+
+    def __init__(
+        self,
+        assays: Dict[str, Any] = None,
+        row_data: Optional[biocframe.BiocFrame] = None,
+        column_data: Optional[biocframe.BiocFrame] = None,
+        row_names: Optional[List[str]] = None,
+        column_names: Optional[List[str]] = None,
+        metadata: Optional[dict] = None,
+        bio_sample_information: Optional[biocframe.BiocFrame] = None, # NEW SLOT
+        validate: bool = True,
+    ) -> None:
+        super().__init__(
+            assays,
+            row_data=row_data,
+            column_data=column_data,
+            row_names=row_names,
+            column_names=column_names,
+            metadata=metadata,
+            validate=validate,
+        )
+
+        self._bio_sample_information = bio_sample_information
+
+        if validate:
+            _validate_bio_sample_information(self._bio_sample_information)
+
+    def __deepcopy__(self, memo=None, _nil=[]):
+        """
+        Returns:
+            A deep copy of the current ``BioSampleSE``.
+        """
+        from copy import deepcopy
+
+        _assays_copy = deepcopy(self._assays)
+        _rows_copy = deepcopy(self._rows)
+        _cols_copy = deepcopy(self._cols)
+        _row_names_copy = deepcopy(self._row_names)
+        _col_names_copy = deepcopy(self._column_names)
+        _bio_sample_information_copy = deepcopy(self._bio_sample_information)
+        _metadata_copy = deepcopy(self.metadata)
+
+        current_class_const = type(self)
+        return current_class_const(
+            assays=_assays_copy,
+            row_data=_rows_copy,
+            column_data=_cols_copy,
+            row_names=_row_names_copy,
+            column_names=_col_names_copy,
+            bio_sample_information=_bio_sample_information_copy,
+            metadata=_metadata_copy,
+        )
+
+    def __copy__(self):
+        """
+        Returns:
+            A shallow copy of the current ``BioSampleSE``.
+        """
+        current_class_const = type(self)
+        return current_class_const(
+            assays=self._assays,
+            row_data=self._rows,
+            column_data=self._cols,
+            row_names=self._row_names,
+            column_names=self._column_names,
+            bio_sample_information=self._bio_sample_information,
+            metadata=self._metadata,
+        )
+
+    def copy(self):
+        """Alias for :py:meth:`~__copy__`."""
+        return self.__copy__()
+
+
+    def get_bio_sample_information(self) -> Optional[biocframe.BiocFrame]:
+        """Get biosample information.
+
+        Returns:
+            biosample information or None if not availabl.
+        """
+        return self._bio_sample_information
+
+    def set_bio_sample_information(
+        self, bio_sample_information: Optional[biocframe.BiocFrame], in_place: bool = False
+    ) -> "BioSampleSE":
+        """Set new biosample information.
+
+        Args:
+            bio_sample_information:
+                A new `BiocFrame` object containing biosample information.
+
+            in_place:
+                Whether to modify the ``BioSampleSE`` in place.
+
+        Returns:
+            A modified ``BioSampleSE`` object, either as a copy of the original
+            or as a reference to the (in-place-modified) original.
+        """
+        _validate_bio_sample_information(bio_sample_info)
+
+        output = self._define_output(in_place) # MAKES A SHALLOW COPY
+        output._bio_sample_information = bio_sample_info
+        return output
+
+
+    @property
+    def bio_sample_information(self) -> biocframe.BiocFrame:
+        """Alias for :py:meth:`~get_bio_sample_info`."""
+        return self.get_bio_sample_info()
+
+    @bio_sample_information.setter
+    def bio_sample_information(self, bio_sample_info: biocframe.BiocFrame) -> None:
+        """Alias for :py:meth:`~set_bio_sample_info`."""
+        warn(
+            "Setting property 'bio_sample_information' is an in-place operation, use 'set_bio_sample_info' instead",
+            UserWarning,
+        )
+        return self.set_bio_sample_information(row_ranges=row_ranges, in_place=True)
+
+
+
    +
    +

    That’s the minimum required to extend a SummarizedExperiment and adapt it to new use cases. Please follow the developer guide, which provides information on class design, package setup, and documentation to ensure consistency in how BiocPy-related packages are developed.

    +
    +
    + +
    +
    + +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/genindex.html b/genindex.html index 13b1f4b..4ab04a7 100644 --- a/genindex.html +++ b/genindex.html @@ -4,9 +4,10 @@ - Index - SummarizedExperiment 0.4.4 documentation + Index - SummarizedExperiment 0.4.5 documentation + @@ -177,7 +178,7 @@
    -
    SummarizedExperiment 0.4.4 documentation
     +
    SummarizedExperiment 0.4.5 documentation
    @@ -201,7 +202,7 @@
    - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
    • +
  • __getitem__() (summarizedexperiment.BaseSE.BaseSE method) +
-
+
diff --git a/index.html b/index.html index 9b90dd7..66473d5 100644 --- a/index.html +++ b/index.html @@ -3,12 +3,13 @@ - + - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation + @@ -179,7 +180,7 @@
-
SummarizedExperiment 0.4.4 documentation
 +
SummarizedExperiment 0.4.5 documentation
@@ -203,7 +204,7 @@
- SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
-
SummarizedExperiment 0.4.4 documentation
 +
SummarizedExperiment 0.4.5 documentation
@@ -203,7 +204,7 @@
- SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
-
+
diff --git a/objects.inv b/objects.inv index 1628842..9c16fc3 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/py-modindex.html b/py-modindex.html index d9ed084..f719288 100644 --- a/py-modindex.html +++ b/py-modindex.html @@ -4,9 +4,10 @@ - Python Module Index - SummarizedExperiment 0.4.4 documentation + Python Module Index - SummarizedExperiment 0.4.5 documentation + @@ -177,7 +178,7 @@
-
SummarizedExperiment 0.4.4 documentation
 +
SummarizedExperiment 0.4.5 documentation
@@ -201,7 +202,7 @@
- SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
-
+
diff --git a/readme.html b/readme.html index 26970a2..23c8a0f 100644 --- a/readme.html +++ b/readme.html @@ -3,12 +3,13 @@ - + - SummarizedExperiment - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment - SummarizedExperiment 0.4.5 documentation + @@ -179,7 +180,7 @@
-
SummarizedExperiment 0.4.4 documentation
 +
SummarizedExperiment 0.4.5 documentation
@@ -203,7 +204,7 @@
- SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
-
    -
  • Overview
    • -
  • Tutorial
    • +
      +
    • Overview
      • +
    • Extending Classes
    • Module Reference @@ -267,18 +268,24 @@ Unit tests

      SummarizedExperiment

      -

      Container to represent genomic experiments, follows Bioconductor’s SummarizedExperiment.

      +

      This package provides containers to represent genomic experimental data as 2-dimensional matrices, follows Bioconductor’s SummarizedExperiment. In these matrices, the rows typically denote features or genomic regions of interest, while columns represent samples or cells.

      +

      The package currently includes representations for both SummarizedExperiment and RangedSummarizedExperiment. A distinction lies in the fact RangedSummarizedExperiment object provides an additional slot to store genomic regions for each feature and is expected to be GenomicRanges (more here).

      Install

      -

      Package is published to PyPI,

      +

      To get started, Install the package from PyPI,

      pip install summarizedexperiment

      Usage

      -

      Currently supports SummarizedExperiment & RangedSummarizedExperiment classes

      -

      First create necessary sample data

      +

      A SummarizedExperiment contains three key attributes,

      +
        +

      • assays: A dictionary of matrices with assay names as keys, e.g. counts, logcounts etc.

        • +

      • row_data: Feature information e.g. genes, transcripts, exons, etc.

        • +

      • column_data: Sample information about the columns of the matrices.

        • +
      +

      First lets mock feature and sample data:

      from random import random
 import pandas as pd
 import numpy as np
@@ -373,26 +380,8 @@ 
      Note
         
         
      
-          
-              
      
-                
      
-                  Next
-                
      
-                
      Tutorial
      
-              
      
-              
-                  
-          
-              
-              
      
-                
      
-                  Previous
-                
      
-                
-                
      Home
      
-                
-              
      
-                  
+          
+          
         
      
         
      
           
      
@@ -438,7 +427,7 @@ 
      Note
   
      
-
      
+
      diff --git a/search.html b/search.html index 0280b2a..8262848 100644 --- a/search.html +++ b/search.html @@ -7,8 +7,9 @@ -Search - SummarizedExperiment 0.4.4 documentation +Search - SummarizedExperiment 0.4.5 documentation + @@ -179,7 +180,7 @@
      -
      SummarizedExperiment 0.4.4 documentation
       +
      SummarizedExperiment 0.4.5 documentation
      @@ -203,7 +204,7 @@
      - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
      -
      +
      diff --git a/searchindex.js b/searchindex.js index 70570dc..08093d0 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"Accessors": [[8, "accessors"]], "Changelog": [[3, "changelog"]], "Clone the repository": [[4, "clone-the-repository"]], "Code Contributions": [[4, "code-contributions"]], "Combine operations": [[8, "combine-operations"]], "Construct a SummarizedExperiment object": [[8, "construct-a-summarizedexperiment-object"]], "Contents": [[5, "contents"]], "Contributing": [[4, "contributing"]], "Contributors": [[2, "contributors"]], "Create an environment": [[4, "create-an-environment"]], "Documentation Improvements": [[4, "documentation-improvements"]], "Implement your changes": [[4, "implement-your-changes"]], "Indices and tables": [[5, "indices-and-tables"]], "Install": [[5, "install"], [7, "install"]], "Issue Reports": [[4, "issue-reports"]], "License": [[6, "license"]], "Maintainer tasks": [[4, "maintainer-tasks"]], "Module 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      -
      SummarizedExperiment 0.4.4 documentation
       +
      SummarizedExperiment 0.4.5 documentation
      @@ -203,7 +204,7 @@
      - SummarizedExperiment 0.4.4 documentation + SummarizedExperiment 0.4.5 documentation
      -
      -

      Tutorial

      +
      +

      Represent genomic experimental data

      This package provides classes to represent genomic experiments, commonly generated by sequencing experiments. It provides similar interfaces and functionality as the Bioconductor equivalent.

      A SummarizedExperiment contains three slots,

        +

      • assays: A dictionary of matrices with assay names as keys, e.g. counts, logcounts etc.

      • row_data: Feature information e.g. genes, transcripts, exons, etc.

      • column_data: Sample information about the columns of the matrices.

        • -

      • assays: A dictionary of matrices with assay names as keys, e.g. counts, logcounts etc.

      -

      The package currently provides both SummarizedExperiment & RangedSummarizedExperiment representations. A key difference between these two is the rows of a RangedSummarizedExperiment object is a GenomicRanges, representing genomic regions of interest.

      +

      The package currently provides both SummarizedExperiment & RangedSummarizedExperiment representations. A key difference between these two is the RangedSummarizedExperiment object provides an additional slot to store genomic regions for each feature and is expected to be GenomicRanges (more here).

      Construct a SummarizedExperiment object

      -

      First, lets create random experimental “count” data along with feature and sample information.

      -
      from random import random
+
      To construct a SummarizedExperiment, we’ll first generate a matrix of read counts, representing the read counts from a series of RNA-seq experiments. Following that, we’ll create a BiocFrame object to denote feature information and a table for column annotations. This table may include the names for the columns and any other values we wish to represent.
      
+
      
+
      
+
      from random import random
 import pandas as pd
 import numpy as np
 from biocframe import BiocFrame
@@ -313,121 +316,435 @@ 
      Construct a Sum
 )
 
      
 
      
+
      
+
      
+
      Note: The inputs row_data and column_data are expected to be BiocFrame objects and will be coerced to a BiocFrame if a pandas DataFrame is supplied.
      
 
      
 
      SummarizedExperiment
      
 
      A SummarizedExperiment is a base class to represent any genomic experiment. This class expects features (row_data) to be either a pandas DataFrame or any variant of BiocFrame.
      
-
      se = SummarizedExperiment(
+
      
+
      
+
      from summarizedexperiment import SummarizedExperiment
+
+se = SummarizedExperiment(
     assays={"counts": counts}, row_data=row_data, column_data=col_data
 )
+
+print(se)
 
      
 
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (200, 6)
+assays(1): ['counts']
+row_data columns(6): ['seqnames', 'starts', 'ends', 'strand', 'score', 'GC']
+row_names(0):  
+column_data columns(1): ['treatment']
+column_names(6): ['0', '1', '2', '3', '4', '5']
+metadata(0): 
+
      
+
      
+
      
+
      
 
      
 
      
 
      RangedSummarizedExperiment
      
-
      RangedSummarizedExperiment requires features to be a GenomicRanges object.
      
-
      # convert our pandas dataframe to genomic ranges.
-from genomicranges import GenomicRanges
+
      Similarly, we can use the same information to construct a RangeSummarizedExperiment. We convert feature information into a GenomicRanges object and provide this as row_ranges:
      
+
      
+
      
+
      from genomicranges import GenomicRanges
+from summarizedexperiment import RangedSummarizedExperiment
+
 gr = GenomicRanges.from_pandas(row_data.to_pandas())
 
-trse = RangedSummarizedExperiment(
+rse = RangedSummarizedExperiment(
     assays={"counts": counts}, row_data=row_data, row_ranges=gr, column_data=col_data
 )
+print(rse)
+
      
+
      
+
      
+
      
+
      class: RangedSummarizedExperiment
+dimensions: (200, 6)
+assays(1): ['counts']
+row_data columns(6): ['seqnames', 'starts', 'ends', 'strand', 'score', 'GC']
+row_names(0):  
+column_data columns(1): ['treatment']
+column_names(6): ['0', '1', '2', '3', '4', '5']
+metadata(0): 
+
      
+
      
+
      /home/runner/work/SummarizedExperiment/SummarizedExperiment/.tox/docs/lib/python3.9/site-packages/genomicranges/SeqInfo.py:348: UserWarning: 'seqnames' is deprecated, use 'get_seqnames' instead
+  warn("'seqnames' is deprecated, use 'get_seqnames' instead", UserWarning)
 
      
 
      
+
      
+
      -
      -

      Accessors

      -

      Properties can be accessed directly from the class instance.

      -
      tse.assays
-tse.row_data or # tse.row_ranges
-tse.column_data
-tse.metadata
+
      
+
      Delayed or file-backed arrays
      
+
      The general idea is that DelayedArray’s are a drop-in replacement for NumPy arrays, at least for BiocPy applications.
      
+
      For example, we can use the DelayedArray inside a SummarizedExperiment:
      
+
      
+
      
+
      import numpy
+import delayedarray
+
+# create a delayed array, can also be file-backed
+x = numpy.random.rand(100, 20)
+d = delayedarray.wrap(x)
+
+# operate over delayed arrays
+filtered = d[1:100:2,1:8]
+total = filtered.sum(axis=0)
+normalized = filtered / total
+transformed = numpy.log1p(normalized)
 
-# Access the counts assay
-tse.assay("counts")
+import summarizedexperiment as SE
+se_delayed = SE.SummarizedExperiment({ "counts": filtered, "lognorm": transformed })
+print(se_delayed)
 
      
 
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (50, 7)
+assays(2): ['counts', 'lognorm']
+row_data columns(0): []
+row_names(0):  
+column_data columns(0): []
+column_names(0):  
+metadata(0): 
+
      
+
      
+
      
+
      
 
      
-
      
-
      Subset an experiment
      
-
      You can subset a SummarizedExperiment object using the [] slice operator.
      
-
      
-
      Slice by index position
      
-
      # subset the first 10 rows and the first 3 samples
-subset_tse = tse[0:10, 0:3]
+
      
+
      Interop with anndata
      
+
      Converting a SummarizedExperiment to an AnnData representation is straightforward:
      
+
      
+
      
+
      adata = se.to_anndata()
+print(adata)
 
      
 
      
+
      
+
      
+
      AnnData object with n_obs × n_vars = 6 × 200
+    obs: 'treatment', 'rownames'
+    var: 'seqnames', 'starts', 'ends', 'strand', 'score', 'GC'
+    layers: 'counts'
+
      
+
      
+
      /home/runner/work/SummarizedExperiment/SummarizedExperiment/.tox/docs/lib/python3.9/site-packages/anndata/_core/aligned_df.py:67: ImplicitModificationWarning: Transforming to str index.
+  warnings.warn("Transforming to str index.", ImplicitModificationWarning)
+
      
+
      
+
      
+
      
+
      To convert an AnnData object to a BiocPy representation, utilize the from_anndata method in the SingleCellExperiment class. This minimizes the loss of information when converting between these two representations.
      
 
      
-
      
-
      Slice by row names or column names
      
-
      Alternatively, we can use a sequence of row (feature) or column (sample) names to subset a SummarizedExperiment . To demonstrate this, we create a SummarizedExperiment object with index names.
      
-
      row_data = BiocFrame(
-    {
-        "seqnames": ["chr_5", "chr_3", "chr_2"],
-        "start": [100, 200, 300],
-        "end": [110, 210, 310]
-    }
-)
-col_data = BiocFrame(
-    {
-        "sample": ["SAM_1", "SAM_3", "SAM_3"],
-        "disease": ["True", "True", "True"],
+
      
+
      Getters/Setters
      
+
      Getters are available to access various attributes using either the property notation or functional style.
      
+
      
+
      
+
      # access assay names
+print("assay names (as property): ", se.assay_names)
+print("assay names (functional style): ", se.get_assay_names())
+
+# access row data
+print(se.row_data)
+
      
+
      
+
      
+
      
+
      assay names (as property):  ['counts']
+assay names (functional style):  ['counts']
+BiocFrame with 200 rows and 6 columns
+      seqnames  starts    ends strand   score                   GC
+        <list> <range> <range> <list> <range>               <list>
+  [0]     chr1     100     110      -       0  0.33661938499226773
+  [1]     chr2     101     111      +       1  0.08117605631823166
+  [2]     chr2     102     112      +       2   0.7803296397482788
+           ...     ...     ...    ...     ...                  ...
+[197]     chr3     297     307      +     197  0.41299197646931673
+[198]     chr3     298     308      -     198   0.2278273261122027
+[199]     chr3     299     309      -     199 0.028240182603081476
+
      
+
      
+
      
+
      
+
      
+
      Access an assay
      
+
      One can access an assay by index or name:
      
+
      
+
      
+
      se.assay(0) # same as se.assay("counts")
+
      
+
      
+
      
+
      
+
      array([[0.64360331, 0.59445294, 0.136433  , 0.22204076, 0.76055558,
+        0.8070267 ],
+       [0.54835031, 0.78297301, 0.96854724, 0.04594184, 0.37821714,
+        0.51487815],
+       [0.52805442, 0.20320068, 0.48829611, 0.53442027, 0.05983007,
+        0.22587434],
+       ...,
+       [0.07033562, 0.34382563, 0.55570682, 0.39292596, 0.20664601,
+        0.70917104],
+       [0.15801566, 0.91225858, 0.43685777, 0.74345587, 0.27199831,
+        0.95449414],
+       [0.74080643, 0.17205765, 0.0728668 , 0.2019268 , 0.34420141,
+        0.16213582]])
+
      
+
      
+
      
+
      
+
      
+
      
+
      Setters
      
+
      All property-based setters are in_place operations, with further details discussed in functional paradigm section.
      
+
      
+
      
+
      modified_column_data = se.column_data.set_column("score", range(10,16))
+modified_se = se.set_column_data(modified_column_data)
+print(modified_se)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (200, 6)
+assays(1): ['counts']
+row_data columns(6): ['seqnames', 'starts', 'ends', 'strand', 'score', 'GC']
+row_names(0):  
+column_data columns(2): ['treatment', 'score']
+column_names(6): ['0', '1', '2', '3', '4', '5']
+metadata(0): 
+
      
+
      
+
      
+
      
+
      Now, lets check the column_data on the original object.
      
+
      
+
      
+
      print(se.column_data)
+
      
+
      
+
      
+
      
+
      BiocFrame with 6 rows and 1 column
+  treatment
+     <list>
+0      ChIP
+1     Input
+2      ChIP
+3     Input
+4      ChIP
+5     Input
+
      
+
      
+
      
+
      
+
      
+
      
+
      
+
      Subset experiments
      
+
      You can subset experimental data by using the subset ([]) operator. This operation accepts different slice input types, such as a boolean vector, a slice object, a list of indices, or names (if available) to subset.
      
+
      In our previous example, we didn’t include row or column names. Let’s create another SummarizedExperiment object that includes names.
      
+
      
+
      
+
      row_data = BiocFrame({
+    "seqnames": ["chr_5", "chr_3", "chr_2"],
+    "start": [100, 200, 300],
+    "end": [110, 210, 310],
+})
+
+col_data = BiocFrame({
+    "sample": ["SAM_1", "SAM_3", "SAM_3"],
+    "disease": ["True", "True", "True"],
     },
-    index=["cell_1", "cell_2", "cell_3"],
+    row_names=["cell_1", "cell_2", "cell_3"],
 )
-se_with_index_names = SummarizedExperiment(
+
+se_with_names = SummarizedExperiment(
     assays={
         "counts": np.random.poisson(lam=5, size=(3, 3)),
-        "lognorm": np.random.lognormal(size=(3, 3))
+        "lognorm": np.random.lognormal(size=(3, 3)),
     },
     row_data=row_data,
     column_data=col_data,
     row_names=["HER2", "BRCA1", "TPFK"],
-    column_names=["cell_1", "cell_2", "cell_3"]
+    column_names=["cell_1", "cell_2", "cell_3"],
 )
 
-# subset by name
-subset = se_with_index_names[
-    ["HER2", "BRCA1"], ["cell_1", "cell_3"]
-]
+print(se_with_names)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (3, 3)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(3): ['HER2', 'BRCA1', 'TPFK']
+column_data columns(2): ['sample', 'disease']
+column_names(3): ['cell_1', 'cell_2', 'cell_3']
+metadata(0): 
 
      
 
      
-
      In the case a name does not exist, an error will be thrown:
      
-
      # throws error because "RAND" does not exist
-subset_se_with_index_names = se_with_index_names[
-    ["HER2", "BRCA1", "RAND"], ["cell_1", "cell_3"]
-]
+
      
+
      
+
      
+
      Subset by index position
      
+
      A straightforward slice operation:
      
+
      
+
      
+
      subset_se = se_with_names[0:10, 0:3]
+print(subset_se)
 
      
 
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (3, 3)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(3): ['HER2', 'BRCA1', 'TPFK']
+column_data columns(2): ['sample', 'disease']
+column_names(3): ['cell_1', 'cell_2', 'cell_3']
+metadata(0): 
+
      
+
      
+
      
+
      
 
      
-
      
-
      Slice by a boolean vector
      
-
      Similarly you could also slice by a boolean array. Note, the boolean vectors should contain the same number of features for the row slice and the same number of samples for the column slice.
      
-
      # subset with boolean array
-subset_se_with_bools = se_with_index_names[
-    [True, True, False], [True, False, True]
-]
+
      
+
      Subset by row names or column names
      
+
      Either one or both of the slices can contain names. These names are mapped to row_names and column_names of the SummarizedExperiment object.
      
+
      
+
      
+
      subset_se = se_with_names[:2, ["cell_1", "cell_3"]]
+print(subset_se)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (2, 2)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(2): ['HER2', 'BRCA1']
+column_data columns(2): ['sample', 'disease']
+column_names(2): ['cell_1', 'cell_3']
+metadata(0): 
 
      
 
      
+
      
+
      
+
      An Exception is raised if a names does not exist.
      
 
      
+
      
+
      Subset by boolean vector
      
+
      Similarly, you can also slice by a boolean array.
      
+
      Note that the boolean vectors should contain the same number of features for the row slice and the same number of samples for the column slice.
      
+
      
+
      
+
      subset_se_with_bools = se_with_names[[True, True, False], [True, False, True]]
+print(subset_se_with_bools)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (2, 2)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(2): ['HER2', 'BRCA1']
+column_data columns(2): ['sample', 'disease']
+column_names(2): ['cell_1', 'cell_3']
+metadata(0): 
+
      
+
      
+
      
+
      
+
      
+
      
+
      Subset by empty list
      
+
      This is a feature not a bug ;), you can specify an empty list to completely remove all rows or samples.
      
+
      An empty array ([]) is not the same as an empty slice (:). This helps us avoid unintented operations.
      
+
      
+
      
+
      subset = se_with_names[:2, []]
+print(subset)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (2, 0)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(2): ['HER2', 'BRCA1']
+column_data columns(2): ['sample', 'disease']
+column_names(0): []
+metadata(0): 
+
      
+
      
+
      
+
      
 
      
 
      
-
      Range based operations
      
-
      RangedSummarizedExperiment objects on the other hand supports many interval based operations similar to GenomicRanges.
      
-
      query = pd.DataFrame({"seqnames": ["chr2",], "starts": [4], "ends": [6], "strand": ["+"]})
-
-query = GenomicRanges.from_pandas(query)
+
      Range-based operations
      
+
      Additionally, since RangeSummarizedExperiment contains row_ranges, this allows us to perform a number of range-based operations that are possible on a GenomicRanges object.
      
+
      For example, to subset RangeSummarizedExperiment with a query set of regions:
      
+
      
+
      
+
      from iranges import IRanges
+query = GenomicRanges(seqnames=["chr2"], ranges=IRanges([4], [6]), strand=["+"])
 
-tse.subset_by_overlaps(query)
+result = rse.subset_by_overlaps(query)
+print(result)
+
      
+
      
+
      
+
      
+
      /home/runner/work/SummarizedExperiment/SummarizedExperiment/.tox/docs/lib/python3.9/site-packages/genomicranges/SeqInfo.py:348: UserWarning: 'seqnames' is deprecated, use 'get_seqnames' instead
+  warn("'seqnames' is deprecated, use 'get_seqnames' instead", UserWarning)
 
      
 
      
-
      Checkout the API docs or `GenomicRanges`` for list of interval based operations.
      
+
      class: RangedSummarizedExperiment
+dimensions: (0, 6)
+assays(1): ['counts']
+row_data columns(6): ['seqnames', 'starts', 'ends', 'strand', 'score', 'GC']
+row_names(0):  
+column_data columns(1): ['treatment']
+column_names(6): ['0', '1', '2', '3', '4', '5']
+metadata(0): 
+
      
+
      
+
      /home/runner/work/SummarizedExperiment/SummarizedExperiment/.tox/docs/lib/python3.9/site-packages/genomicranges/SeqInfo.py:348: UserWarning: 'seqnames' is deprecated, use 'get_seqnames' instead
+  warn("'seqnames' is deprecated, use 'get_seqnames' instead", UserWarning)
+
      
+
      
+
      
+
      
+
      Additionally, RSE supports many other interval based operations. Checkout the documentation for more details.
      
+
      
 
      
-
      
-
      Combine operations
      
-
      The combine methods in SummarizedExperiment are used to merge or combine multiple SummarizedExperiment objects, allowing users to aggregate data from different experiments or conditions. First, let’s create multiple SummarizedExperiment objects for combining later.
      
-
      rowData1 = pd.DataFrame(
+
      
+
      Combining experiments
      
+
      SummarizedExperiment implements methods for the combine generics from BiocUtils.
      
+
      These methods enable the merging or combining of multiple SummarizedExperiment objects, allowing users to aggregate data from different experiments or conditions. To demonstrate, let’s create multiple SummarizedExperiment objects.
      
+
      
+
      
+
      rowData1 = pd.DataFrame(
     {
         "seqnames": ["chr_5", "chr_3", "chr_2"],
         "start": [10293804, 12098948, 20984392],
@@ -503,15 +820,164 @@ 
      Combine operationscolumn_data=colData3,
     metadata={"seq_platform": "Illumina NovaSeq 6000"},
 )
+
+print(se1)
+print(se2)
+print(se3)
 
      
 
      
-
      from biocutils import relaxed_combine_columns, combine_columns
-se_combined = combine_columns(se2, se1)
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (3, 3)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(3): ['HER2', 'BRCA1', 'TPFK']
+column_data columns(2): ['sample', 'disease']
+column_names(3): ['cell_1', 'cell_2', 'cell_3']
+metadata(1): seq_type
+
+class: SummarizedExperiment
+dimensions: (3, 3)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(3): ['HER2', 'BRCA1', 'TPFK']
+column_data columns(2): ['sample', 'disease']
+column_names(3): ['cell_4', 'cell_5', 'cell_6']
+metadata(1): seq_platform
 
-se_relaxed_combine = relaxed_combine_columns(se1, se2, se3)
+class: SummarizedExperiment
+dimensions: (3, 3)
+assays(3): ['counts', 'lognorm', 'beta']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(3): ['MYC', 'BRCA2', 'TPFK']
+column_data columns(3): ['sample', 'disease', 'doublet_score']
+column_names(3): ['cell_7', 'cell_8', 'cell_9']
+metadata(1): seq_platform
 
      
 
      
-
      Similarly one can perform combine_rows and relaxed_combine_rows operations.
      
+
      
+
      
+
      The combine_rows or combine_columns operations, expect all experiments to contain the same assay names.
      
+
      To combine experiments by row:
      
+
      
+
      
+
      from biocutils import relaxed_combine_columns, combine_columns, combine_rows, relaxed_combine_rows
+se_combined = combine_rows(se2, se1)
+print(se_combined)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (6, 3)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(6): ['HER2', 'BRCA1', 'TPFK', 'HER2', 'BRCA1', 'TPFK']
+column_data columns(2): ['sample', 'disease']
+column_names(3): ['cell_4', 'cell_5', 'cell_6']
+metadata(1): seq_platform
+
      
+
      
+
      /home/runner/work/SummarizedExperiment/SummarizedExperiment/.tox/docs/lib/python3.9/site-packages/summarizedexperiment/BaseSE.py:96: UserWarning: 'row_data' does not contain unique 'row_names'.
+  warn("'row_data' does not contain unique 'row_names'.", UserWarning)
+
      
+
      
+
      
+
      
+
      Similarly to combine by column:
      
+
      
+
      
+
      se_combined = combine_columns(se2, se1)
+print(se_combined)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (3, 6)
+assays(2): ['counts', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(3): ['HER2', 'BRCA1', 'TPFK']
+column_data columns(2): ['sample', 'disease']
+column_names(6): ['cell_4', 'cell_5', 'cell_6', 'cell_1', 'cell_2', 'cell_3']
+metadata(1): seq_platform
+
      
+
      
+
      
+
      
+
      You can use relaxed_combine_columns or relaxed_combined_rows when there’s mismatch in the number of features or samples. Missing rows or columns in any object are filled in with appropriate placeholder values before combining, e.g. missing assay’s are replaced with a masked numpy array.
      
+
      
+
      
+
      # se3 contains an additional assay not present in se1
+se_relaxed_combine = relaxed_combine_columns(se3, se1)
+print(se_relaxed_combine)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (3, 6)
+assays(3): ['counts', 'beta', 'lognorm']
+row_data columns(3): ['seqnames', 'start', 'end']
+row_names(3): ['MYC', 'BRCA2', 'TPFK']
+column_data columns(3): ['sample', 'disease', 'doublet_score']
+column_names(6): ['cell_7', 'cell_8', 'cell_9', 'cell_1', 'cell_2', 'cell_3']
+metadata(1): seq_platform
+
      
+
      
+
      
+
      
+
      
+
      Empty experiments
      
+
      Both these classes can also contain no experimental data, and they tend to be useful when integrated into more extensive data structures but do not contain any data themselves.
      
+
      To create an empty SummarizedExperiment:
      
+
      
+
      
+
      empty_se = SummarizedExperiment()
+print(empty_se)
+
      
+
      
+
      
+
      
+
      class: SummarizedExperiment
+dimensions: (0, 0)
+assays(0): []
+row_data columns(0): []
+row_names(0):  
+column_data columns(0): []
+column_names(0):  
+metadata(0): 
+
      
+
      
+
      
+
      
+
      Similarly an empty RangeSummarizedExperiment:
      
+
      
+
      
+
      empty_rse = RangedSummarizedExperiment()
+print(empty_rse)
+
      
+
      
+
      
+
      
+
      class: RangedSummarizedExperiment
+dimensions: (0, 0)
+assays(0): []
+row_data columns(0): []
+row_names(0):  
+column_data columns(0): []
+column_names(0):  
+metadata(0): 
+
      
+
      
+
      /home/runner/work/SummarizedExperiment/SummarizedExperiment/.tox/docs/lib/python3.9/site-packages/genomicranges/SeqInfo.py:348: UserWarning: 'seqnames' is deprecated, use 'get_seqnames' instead
+  warn("'seqnames' is deprecated, use 'get_seqnames' instead", UserWarning)
+
      
+
      
+
      
+
      
+
      
 
      
 
      
 
@@ -520,23 +986,23 @@ 
      Combine operations
-          
+          
               
      
                 
      
                   Next
                 
      
-                
      summarizedexperiment
      
+                
      Extending summarized experiments
      
               
      
               
                   
-          
+          
               
               
      
                 
      
                   Previous
                 
      
                 
-                
      SummarizedExperiment
      
+                
      Home
      
                 
               
      
                   
@@ -570,21 +1036,31 @@ 
      Combine operations