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Shannon Axelrod
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| .. _DecodedIntensityTable: | ||
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| DecodedIntensityTable | ||
| ===================== | ||
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| .. autoclass:: starfish.core.intensity_table.decoded_intensity_table.DecodedIntensityTable | ||
| :members: |
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176
starfish/core/intensity_table/decoded_intensity_table.py
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| from typing import Optional, Tuple | ||
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| import numpy as np | ||
| import pandas as pd | ||
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| from starfish.core.expression_matrix.expression_matrix import ExpressionMatrix | ||
| from starfish.core.intensity_table.intensity_table import IntensityTable | ||
| from starfish.core.types import ( | ||
| Axes, | ||
| Coordinates, | ||
| DecodedSpots, | ||
| Features, | ||
| ) | ||
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| class DecodedIntensityTable(IntensityTable): | ||
| """ | ||
| DecodedIntensityTable is a container for spot or pixel features extracted from image data | ||
| that have been decoded. It is the primary output from starfish :py:class:`Decode` methods. | ||
| An IntensityTable records the numeric intensity of a set of features in each | ||
| :code:`(round, channel)` tile in which the feature is identified. | ||
| The :py:class:`IntensityTable` has shape | ||
| :code:`(n_feature, n_channel, n_round)`. | ||
| Some :py:class:`SpotFinder` methods identify a position and search for Gaussian blobs in a | ||
| small radius, only recording intensities if they are found in a given tile. Other | ||
| :py:class:SpotFinder: approaches find blobs in a max-projection and measure them everywhere. | ||
| As a result, some IntensityTables will be dense, and others will contain :code:`np.nan` | ||
| entries where no feature was detected. | ||
| Examples | ||
| -------- | ||
| Create an IntensityTable using the ``synthetic_intensities`` method:: | ||
| >>> from starfish.core.test.factories import SyntheticData | ||
| >>> sd = SyntheticData(n_ch=3, n_round=4, n_codes=2) | ||
| >>> codes = sd.codebook() | ||
| >>> sd.intensities(codebook=codes) | ||
| <xarray.IntensityTable (features: 2, c: 3, h: 4)> | ||
| array([[[ 0., 0., 0., 0.], | ||
| [ 0., 0., 8022., 12412.], | ||
| [11160., 9546., 0., 0.]], | ||
| [[ 0., 0., 0., 0.], | ||
| [ 0., 0., 10506., 10830.], | ||
| [11172., 12331., 0., 0.]]]) | ||
| Coordinates: | ||
| * features (features) MultiIndex | ||
| - z (features) int64 7 3 | ||
| - y (features) int64 14 32 | ||
| - x (features) int64 32 15 | ||
| - r (features) float64 nan nan | ||
| * c (c) int64 0 1 2 | ||
| * h (h) int64 0 1 2 3 | ||
| target (features) object 08b1a822-a1b4-4e06-81ea-8a4bd2b004a9 ... | ||
| """ | ||
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| @classmethod | ||
| def from_intensity_table( | ||
| cls, | ||
| intensities: IntensityTable, | ||
| targets: Tuple[str, np.ndarray], | ||
| distances: Optional[Tuple[str, np.ndarray]] = None, | ||
| passes_threshold: Optional[Tuple[str, np.ndarray]] = None): | ||
| """ | ||
| Assign target values to intensities. | ||
| Parameters | ||
| ---------- | ||
| intensities : IntensityTable | ||
| intensity_table to assign target values to | ||
| targets : Tuple[str, np.ndarray] | ||
| Target values to assign | ||
| distances : Optional[Tuple[str, np.ndarray]] | ||
| Corresponding array of distances from target for each feature | ||
| passes_threshold : Optional[Tuple[str, np.ndarray]] | ||
| Corresponding array of boolean values indicating if each itensity passed | ||
| given thresholds. | ||
| Returns | ||
| ------- | ||
| DecodedIntensityTable | ||
| """ | ||
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| intensities = cls(intensities) | ||
| intensities[Features.TARGET] = targets | ||
| if distances: | ||
| intensities[Features.DISTANCE] = distances | ||
| if passes_threshold: | ||
| intensities[Features.PASSES_THRESHOLDS] = passes_threshold | ||
| return intensities | ||
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| def to_decoded_dataframe(self) -> DecodedSpots: | ||
| """ | ||
| Generates a dataframe containing decoded spot information. Guaranteed to contain physical | ||
| spot coordinates (z, y, x) and gene target. Does not contain pixel coordinates. | ||
| """ | ||
| df = self.to_features_dataframe() | ||
| pixel_coordinates = pd.Index([Axes.X, Axes.Y, Axes.ZPLANE]) | ||
| df = df.drop(pixel_coordinates.intersection(df.columns), axis=1).drop(Features.AXIS, axis=1) | ||
| return DecodedSpots(df) | ||
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| def to_mermaid(self, filename: str) -> pd.DataFrame: | ||
| """ | ||
| Writes a .csv.gz file in columnar format that is readable by MERMAID visualization | ||
| software. | ||
| To run MERMAID, follow the installation instructions for that repository and simply | ||
| replace the data.csv.gz file with the output of this function. | ||
| Parameters | ||
| ---------- | ||
| filename : str | ||
| Name for compressed-gzipped MERMAID data file. Should end in '.csv.gz'. | ||
| Notes | ||
| ------ | ||
| See also https://github.com/JEFworks/MERmaid | ||
| """ | ||
| # construct the MERMAID dataframe. As MERMAID adds support for non-categorical variables, | ||
| # additional columns can be added here | ||
| df = self.to_features_dataframe() | ||
| column_order = [ | ||
| Axes.X, | ||
| Axes.Y, | ||
| Features.TARGET, | ||
| Features.TARGET, # added twice to support simultaneous coding | ||
| ] | ||
| mermaid_data = df[column_order] | ||
|
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| # write to disk | ||
| mermaid_data.to_csv(filename, compression='gzip', index=False) | ||
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| def to_expression_matrix(self) -> ExpressionMatrix: | ||
| """ | ||
| Generates a cell x gene count matrix where each cell is annotated with spatial metadata. | ||
| Requires that spots in the IntensityTable have been assigned to cells. | ||
| Returns | ||
| ------- | ||
| ExpressionMatrix : | ||
| cell x gene expression table | ||
| """ | ||
| if Features.CELL_ID not in self.coords: | ||
| raise KeyError( | ||
| "IntensityTable must have 'cell_id' assignments for each cell before this function " | ||
| "can be called. See starfish.spots.AssignTargets.Label.") | ||
| grouped = self.to_features_dataframe().groupby([Features.CELL_ID, Features.TARGET]) | ||
| counts = grouped.count().iloc[:, 0].unstack().fillna(0) | ||
| if self.has_physical_coords: | ||
| grouped = self.to_features_dataframe().groupby([Features.CELL_ID])[[ | ||
| Axes.X.value, Axes.Y.value, Axes.ZPLANE.value, Coordinates.X.value, | ||
| Coordinates.Y.value, Coordinates.Z.value]] | ||
| else: | ||
| grouped = self.to_features_dataframe().groupby([Features.CELL_ID])[[ | ||
| Axes.X.value, Axes.Y.value, Axes.ZPLANE.value]] | ||
| min_ = grouped.min() | ||
| max_ = grouped.max() | ||
| coordinate_df = min_ + (max_ - min_) / 2 | ||
| metadata = {name: (Features.CELLS, data.values) for name, data in coordinate_df.items()} | ||
| metadata[Features.AREA] = (Features.CELLS, np.full(counts.shape[0], fill_value=np.nan)) | ||
| # add genes to the metadata | ||
| metadata.update({Features.GENES: counts.columns.values}) | ||
| metadata.update({Features.CELL_ID: (Features.CELLS, counts.index.values)}) | ||
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| mat = ExpressionMatrix( | ||
| data=counts.values, | ||
| dims=(Features.CELLS, Features.GENES), | ||
| coords=metadata, | ||
| name='expression_matrix' | ||
| ) | ||
| return mat |
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