diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4144df3..6e888c8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,11 @@
 # Changelog
 
+## Version 0.4.2 - 0.4.7
+
+- Fix issue coercing `SummarizedExperiments` to `AnnData` objects and vice-versa.
+- Handling coercions when matrices are delayed arrays 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/README.md b/README.md
index 222359d..d839369 100644
--- a/README.md
+++ b/README.md
@@ -4,20 +4,26 @@
 
 # SingleCellExperiment
 
-Container class to represent single-cell experiments; follows Bioconductor's [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html).
+This package provides container class to represent single-cell experimental data as 2-dimensional matrices. In these matrices, the rows typically denote features or genomic regions of interest, while columns represent cells. In addition, a `SingleCellExperiment` (SCE) object may contain low-dimensionality embeddings, alternative experiments performed on same sample or set of cells. Follows Bioconductor's [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html).
 
 
 ## Install
 
-Package is published to [PyPI](https://pypi.org/project/singlecellexperiment/)
+To get started, install the package from [PyPI](https://pypi.org/project/singlecellexperiment/)
 
-```shell
+```bash
 pip install singlecellexperiment
 ```
 
 ## Usage
 
-Readers are available to read AnnData, H5AD or 10x (MTX, H5) V3 formats as `SingleCellExperiment` objects.
+The `SingleCellExperiment` extends [RangeSummarizedExperiment](https://github.com/BiocPy/SummarizedExperiment) and contains additional attributes:
+
+- `reduced_dims`: Slot for low-dimensionality embeddings for each cell.
+- `alternative_experiments`: Manages multi-modal experiments performed on the same sample or set of cells.
+- `row_pairs` or `column_pairs`: Stores relationships between features or cells.
+
+Readers are available to parse h5ad or `AnnData` objects to SCE:
 
 ```python
 import singlecellexperiment
@@ -51,7 +57,8 @@ tse = SingleCellExperiment(
 )
 ```
 
-Since `SingleCellExperiment` extends `SummarizedExperiment`, most methods especially slicing and accessors are applicable here. Checkout the [documentation](https://biocpy.github.io/SingleCellExperiment/) for more info.
+Since `SingleCellExperiment` extends `RangeSummarizedExperiment`, most methods especially slicing and accessors are applicable here.
+Checkout the [documentation](https://biocpy.github.io/SingleCellExperiment/) for more info.
 
 <!-- pyscaffold-notes -->
 
diff --git a/docs/conf.py b/docs/conf.py
index 47c7c73..247476e 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -72,6 +72,7 @@
     "sphinx.ext.ifconfig",
     "sphinx.ext.mathjax",
     "sphinx.ext.napoleon",
+    "sphinx_autodoc_typehints",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -79,7 +80,8 @@
 
 
 # Enable markdown
-extensions.append("myst_parser")
+# extensions.append("myst_parser")
+extensions.append("myst_nb")
 
 # Configure MyST-Parser
 myst_enable_extensions = [
@@ -167,9 +169,11 @@
 todo_emit_warnings = True
 
 autodoc_default_options = {
-    'special-members': True,
-    'undoc-members': False,
-    'exclude-members': '__weakref__, __dict__, __str__, __module__, __init__'
+    # 'members': 'var1, var2',
+    # 'member-order': 'bysource',
+    "special-members": True,
+    "undoc-members": True,
+    "exclude-members": "__weakref__, __dict__, __str__, __module__",
 }
 
 autosummary_generate = True
@@ -309,7 +313,8 @@
     "pyscaffold": ("https://pyscaffold.org/en/stable", None),
     "biocframe": ("https://biocpy.github.io/BiocFrame", None),
     "genomicranges": ("https://biocpy.github.io/GenomicRanges", None),
-    "singlecellexperiment": ("https://biocpy.github.io/SingleCellExperiment", None),
+    "summarizedexperiment": ("https://biocpy.github.io/SummarizedExperiment", None),
+    "biocutils": ("https://biocpy.github.io/BiocUtils", None),
 }
 
 print(f"loading configurations for {project} {version} ...", file=sys.stderr)
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
index 06bbeaf..bac8220 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,22 +1,22 @@
 # SingleCellExperiment
 
-Container class for representing data from single-cell experiments; follows Bioconductor's [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html).
+This package provides container class to represent single-cell experimental data as 2-dimensional matrices. In these matrices, the rows typically denote features or genomic regions of interest, while columns represent cells. In addition, a `SingleCellExperiment` (SCE) object may contain low-dimensionality embeddings, alternative experiments performed on same sample or set of cells. Follows Bioconductor's [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html).
 
 
 ## Install
 
-Package is published to [PyPI](https://pypi.org/project/singlecellexperiment/)
+To get started, install the package from [PyPI](https://pypi.org/project/singlecellexperiment/)
 
-```shell
+```bash
 pip install singlecellexperiment
 ```
+
 ## Contents
 
 ```{toctree}
 :maxdepth: 2
 
-Overview <readme>
-Tutorial <tutorial>
+Overview <tutorial>
 Module Reference <api/modules>
 Contributions & Help <contributing>
 License <license>
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 6d65a9b..c20cf60 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,7 +1,9 @@
 furo
+myst-nb
 # Requirements file for ReadTheDocs, check .readthedocs.yml.
 # To build the module reference correctly, make sure every external package
 # under `install_requires` in `setup.cfg` is also listed here!
 # sphinx_rtd_theme
 myst-parser[linkify]
 sphinx>=3.2.1
+sphinx-autodoc-typehints
diff --git a/docs/tutorial.md b/docs/tutorial.md
index 984adec..43081c7 100644
--- a/docs/tutorial.md
+++ b/docs/tutorial.md
@@ -1,33 +1,59 @@
-# Tutorial
+---
+file_format: mystnb
+kernelspec:
+  name: python
+---
 
-Container class to represent single-cell experiments; follows Bioconductor's [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html).
+## Represent single-cell experiments
 
-# Import as `SingleCellExperiment`
+This package provides container class to represent single-cell experimental data as 2-dimensional matrices. In these matrices, the rows typically denote features or genomic regions of interest, while columns represent cells. In addition, a `SingleCellExperiment` (SCE) object may contain low-dimensionality embeddings, alternative experiments performed on same sample or set of cells.
 
-Readers are available to read `AnnData`, `H5AD` or `10x (MTX, H5) V3` formats as `SingleCellExperiment` objects.
+:::{important}
+The design of `SingleCellExperiment` class and its derivates adheres to the R/Bioconductor specification, where rows correspond to features, and columns represent cells.
+:::
 
-```python
-import singlecellexperiment
+:::{note}
+These classes follow a functional paradigm for accessing or setting properties, with further details discussed in [functional paradigm](https://biocpy.github.io/tutorial/chapters/philosophy.html#functional-discipline) section.
+:::
+
+## Installation
+
+To get started, install the package from [PyPI](https://pypi.org/project/singlecellexperiment/)
 
-sce = singlecellexperiment.read_h5ad("tests/data/adata.h5ad")
+```bash
+pip install singlecellexperiment
 ```
 
-Similarly `read_tenx_h5`, `read_tenx_mtx` and `from_anndata` methods are  available to read various formats.
+## Construction
 
-# Construct a `SingleCellExperiment` object
+The `SingleCellExperiment` extends `RangeSummarizedExperiment` and contains additional attributes:
 
-`SingleCellExperiment` extends `SummarizedExperiment`, so it must contain `assays`, `row_data` and `column_data` objects. Additionally
-`SingleCellExperiment` objects may contain dimensionality embeddings (e.g tSNE, UMAP etc), alternative experiment for multi-modal experiments and row/column pairings.
+- `reduced_dims`: Slot for low-dimensionality embeddings for each cell.
+- `alternative_experiments`: Manages multi-modal experiments performed on the same sample or set of cells.
+- `row_pairs` or `column_pairs`: Stores relationships between features or cells.
+
+:::{note}
+In contrast to R, matrices in Python are unnamed and do not contain row or column names. Hence, these matrices cannot be directly used as values in assays or alternative experiments. We strictly enforce type checks in these cases. To relax these restrictions for alternative experiments, set `type_check_alternative_experiments` to `False`.
+:::
+
+:::{important}
+If you are using the `alternative_experiments` slot, the number of cells must match the parent experiment.  Otherwise, the expectation is that the cells do not share the same sample or annotations and cannot be set in alternative experiments!
+:::
+
+Before we construct a `SingleCellExperiment` object, lets generate information about rows, columns and a mock experimental data from single-cell rna-seq experiments:
+
+```{code-cell}
 
-```python
 import pandas as pd
 import numpy as np
+from scipy import sparse as sp
 from biocframe import BiocFrame
 from genomicranges import GenomicRanges
+from random import random
 
 nrows = 200
 ncols = 6
-counts = np.random.rand(nrows, ncols)
+counts = sp.rand(nrows, ncols, density=0.2, format="csr")
 row_data = BiocFrame(
     {
         "seqnames": [
@@ -58,59 +84,248 @@ col_data = pd.DataFrame(
 )
 ```
 
-Finally construct the object,
+Now lets create the `SingleCellExperiment` instance:
 
-```python
+```{code-cell}
 from singlecellexperiment import SingleCellExperiment
 
-tse = SingleCellExperiment(
-    assays={"counts": counts}, row_data=row_data, column_data=col_data
+sce = SingleCellExperiment(
+    assays={"counts": counts}, row_data=row_data, column_data=col_data,
+    reduced_dims = {"random_embeds": np.random.rand(ncols, 4)}
 )
+
+print(sce)
 ```
 
-# Accessors
 
-Multiple methods are available to access various slots of a `SingleCellExperiment` object
+:::{tip}
+You can also use delayed or file-backed arrays for representing experimental data, check out [this section](./summarized_experiment.qmd#delayed-or-file-backed-arrays) from summarized experiment.
+:::
 
-```python
-tse.assays
-tse.row_data
-tse.column_data
-tse.reduced_dims
-tse.alternative_experiments
-tse.row_pairs
-tse.column_pairs
+
+### Interop with `anndata`
+
+We provide convenient methods for loading an `AnnData` or `h5ad` file into `SingleCellExperiment` objects.
+
+For example, lets create an `AnnData` object,
+
+```{code-cell}
+import anndata as ad
+from scipy import sparse as sp
+
+counts = sp.csr_matrix(np.random.poisson(1, size=(100, 2000)), dtype=np.float32)
+adata = ad.AnnData(counts)
+print(adata)
 ```
 
-### Access specific sets
+Converting `AnnData` as `SingleCellExperiment` is straightforward:
+
+```{code-cell}
+sce_adata = SingleCellExperiment.from_anndata(adata)
+print(sce_adata)
+```
 
-For reduced dimension and alternative experiment slots, one can also access specific objects
 
-```python
-tse.reduced_dim("tSNE")
+and vice-verse.  All assays from SCE are represented in the `layers` slot of the `AnnData` object:
 
-tse.alternative_experiment("crop-seq")
+```{code-cell}
+adata2 = sce_adata.to_anndata()
+print(adata2)
 ```
 
-# Subset experiment
+Similarly, one can load a h5ad file:
 
-Similar to `SummarizedExperiment`, you can subset by index
 
 ```python
-# subset the first 10 rows and the first 3 samples
-subset_tse = tse[0:10, 0:3]
+from singlecellexperiment import read_h5ad
+sce_h5 = read_h5ad("../../assets/data/adata.h5ad")
+print(sce_h5)
 ```
 
-# Export as AnnData objects
+### From 10X formats
 
-Methods are available to convert `SingleCellExperiment` objects as `AnnData`
+In addition, we also provide convenient methods to load a [10X Genomics HDF5 Feature-Barcode Matrix Format](https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/outputs/cr-outputs-h5-matrices) file.
 
 ```python
-adata = tse.to_anndata()
+from singlecellexperiment import read_tenx_h5
+sce_h5 = read_tenx_h5("../../assets/data/tenx.sub.h5")
+print(sce_h5)
 ```
 
-or an `MuData`
+:::{note}
+Methods are also available to read a 10x matrix market directory using the `read_tenx_mtx` function.
+:::
 
-```python
-mdata = tse.to_mudata()
+## Getters/Setters
+
+Getters are available to access various attributes using either the property notation or functional style.
+
+Since `SingleCellExperiment` extends `RangedSummarizedExperiment`, all getters and setters from the base class are accessible here; more details [here](./summarized_experiment.qmd).
+
+```{code-cell}
+# access assay names
+print("reduced dim names (as property): ", sce.reduced_dim_names)
+print("reduced dim names (functional style): ", sce.get_reduced_dim_names())
+
+# access row data
+print(sce.row_data)
+```
+
+#### Access a reduced dimension
+
+One can access an reduced dimension by index or name:
+
+```{code-cell}
+sce.reduced_dim(0) # same as se.reduced_dim("random_embeds")
+```
+
+## 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 `SingleCellExperiment` object that includes names.
+
+```{code-cell}
+subset_sce = sce[0:10, 0:3]
+print(subset_sce)
+```
+
+
+## Combining experiments
+
+`SingleCellExperiment` implements methods for the `combine` generic from [**BiocUtils**](https://github.com/BiocPy/biocutils).
+
+These methods enable the merging or combining of multiple `SingleCellExperiment` objects, allowing users to aggregate data from different experiments or conditions. Note: `row_pairs` and `column_pairs` are not ignored as part of this operation.
+
+
+To demonstrate, let's create multiple `SingleCellExperiment` objects (read more about this in [combine section from `SummarizedExperiment`](./summarized_experiment.qmd#combining-experiments)).
+
+```{code-cell}
+
+ncols = 10
+nrows = 100
+sce1 = SingleCellExperiment(
+    assays={"counts": np.random.poisson(lam=10, size=(nrows, ncols))},
+    row_data=BiocFrame({"A": [1] * nrows}),
+    column_data=BiocFrame({"A": [1] * ncols}),
+)
+
+sce2 = SingleCellExperiment(
+    assays={
+        "counts": np.random.poisson(lam=10, size=(nrows, ncols)),
+        # "normalized": np.random.normal(size=(nrows, ncols)),
+    },
+    row_data=BiocFrame({"A": [3] * nrows}),
+    column_data=BiocFrame({"A": [3] * ncols}),
+)
+
+rowdata1 = pd.DataFrame(
+    {
+        "seqnames": ["chr_5", "chr_3", "chr_2"],
+        "start": [500, 300, 200],
+        "end": [510, 310, 210],
+    },
+    index=["HER2", "BRCA1", "TPFK"],
+)
+coldata1 = pd.DataFrame(
+    {
+        "sample": ["SAM_1", "SAM_2", "SAM_3"],
+        "disease": ["True", "True", "True"],
+        "doublet_score": [0.15, 0.62, 0.18],
+    },
+    index=["cell_1", "cell_2", "cell_3"],
+)
+sce_alts1 = SingleCellExperiment(
+    assays={
+        "counts": np.random.poisson(lam=5, size=(3, 3)),
+        "lognorm": np.random.lognormal(size=(3, 3)),
+    },
+    row_data=rowdata1,
+    column_data=coldata1,
+    row_names=["HER2", "BRCA1", "TPFK"],
+    column_names=["cell_1", "cell_2", "cell_3"],
+    metadata={"seq_type": "paired"},
+    reduced_dims={"PCA": np.random.poisson(lam=10, size=(3, 5))},
+    alternative_experiments={
+        "modality1": SingleCellExperiment(
+            assays={"counts2": np.random.poisson(lam=10, size=(3, 3))},
+        )
+    },
+)
+
+rowdata2 = pd.DataFrame(
+    {
+        "seqnames": ["chr_5", "chr_3", "chr_2"],
+        "start": [500, 300, 200],
+        "end": [510, 310, 210],
+    },
+    index=["HER2", "BRCA1", "TPFK"],
+)
+coldata2 = pd.DataFrame(
+    {
+        "sample": ["SAM_4", "SAM_5", "SAM_6"],
+        "disease": ["True", "False", "True"],
+        "doublet_score": [0.05, 0.23, 0.54],
+    },
+    index=["cell_4", "cell_5", "cell_6"],
+)
+sce_alts2 = SingleCellExperiment(
+    assays={
+        "counts": np.random.poisson(lam=5, size=(3, 3)),
+        # "lognorm": np.random.lognormal(size=(3, 3)),
+    },
+    row_data=rowdata2,
+    column_data=coldata2,
+    metadata={"seq_platform": "Illumina NovaSeq 6000"},
+    reduced_dims={"PCA": np.random.poisson(lam=5, size=(3, 5))},
+    alternative_experiments={
+        "modality1": SingleCellExperiment(
+            assays={"counts2": np.random.poisson(lam=5, size=(3, 3))},
+        )
+    },
+)
+
+```
+
+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
+sce_combined = combine_rows(sce2, sce1)
+print(sce_combined)
+```
+
+Similarly to combine by column:
+
+```{code-cell}
+sce_combined = combine_columns(sce2, sce1)
+print(sce_combined)
+```
+
+:::{note}
+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.
+:::
+
+```{code-cell}
+# sce_alts1 contains an additional assay not present in sce_alts2
+sce_relaxed_combine = relaxed_combine_columns(sce_alts1, sce_alts2)
+print(sce_relaxed_combine)
+```
+
+
+## Export as `AnnData` or `MuData`
+
+The package also provides methods to convert a `SingleCellExperiment` object into a `MuData` representation:
+
+```{code-cell}
+mdata = sce.to_mudata()
+mdata
+```
+
+or coerce to an `AnnData` object:
+
+```{code-cell}
+adata, alts = sce.to_anndata()
+print("main experiment: ", adata)
+print("alternative experiments: ", alts)
 ```
diff --git a/setup.cfg b/setup.cfg
index b07b14a..0459b42 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -49,7 +49,7 @@ python_requires = >=3.8
 # For more information, check out https://semver.org/.
 install_requires =
     importlib-metadata; python_version<"3.8"
-    summarizedexperiment>=0.4.0,<0.5.0
+    summarizedexperiment>=0.4.5
 
 [options.packages.find]
 where = src