.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/preprocessing/plot_pca_neighbors_clustering.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_preprocessing_plot_pca_neighbors_clustering.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_preprocessing_plot_pca_neighbors_clustering.py:
cluster generation
==================
This example demonstrates how to perform highly variable gene selection, PCA, nearest neighbor calculation, and clustering.
.. GENERATED FROM PYTHON SOURCE LINES 8-19
.. code-block:: default
import besca as bc
import scanpy as sc
import pytest
# pytest.skip('Test is only for here as example and should not be executed')
# import example dataset that has previously been filtered
adata = bc.datasets.pbmc3k_filtered()
## We get the raw matrix containing all the initial genes, keeping the filtering on the cells
adata = bc.get_raw(adata)
.. GENERATED FROM PYTHON SOURCE LINES 20-24
highly variable gene selection
------------------------------
select highly variable genes (considers correction for gene expression level)
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.. code-block:: default
# define thresholds for highly variable genes
variable_genes_min_mean = 0.01
variable_genes_max_mean = 5
variable_genes_min_disp = 0.4
# identify genes with variable expression
filter_result = sc.pp.filter_genes_dispersion(
adata.X,
min_mean=variable_genes_min_mean,
max_mean=variable_genes_max_mean,
min_disp=variable_genes_min_disp,
)
sc.pl.filter_genes_dispersion(filter_result)
nbr_variable_genes = sum(filter_result.gene_subset)
print("number of variable genes selected ", nbr_variable_genes)
# perform the actual filtering
adata = adata[:, filter_result.gene_subset]
.. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_001.png
:alt: plot pca neighbors clustering
:srcset: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
number of variable genes selected 1897
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set random seed
---------------
To get reproducible results you need to define a random seed for all of the stochastic
processes, such as e.g. PCA, neighbors, etc.
.. GENERATED FROM PYTHON SOURCE LINES 49-53
.. code-block:: default
# set random seed
random_seed = 0
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PCA
---
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.. code-block:: default
# log transform our data (is easier to work with numbers like this)
sc.pp.log1p(adata)
# Scale data to unit variance and zero mean, and cut-off at max value 10
sc.pp.scale(adata, max_value=10)
# calculate 50 principle components of the dataset
sc.tl.pca(adata, random_state=random_seed, svd_solver="arpack")
# visualize the amount of variance explained by each PC
sc.pl.pca_variance_ratio(adata)
# visualize the loadings onto the first 3 PCs
sc.pl.pca_loadings(adata)
.. rst-class:: sphx-glr-horizontal
*
.. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_002.png
:alt: variance ratio
:srcset: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_002.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_003.png
:alt: PC1, PC2, PC3
:srcset: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_003.png
:class: sphx-glr-multi-img
.. GENERATED FROM PYTHON SOURCE LINES 73-75
nearest neighbors
-----------------
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.. code-block:: default
sc.pp.neighbors(adata, n_neighbors=15, random_state=random_seed, n_pcs=50)
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leiden clustering
------------------
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.. code-block:: default
sc.tl.leiden(adata, random_state=random_seed)
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UMAP and t-SNE generation
-------------------------
.. GENERATED FROM PYTHON SOURCE LINES 87-94
.. code-block:: default
# calculate UMAP
sc.tl.umap(adata, random_state=random_seed)
# calculate t-SNE
sc.tl.tsne(adata, random_state=random_seed)
.. GENERATED FROM PYTHON SOURCE LINES 95-97
visualize the results
---------------------
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.. code-block:: default
sc.pl.umap(adata, color=["leiden"])
sc.pl.tsne(adata, color=["leiden"])
.. rst-class:: sphx-glr-horizontal
*
.. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_004.png
:alt: leiden
:srcset: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_004.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_005.png
:alt: leiden
:srcset: /auto_examples/preprocessing/images/sphx_glr_plot_pca_neighbors_clustering_005.png
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 11.037 seconds)
.. _sphx_glr_download_auto_examples_preprocessing_plot_pca_neighbors_clustering.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_pca_neighbors_clustering.py <plot_pca_neighbors_clustering.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_pca_neighbors_clustering.ipynb <plot_pca_neighbors_clustering.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_