scbean 0.2.7

integration

scbean.VIPCCA

Variational inference of probabilistic canonical correlation analysis

introduction......

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Create conda environment

For more information about conda environment, see this tutorial.

$ conda create -n scbean python=3.6
$ conda activate scbean

Install VIPCCA from pypi

$ pip install scbean

Install VIPCCA from GitHub source code

$ git clone https://github.com/jhu99/scbean.git
$ cd ./scbean/
$ pip install .

Note: Please make sure that the pip is for python>=3.6. The current release depends on tensorflow with version 2.4.0. Install tenserfolow-gpu if gpu is avialable on the machine.

Usage

For detailed documentation, please check here.

Quick Start

Download the data of the sample we provided.

import scbean.model.vipcca as vip
import scbean.tools.utils as tl
import scbean.tools.plotting as pl

# If your script depends on a specific backend you can use the use() function:
import matplotlib
matplotlib.use('TkAgg')

# read single-cell data.
adata_b1 = tl.read_sc_data("./data/mixed_cell_lines/293t.h5ad", batch_name="293t")
adata_b2 = tl.read_sc_data("./data/mixed_cell_lines/jurkat.h5ad", batch_name="jurkat")
adata_b3 = tl.read_sc_data("./data/mixed_cell_lines/mixed.h5ad", batch_name="mixed")

# pp.preprocessing include filteration, log-TPM normalization, selection of highly variable genes.
adata_all= tl.preprocessing([adata_b1, adata_b2, adata_b3])

# VIPCCA will train the neural network on the provided datasets.
handle = vip.VIPCCA(
							adata_all,
							res_path='./results/CVAE_5/',
							split_by="_batch",
							epochs=100,
							lambda_regulizer=5,
							)

# transform user's single-cell data into shared low-dimensional space and recover gene expression.
adata_integrate=handle.fit_integrate()

# Visualization
pl.run_embedding(adata_integrate, path='./results/CVAE_5/',method="umap")
pl.plotEmbedding(adata_integrate, path='./results/CVAE_5/', method='umap', group_by="_batch",legend_loc="right margin")
pl.plotEmbedding(adata_integrate, path='./results/CVAE_5/', method='umap', group_by="celltype",legend_loc="on data")