integration
Project description
scbean
scbean is a package we provide for single-cell data integration and other tasks.
scbean--VIPCCA
Variational inference of probabilistic canonical correlation analysis
introduction...... ............
Create conda environment
For more information about conda environment, see this tutorial.
$ conda create -n scbean python=3.6
$ conda activate scbean
Install scbean from pypi
$ pip install scbean
Install scbean 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. -
If there is a need to run large data sets, we provide version 1.1.1 (depending on tensorflow 1.15.1), which uses sparseTensor to reduce memory usage.
$ pip install scbean==1.1.1
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")
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