scbean 0.4.0

integration

Scbean.VIPCCA

Variational inference of probabilistic canonical correlation analysis (VIPCCA) was implemented in a python package scbean, providing a range of single-cell data analysis including dimension reduction, remvoing batch-effects, transfer well-annotated celltype labels from scRNA-seq onto scATAC-seq cells by learning from the integrated data. It's efficient and scalable for large-scale datasets with more than 1 million cells. We will also provide more fundamental analyses for multi-modal data and spatial resoved transcriptomics in the future. The output can be easily used for downstream data analyses such as clustering, identification of cell subpopulations, differential gene expression, visualization using either Seurat or Scanpy.

Installation

• Create conda environment

  $ conda create -n scbean python=3.8
  $ conda activate scbean
  

• Install scbean from pypi

  $ pip install scbean
  

• Alternatively, install the develop version of scbean from GitHub source code

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

Note: Please make sure your python version >= 3.7, and install tensorflow-gpu if GPU is available on your your machine.

Usage

For detailed guide about the usage of scbean, the tutorial and documentation were provided here.

Quick start

Download the data of the following test code.

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

# Please choose an appropiate matplotlib backend.
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")

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

# Construct VIPCCA with specific setting.
handle = vip.VIPCCA(
							adata_all,
							res_path='./results/CVAE_5/',
							split_by="_batch",
							epochs=100,
							lambda_regulizer=5,
							)

# Training and integrating multiple single-cell datasets. The VIPCCA's output include cell representation in reduced dimensional space and recovered 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")