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Vivianstats / scINSIGHT

Matrix factorization model for interpreting single cell gene expression in biologically heterogeneous data
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02649-3
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bioinformatics gene-expression integration scrna-seq

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scINSIGHT for interpreting single cell gene expression in biologically heterogeneous data

Kun Qian, Wei Vivian Li 2022-05-20

Latest News

2022/05/19:

Introduction

scINSIGHT uses a novel matrix factorization model to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. It assumes that each gene module is a sparse and non-negative linear combination of genes, and each cell is jointly defined by the expression of common and condition-specific modules. Given multiple gene expression samples from different biological conditions, scINSIGHT aims to simultaneously identify common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space.

Any suggestions on the package are welcome! For technical problems, please report to Issues. For suggestions and comments on the method, please contact Kun (kun_qian@foxmail.com) or Vivian (vivian.li@rutgers.edu).

Installation

You can install scINSIGHT from CRAN with:

install.packages("scINSIGHT")

Usage

Please refer to the package vignette for examples about how to use the package functions.