Interpretation of Heterogeneous Single-Cell Gene Expression Data.

scINSIGHT for interpreting single cell gene expression in biologically heterogeneous data

Kun Qian, Wei Vivian Li 2025-10-19

Latest News

2025/10/19:

• Version 0.1.5 released!

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 ([email protected]) or Vivian ([email protected]).

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.