scGEAToolbox: a Matlab toolbox for single-cell RNA sequencing data analysis Academic Article uri icon


  • AbstractMotivationThe recent development of single-cell technologies, especially single-cell RNA sequencing (scRNA-seq), provides an unprecedented level of resolution to the cell type heterogeneity. It also enables the study of gene expression variability across individual cells within a homogenous cell population. Feature selection algorithms have been used to select biologically meaningful genes while controlling for sampling noise. An easy-to-use application for feature selection on scRNA-seq data requires integration of functions for data filtering, normalization, visualization, and enrichment analyses. Graphic user interfaces (GUIs) are desired for such an application.ResultsWe used native Matlab and App Designer to develop scGEApp for feature selection on singlecell gene expression data. We specifically designed a new feature selection algorithm based on the 3D spline fitting of expression mean (), coefficient of variance (CV), and dropout rate (rdrop), making scGEApp a unique tool for feature selection on scRNA-seq data. Our method can be applied to single-sample or two-sample scRNA-seq data, identify feature genes, e.g., those with unexpectedly high CV for given and rdrop of those genes, or genes with the most feature changes. Users can operate scGEApp through GUIs to use the full spectrum of functions including normalization, batch effect correction, imputation, visualization, feature selection, and downstream analyses with GSEA and GOrilla.Availability informationSupplementary data are available at Bioinformatics online.

published proceedings

  • bioRxiv

altmetric score

  • 9.83

author list (cited authors)

  • Cai, J. J.

citation count

  • 2

complete list of authors

  • Cai, James J

publication date

  • February 2019