scGEAToolbox

scGEAToolbox provides computational analysis of single-cell RNA sequencing (scRNA-seq) data to quantify cell heterogeneity, gene expression variability, trajectories/pseudotime, and gene regulatory networks.

Key Features:

• Data Normalization: Functions to normalize scRNA-seq count data for comparable metrics across samples and batches.
• Feature Selection: Methods to select informative genes/features for dimensionality reduction and downstream analysis.
• Batch Correction: Procedures to mitigate batch effects that confound biological variation.
• Imputation: Techniques to impute missing or zero-inflated values in sparse scRNA-seq data.
• Cell Clustering: Algorithms to identify distinct cell populations from single-cell expression profiles.
• Trajectory/Pseudotime Analysis: Tools for inferring developmental trajectories and ordering cells along pseudotime.
• Network Construction: Functions to construct and analyze gene regulatory networks from single-cell data.

Scientific Applications:

• Cell heterogeneity analysis: Characterizing cellular diversity and gene expression variability within heterogeneous samples using scRNA-seq data.
• Developmental trajectory and pseudotime inference: Studying dynamic biological processes and developmental pathways by ordering cells in pseudotime.
• Gene regulatory network reconstruction: Inferring regulatory interactions and network structure among genes from single-cell expression profiles.
• Preprocessing for downstream analyses: Enabling normalization, batch correction, and imputation to improve the reliability of subsequent analyses.

Methodology:

Functions are implemented in native MATLAB, with wrapper functions to interface with third-party tools developed in MATLAB or other programming languages.