Open AccessIRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis
Author(s) -
Yuzhou Chang,
Carter Allen,
Chunli Wan,
Dongjun Chung,
Chi Zhang,
Zihai Li,
Qin Ma
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab108
Subject(s) - cluster analysis , biclustering , source code , computer science , rna seq , computational biology , gene , identification (biology) , biology , data mining , artificial intelligence , genetics , gene expression , transcriptome , programming language , fuzzy clustering , cure data clustering algorithm , botany
Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline.