Yuzhou Chang1, Carter Allen1, Changlin Wan2, Dongjun Chung1, Chi Zhang2, Zihai Li3, Qin Ma1. 1. Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. 2. Center for Computational Biology and Bioinformatics and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA. 3. Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA.
Abstract
MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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