Package: IRISFGM
Type: Package
Title: Comprehensive Analysis of Gene Interactivity Networks Based on
        Single-Cell RNA-Seq
Version: 1.6.0
Date: 2020-12-22
Authors@R: c(person("Yuzhou", "Chang", role = c("aut", "cre"),
                     email = "yuzhou.chang@osumc.edu"),
              person("Qin", "Ma", role = "aut"),
              person("Carter", "Allen", role = "aut"),
              person("Dongjun", "Chung", role = "aut"))
Description: Single-cell RNA-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 functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its availability is limited to a C implementation, and its applicative power is affected by only a few downstream analyses 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 identify co-expressed and co-regulated FGMs, predict types/clusters, identify differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM also applies Seurat objects that can be easily used in the Seurat vignettes.
License: GPL-2
Imports: Rcpp (>= 1.0.0), MCL, anocva, Polychrome, RColorBrewer,
        colorspace, AnnotationDbi, ggplot2, org.Hs.eg.db, org.Mm.eg.db,
        pheatmap, AdaptGauss, DEsingle,DrImpute, Matrix, Seurat,
        SingleCellExperiment, clusterProfiler, ggpubr, ggraph, igraph,
        mixtools, scater, scran, stats, methods, grDevices, graphics,
        utils, knitr
LinkingTo: Rcpp
VignetteBuilder: knitr
RoxygenNote: 7.1.1
Encoding: UTF-8
Collate: 'Classes.R' 'generics.R' 'AddMeta.R' 'Bicluster.R' 'Bric.R'
        'CellTypePrediction.R' 'DifferentialGene.R'
        'DimensionReducntionBasedOnLTMG.R' 'EnrichPathway.R'
        'InputData.R' 'LTMGSCA.R' 'LTMG.R' 'Object.R' 'PlotHeatmap.R'
        'PlotNetwork.R' 'PreprocessData.R' 'RcppExports.R' 'data.R'
biocViews: Software, GeneExpression, SingleCell, Clustering,
        DifferentialExpression, Preprocessing, DimensionReduction,
        Visualization, Normalization, DataImport
Depends: R (>= 4.1)
Suggests: rmarkdown
git_url: https://git.bioconductor.org/packages/IRISFGM
git_branch: RELEASE_3_16
git_last_commit: c2d45f0
git_last_commit_date: 2022-11-01
Date/Publication: 2022-11-01
NeedsCompilation: yes
Packaged: 2022-11-01 21:52:36 UTC; biocbuild
Author: Yuzhou Chang [aut, cre],
  Qin Ma [aut],
  Carter Allen [aut],
  Dongjun Chung [aut]
Maintainer: Yuzhou Chang <yuzhou.chang@osumc.edu>
Built: R 4.2.2; x86_64-conda-linux-gnu; 2022-11-08 02:00:33 UTC; unix
