DOI: 10.18129/B9.bioc.Linnorm    

Linear model and normality based transformation method (Linnorm)

Bioconductor version: Release (3.6)

Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. It transforms such datasets for parametric tests. In addition to the transformtion function (Linnorm), the following pipelines are implemented: 1. Library size/Batch effect normalization (Linnorm.Norm), 2. Cell subpopluation analysis and visualization using t-SNE or PCA K-means clustering or Hierarchical clustering (Linnorm.tSNE, Linnorm.PCA, Linnorm.HClust), 3. Differential expression analysis or differential peak detection using limma (Linnorm.limma), 4. Highly variable gene discovery and visualization (Linnorm.HVar), 5. Gene correlation network analysis and visualization (Linnorm.Cor), 6. Stable gene selection for scRNA-seq data; for users without or do not want to rely on spike-in genes (Linnorm.SGenes). 7. Data imputation. (under development) (Linnorm.DataImput). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, the RnaXSim function is included for simulating RNA-seq data for the evaluation of DEG analysis methods.

Author: Shun Hang Yip <shunyip at>, Panwen Wang <pwwang at>, Jean-Pierre Kocher <Kocher.JeanPierre at>, Pak Chung Sham <pcsham at>, Junwen Wang <junwen at>

Maintainer: Ken Shun Hang Yip <shunyip at>

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biocViews BatchEffect, ChIPSeq, Clustering, DifferentialExpression, GeneExpression, Genetics, Network, Normalization, PeakDetection, RNASeq, Sequencing, SingleCell, Software, Transcription
Version 2.2.0
In Bioconductor since BioC 3.3 (R-3.3) (2 years)
License MIT + file LICENSE
Depends R (>= 3.4)
Imports Rcpp (>= 0.12.2), RcppArmadillo (>=, fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels
LinkingTo Rcpp, RcppArmadillo
Suggests BiocStyle, knitr, rmarkdown, gplots, RColorBrewer, moments, testthat
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