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This package is for version 3.4 of Bioconductor; for the stable, up-to-date release version, see Linnorm.

Linear model and normality based transformation method (Linnorm)

Bioconductor version: 3.4

Please note that significant updates to Linnorm are available in version 1.99.x +, we strongly suggest using the newest version. 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, the following pipelines are implemented: 1. Cell subpopluation analysis and visualization using PCA clustering, 2. Differential expression analysis or differential peak detection using limma, 3. Highly variable gene discovery and visualization, 4. Gene correlation network analysis and visualization. 5. Hierarchical clustering and plotting. Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, Linnorm provides the RnaXSim function for the simulation of RNA-seq raw counts for the evaluation of differential expression analysis methods. RnaXSim can simulate RNA-seq dataset in Gamma, Log Normal, Negative Binomial or Poisson distributions.

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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PDF R Script Linnorm User Manual
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biocViews BatchEffect, ChIPSeq, Clustering, DifferentialExpression, GeneExpression, Genetics, Network, Normalization, PeakDetection, RNASeq, Sequencing, Software, Transcription
Version 1.2.11
In Bioconductor since BioC 3.3 (R-3.3) (1 year)
License MIT + file LICENSE
Depends R (>= 3.3)
Imports Rcpp (>= 0.12.2), RcppArmadillo, fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap
LinkingTo Rcpp, RcppArmadillo
Suggests BiocStyle, knitr, rmarkdown, gplots, RColorBrewer
Depends On Me
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