Linnorm

DOI: 10.18129/B9.bioc.Linnorm    

This package is for version 3.7 of Bioconductor; for the stable, up-to-date release version, see Linnorm.

Linear model and normality based transformation method (Linnorm)

Bioconductor version: 3.7

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 bu.edu>, Panwen Wang <pwwang at pwwang.com>, Jean-Pierre Kocher <Kocher.JeanPierre at mayo.edu>, Pak Chung Sham <pcsham at hku.hk>, Junwen Wang <junwen at uw.edu>

Maintainer: Ken Shun Hang Yip <shunyip at bu.edu>

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("Linnorm")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("Linnorm")

 

PDF	R Script	Linnorm User Manual
PDF		Reference Manual
Text		NEWS
Text		LICENSE

Details

biocViews	BatchEffect, ChIPSeq, Clustering, DifferentialExpression, GeneExpression, Genetics, Network, Normalization, PeakDetection, RNASeq, Sequencing, SingleCell, Software, Transcription
Version	2.4.0
In Bioconductor since	BioC 3.3 (R-3.3) (2.5 years)
License	MIT + file LICENSE
Depends	R (>= 3.4)
Imports	Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), 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
SystemRequirements
Enhances
URL	http://www.jjwanglab.org/Linnorm/
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	Linnorm_2.4.0.tar.gz
Windows Binary	Linnorm_2.4.0.zip (32- & 64-bit)
Mac OS X 10.11 (El Capitan)	Linnorm_2.4.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/Linnorm
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/Linnorm
Package Short Url	http://bioconductor.org/packages/Linnorm/
Package Downloads Report	Download Stats