Linnorm: Linear model and normality based transformation method (Linnorm)

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.

Package details

AuthorShun Hang Yip <[email protected]>, Panwen Wang <[email protected]>, Jean-Pierre Kocher <[email protected]>, Pak Chung Sham <[email protected]>, Junwen Wang <[email protected]>
Bioconductor views BatchEffect ChIPSeq Clustering DifferentialExpression GeneExpression Genetics Network Normalization PeakDetection RNASeq Sequencing SingleCell Software Transcription
MaintainerKen Shun Hang Yip <[email protected]>
LicenseMIT + file LICENSE
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:

Try the Linnorm package in your browser

Any scripts or data that you put into this service are public.

Linnorm documentation built on Nov. 1, 2018, 3:04 a.m.