README.md

In tangchao7498/ICAS:

Intron Centric Alternative Splicing analysis

Introduction

For post-transcriptional splicing analysis, an important question is the quantification and statistical inference of systematic changes between conditions, as compared to within-condition variability. Here, we have developed a newR package named ICAS (Intron-Centric Alternative Splicing) to quantify alternative spliced introns and many differential splicing analysis methods. Compared with existing methods, ICAS are more robust in both quantitative and subsequent analysis, such as differential splicing and splice QTL analysis, indicating that our methods are less affected by other confounding factors, such as sequencing library construction. Also, ICAS has many other advantages and functions. First, ICAS can find novel splicing events. Second, ICAS can provide differentially expressed alternative splicing event analysis and visualization. Moreover, ICAS runs very fast and is not affected by the number of samples.

Requirements

ICAS relies on many successful R packages. If you want to use ICAS, you'd better install the following dependency packages first.

• methods
• parallel
• patchwork
• pbapply
• BiocGenerics
• GenomeInfoDb
• GenomicAlignments
• GenomicFeatures
• GenomicRanges
• IRanges
• MAST
• Matrix
• ROCR
• Rsamtools
• S4Vectors
• SummarizedExperiment
• VGAM
• data.table
• ggbio
• lmtest
• matrixStats
• parallel
• pbapply
• pheatmap
• reshape2
• rtracklayer
• stats
• utils
• varSelRF

Installation

Your can install ICAS from GitHub by: ```{r eval=FALSE} library(devtools) devtools::install_github("tangchao7498/ICAS")

## Citing the ICAS package

If you're using ICAS in a publication, you can get the citation by
```{r citation,eval=FALSE}
citation("ICAS")

Full documentation

https://github.com/tangchao7498/ICAS/wiki

https://github.com/tangchao7498/ICAS/blob/master/vignettes/ICAS_Tutorial.pdf

tangchao7498/ICAS documentation built on Jan. 28, 2021, 3:56 p.m.