RNAseq-Data-Analysis

The repository includes the algorithms for RNA-Seq data analysis, including differentially expressed (DE) and gene-pathway interactions (GPI). The two methods are based on R. R, a free and open source software, is widely used in statistical and biological data analysis.

isoVCT

Variance Component Test with isoform (isoVCT) is an algorithm for identifying the DE genes for next generation seqencing (NGS) data. It can also automatically select a suitable distribution (Poisson or Negative Binomial) of each gene.

Getting Started

You should download the packages listed under the requirements header below. All of the packages you can download from CRAN package. The packages are as following： lme4, pipeR, MASS.

Support

Before contacting us, please try the following:

• The wiki has tutorials on RNA-Seq data, generalized linear mixed model (GLMM), variance component test and score statistics.
  
• The methods are described in the papers.
  

If that doesn't work, you can get in touch with us via the email.

Citation

If you use the software, please cite
Yang S, Shao F, Duan W, Zhao Y, Chen F: Variance component testing for identifying differentially expressed genes in RNA-seq data. PeerJ 2017, 5.

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PEA

Permutaion based gEne-pAthway interactions identification in binary phenotype (PEA) is an method for testing the gene-pathway interaction (GPI).

Installation

The code depends on Rcpp package. If your computer do not install the Rcpp package, please install it first. For the linear algebra calculation, we use the Armadillo library.

#install packages (without Rcpp package)
install.packages("Rcpp")
#library Rcpp package
library(Rcpp)
#complie the Cpp code in R environment (yourpath: the path of PEA in your computer )
sourceCpp("yourpath/PEA.cpp")

Example

We have upload the example data (simData.Rdata). Data is generated by simulation. The details of simulation is in our article.

• simData[[1]]: vector (n by 1), the binary phenotye.
  
• simData[[2]]: matrix (n by m), the covariate.
  
• simData[[3]]: matrix (n by p), the expresion level of genes in the core pathway.
  
• simData[[4]]: vector (n by 1), the expresionlevel of potential gene.
  

Testing of the GPI

After compiling the code, you can run the main function PEA.

> PEA_res <- PEA(simData[[1]], simData[[2]], simData[[4]], simData[[3]], 10000)
> PEA_res
$U
[1] 41.57699
$P
[1] 0.0592
$betaHat
          [,1]
[1,] -2.400715

The output of PEA is a list.

• PEA_res$U: scalar, the score statsitics of GPI.
  
• PEA_res$P: scalar, the P value of GPI.
  
• PEA_res$betaHat: vector, the estimation of covariates coefficient.
  

Support

Before contacting us, please try the following:

• The methods are described in the papers.
  

If that doesn't work, you can get in touch with us via the email (yangsheng@njmu.edu.cn).

Citation

If you use the software, please cite Shao F, Wang Y, Zhao Y, Yang S: Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype. BMC Genetics 2019, 20(1):36.