RNASeqDifferentialAnalysis: RNASeqDifferentialAnalysis

In HowardChao/RNASeqWorkflow: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow

RNASeqDifferentialAnalysis

Description

This function will run differential analysis on ballgown, DESeq2 and edgeR in background.
This function do following things :

1. ballgown analysis
   Raw reads are normalized into FPKM values
   The main statistic test in ballgown is paramatic F-test comparing nested linear models
   

2. DESeq2 analysis
   Median of rations normalization(MRN) is used in DESeq2 for raw reads count normalization.
   Sequencing depth and RNA composition is taken into consideration is this normalization method.
   The main statistic test in DESeq2 is negative binomial distribution.
   

3. edgeR analysis
   Raw reads are normalized by TMM and library size. (run calcNormFactors() to get a DGEList, and then run cpm() on that DGEList)
   The main statistic test in edgeR is trimmed mean of M-values(TMM).
   

If you want to run differential analysis on ballgown, DESeq2, edgeR for the following RNA-Seq workflow in background, please see RNASeqDifferentialAnalysis() function.

Usage

RNASeqDifferentialAnalysis(RNASeqRParam, which.trigger = "OUTSIDE",
  INSIDE.path.prefix = NA, Pre_DE.visualization = TRUE,
  Post_DE.visualization = TRUE, ballgown.run = TRUE,
  ballgown.pval = 0.05, ballgown.log2FC = 1, DESeq2.run = TRUE,
  DESeq2.pval = 0.1, DESeq2.log2FC = 1, edgeR.run = TRUE,
  edgeR.pval = 0.05, edgeR.log2FC = 1, check.s4.print = TRUE)

Arguments

RNASeqRParam	S4 object instance of experiment-related parameters
which.trigger	Default value is OUTSIDE. User should not change this value.
INSIDE.path.prefix	Default value is NA. User should not change this value.
Pre_DE.visualization	Default TRUE. Whether to visualize pre-DE analysis results.
Post_DE.visualization	Default TRUE. Whether to visualize post-DE analysis results.
ballgown.run	Default TRUE. Logical value whether to run ballgown differential analysis.
ballgown.pval	Default 0.05. Set the threshold of ballgown p-value to filter out differential expressed gene.
ballgown.log2FC	Default 1. Set the threshold of ballgown log2 fold change to filter out differential expressed gene.
DESeq2.run	Default TRUE. Logical value whether to run DESeq2 differential analysis.
DESeq2.pval	Default 0.05. Set the threshold of DESeq2 p-value to filter out differential expressed gene.
DESeq2.log2FC	Default 1. Set the threshold of DESeq2 log2 fold change to filter out differential expressed gene.
edgeR.run	Default TRUE. Logical value whether to run edgeR differential analysis.
edgeR.pval	Default 0.05. Set the threshold of edgeR p-value to filter out differential expressed gene.
edgeR.log2FC	Default 1. Set the threshold of edgeR log2 fold change to filter out differential expressed gene.
check.s4.print	Default TRUE. If TRUE, the result of checking RNASeqRParam will be reported in 'Rscript_out/Environment_Set.Rout'. If FALSE, the result of checking RNASeqRParam will not be in 'Rscript_out/Environment_Set.Rout'.

Value

None

Author(s)

Kuan-Hao Chao

Examples

data(yeast)
## Not run: 
RNASeqDifferentialAnalysis(RNASeqRParam = yeast)
## End(Not run)

HowardChao/RNASeqWorkflow documentation built on May 9, 2022, 10:49 a.m.