riborex: Identifying differential translation from ribosome profiling...

In smithlabcode/riborex: Identification of differential translation from Ribo-seq data

Description

This function identifies differentially translated genes by co-analyzing both RNA- and Ribo-seq data. It uses existing RNA-seq data analysis tools as its engines. Currently, the engines include DESeq2, edgeR, edgeRD and Voom. Among them, DESeq2, edgeR and Voom estimate dispersion across all RNA- and Ribo-seq samples, while edgeRD estimates dispersion for RNA- and Ribo-seq data separately.

Usage

riborex(rnaCntTable, riboCntTable, rnaCond, riboCond, engine = "DESeq2", contrast = NULL, minMeanCount = 1)

Arguments

rnaCntTable	a matrix or data frame of raw read counts summarized from RNA-seq data. The rows correspond to genes and columns correspond to samples.
riboCntTable	a matrix or data frame of raw read counts summarized from Ribo-seq data. The rows correspond to genes and columns correspond to samples. It should have the same set of genes as the rnaCntTable.
rnaCond	a vector for single factor experiment or a data frame for multi-factor experiment to label the treatment applied on samples. The order of samples should be the same as that in rnaCntTable.
riboCond	a vector for single factor experiment or a data frame for multi-factor experiment to label the treatment applied on samples. The order of samples should be the same as that in riboCntTable. The set of factors and the order of them should be the same as rnaCond.
engine	A string to specify the name of engine to be used. You can choose "DESeq2", "edgeR", "edgeRD" or "Voom". Among them, DESeq2, edgeR and Voom estimate dispersion across all RNA- and Ribo-seq samples, while edgeRD estimates dispersion for RNA- and Ribo-seq data separately.
contrast	a vector to specify the comparison to extract the results.
minMeanCount	The criterion for filtering low read count. The genes whose mean read count across all RNA- and Ribo-seq samples less than minMeanCount will be omitted.

Author(s)

Wenzheng Li <[e-mail]>

Examples

  ### load the data
  data(riborexdata)
  ### get rna-seq read count table
  rna <- riborexdata$rna
  ### get ribo-seq read count table
  ribo <- riborexdata$ribo
  ### prepare rna-seq condtions
  rnacond <- c("control", "control", "treated", "treated")
  ### prepare ribo-seq condtions
  ribocond <- c("control", "control", "treated", "treated")
  ### run riborex with default engine "DESeq2"
  res.deseq2 <- riborex(rna, ribo, rnacond, ribocond)
  ### run riborex with engine "edgeR"
  res.edgeR <- riborex(rna, ribo, rnacond, ribocond, "edgeR")
  ### run riborex with engine "edgeRD"
  res.edgeRD <- riborex(rna, ribo, rnacond, ribocond, "edgeRD")
  ### run riborex with engine "Voom"
  res.voom <- riborex(rna, ribo, rnacond, ribocond, "Voom")

smithlabcode/riborex documentation built on April 12, 2021, 11:46 a.m.