doMatch: Match the RNA-Seq and ChIP-Seq data on the gene level

Description

doMatch computes the number of reads (counts) fall into specific genomic region such as promoter or genebody for ChIP-Seq, and calculate the gene expression in counts, and then match the RNA-Seq and ChIP-Seq data on the gene level with the method of "weighetd mean" or "highest".

Usage

doMatch(rnaMeta, chipMeta, region, method, ensemblDataset,
  fragLength = 180, promoter.length = 5000)

Arguments

rnaMeta

metadata for RNA-Seq include column named "condition" indicates the experiment condition or cell type, and column named "files" indicates the paths of cprresponing abundance.tsv file that is returned from Kallisto.

chipMeta

metadata for ChIP-Seq include column of "mark" column indicates the markers of histone modifications, column of "condition" indicates the experiment condition or cell type, and "files" column indicates the paths and the file names of the aligned bam files.

region

region has to be specified as "promoter" or "genebody".

method

method has to be specified as "weighted.mean" or"highest" if region is set as "promoter".

ensemblDataset

Ensembl Dataset you want to use. To see the different datasets available within a biomaRt you can e.g. do: mart = useMart('ensembl'), followed by listDatasets(mart).

fragLength

extend reads toward the 3'-end to the average DNA fragment size obtained after DNA size selection.

promoter.length

the length of the promoter region.

Value

A list with the following three items.

res.rna a data frame contains RNA-Seq counts

res.chip a data frame contains ChIP-Seq counts

matched.data a dataframe contains matched RNA-Seq counts and ChIP-Seq counts.

Examples

Run this code

# NOT RUN {
data(test_rna_meta)
data(test_chip_meta)
# }
# NOT RUN {
for(i in test_rna_meta$SRR){
test_rna_meta$files <- system.file("extdata",paste0(i,".tsv"),
package = "intePareto")
}
for(i in test_chip_meta$SRR){
test_chip_meta$files <- system.file("extdata", paste0(i,".bam"),
package = "intePareto")
}
doMatch(rnaMeta = test_rna_meta,
chipMeta = test_chip_meta,
region = "promoter",
method = "weighted.mean",
ensemblDataset = "mmusculus_gene_ensembl")
# }

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