motifBindingNegativeBinomialCount: Model IP and Input count values with negative Binomal

In ManchesterBioinference/Motif2Site: Detect binding sites from motifs and ChIP-seq experiments, and compare binding sites across conditions

Description

Using edgeR TMM normalization and estimating dispersion as well as Adapting exact test function from edgeR to model IP vs Input counts.

To make this function memory effcient motifs into smaller sets and compute them seperately and combine them at the end.

Usage

motifBindingNegativeBinomialCount(
  countTableFile,
  replicateNumber,
  outputFile,
  currentDir
)

Arguments

countTableFile	Table of counts which contains all IP and Input value raw counts
replicateNumber	experiment replicate number
outputFile	The name of the output file generated by this function
currentDir	Directory for I/O operations

Value

A dataframe includes fold enrichment, pvalue, and normalized count values

ManchesterBioinference/Motif2Site documentation built on Feb. 13, 2022, 2:03 a.m.