run_diff: Run the differential analysis pipeline
In anilchalisey/parseR: Pipeline for Analysis of RNA-seq in R

Function to run the differential analysis portion of the pipeline. There are two possible starting points:

BAM filesIf starting with aligned BAM files, then set count = T. The pipeline will then begin with read counting before progressing to differential analysis.
Count filesIf starting with count files generated externally, then set count = F and specify the text file containing the matrix of counts. The pipeline will proceed directly to the differential analysis. The row names of the matrix should indicate the gene and the column name the library. It is important that column names match the baseline in the sample metadata file.

run_diff(threads = 1, experimentTitle = "", modules = "LED",
  p.value = 0.05, sample.path = NULL, contrast.column = NULL,
  block.column = NULL, contrast.levels = NULL, count = TRUE,
  count.file = NULL, featurecounts = "featureCounts", bamfiles = NULL,
  annotationFile = NULL, annotationFormat = NULL,
  requireBothEndsMapped = TRUE, excludeChimeric = TRUE, pairedEnd = TRUE,
  countMultiMapping = FALSE, multiFeatureReads = TRUE, ignoreDup = FALSE,
  outname = "counts.txt", cpm.cutoff = 1, design = NULL,
  contrast = NULL, norm.method = "TMM", adjust.method = "BH")

`threads`	Number of threads to utilise where parallel processing is possible.
`experimentTitle`	Name of the experiment.
`modules`	The differential expression analysis method to be used. May be any combination of 'L' (limma), 'E' (edgeR), and 'D' (DESeq2).
`p.value`	Value between 0 and 1 specifying the adjusted p.value threshold for significance.
`sample.path`	Full path to the sample metadata file.
`contrast.column`	Column in sample metadata file containing information about the contrasts of interest.
`block.column`	Column in sample metadata file containing information about blocking factors or batch effect.
`contrast.levels`	The order in which the contrasts should be evaluated.
`count`	Boolean (TRUE or FALSE) indicating whether to perform counting.
`count.file`	The path to the file containing the count matrix (if count = F). The first column should be the gene/feature names and the subsequent columns the counts of reads in each sample. The sample column names must match the names in the sample metadata table.
`featurecounts`	The path to featureCounts (if not in executable path).
`bamfiles`	Vector of full path to bam files to be counted (if count = T).
`annotationFile`	Path to annotation file. If NULL, the default hg19 genome is used.
`annotationFormat`	Specify the format of the annotation file. Acceptable formats include <e2><80><98>GTF<e2><80><99> and <e2><80><98>SAF<e2><80><99>. The in-built annotation is 'SAF'.
`requireBothEndsMapped`	Logical. If TRUE, only fragments that have both ends successfully aligned will be considered for summarization. This option should be used together with pairedEnd = TRUE.
`excludeChimeric`	Logical. If TRUE, the chimeric fragments (those fragments that have their two ends aligned to different chromosomes) will NOT be counted. This option should be used together with pairedEnd = TRUE.
`pairedEnd`	Logical. If TRUE, fragments (or templates) will be counted instead of reads. This option is only applicable for paired-end reads.
`countMultiMapping`	If TRUE, multi-mapping reads/fragments will be counted.
`multiFeatureReads`	Reads/fragments overlapping with more than one meta-feature/feature will be counted more than once. Note that when performing meta-feature level summarization, a read (or fragment) will still be counted once if it overlaps with multiple features within the same meta-feature (as long as it does not overlap with other metafeatures).
`ignoreDup`	Logical. If TRUE, reads that were marked as duplicates will be ignored. In paired end data, the entire read pair will be ignored if at least one end is found to be a duplicate read.
`outname`	Character string. Name of the output file. The output file contains the number of reads assigned to each meta-feature or feature.
`cpm.cutoff`	The counts-per-million threshold for filtering counts.
`design`	Custom design matrix that may be used in place of default matrix created by pipeline. Only used for edgeR and limma analyses.
`contrast`	Custom contrast matrix that may be used in place of default matrix created by pipeline. Only used for edgeR and limma analyses.
`norm.method`	Normalisation method used by edgeR/limma analyses. May be "TMM", "RLE", "upperquartile" or "none".
`adjust.method`	Method to be used for adjustment of nominal p-values. May be one of "BH", "bonferroni", "holm", "hochberg", "hommel", "BY".