Description Usage Arguments Details Value
Reads in bam files and creates a count matrix based on a gtf file.
1 2 | read_bam_to_countmatrix(sampleTable, gtffile, projectfolder = getwd(),
outPrefix, singleEnd = FALSE, ignore.strand = TRUE, fragments = TRUE)
|
sampleTable |
Sample Table. See Vignette for instructions on how to build this data frame. |
gtffile |
GTF file. |
projectfolder |
File path where to save the output to. Defaults to working directory. Here, it saves the output to a subfolder called "Networks". |
outPrefix |
Prefix added to output name. |
singleEnd |
A logical indicating if reads are single or paired-end. In Bioconductor > 2.12 it is not necessary to sort paired-end BAM files by qname. When counting with summarizeOverlaps, setting singleEnd=FALSE will trigger paired-end reading and counting. It is fine to also set asMates=TRUE in the BamFile but is not necessary when singleEnd=FALSE. |
ignore.strand |
A logical indicating if strand should be considered when matching. |
fragments |
A logical; applied to paired-end data only. fragments controls which function is used to read the data which subsequently affects which records are included in counting. When fragments=FALSE, data are read with readGAlignmentPairs and returned in a GAlignmentPairs class. This class only holds 'mated pairs' from opposite strands; same-strand pairs singletons, reads with unmapped pairs and other fragments are dropped. When fragments=TRUE, data are read with readGAlignmentsList and returned in a GAlignmentsList class. This class holds 'mated pairs' as well as same-strand pairs, singletons, reads with unmapped pairs and other fragments. Because more records are kept, generally counts will be higher when fragments=TRUE. The term 'mated pairs' refers to records paired with the algorithm described on the ?readGAlignmentsList man page. |
So far, it is only implemented for obtaining read counts for genes based on summarized counts of reads overlapping all exons of each gene.
Writes the count matrix as .txt to file and returns a DESeq data set, which can then be used further with DESeq2.
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