Different scorings and groupings of a coverage representation.
a data.table containing at least columns (count, position), it is possible to have additionals: (genes, fraction, feature)
a character, one of (zscore, transcriptNormalized, mean, median, sum, log2sum, log10sum, sumLength, meanPos and frameSum, periodic, NULL). More info in details
logical TRUE, copy object, to avoid overwriting original object. Set to false to speed up, if original object is not needed.
Usually output of metaWindow or scaledWindowPositions is input in this function.
Content of coverage data.table: It must contain the count and position columns.
genes column: If you have multiple windows, the genes column must define which gene/transcript grouping the different counts belong to. If there is only a meta window or only 1 gene/transcript, then this column is not needed.
fraction column: If you have coverage of i.e RNA-seq and Ribo-seq, or TCP -seq of large and small subunite, divide into fractions. Like factor(RNA, RFP)
feature column: If gene group is subdivided into parts, like gene is transcripts, and feature column can be c(leader, cds, trailer) etc.
Given a data.table coverage of counts, add a scoring scheme.
per: the grouping given, if genes is defined,
group by per gene in default scoring.
zscore (count-windowMean)/windowSD per)
transcriptNormalized (sum(count / sum of counts per))
mean (mean(count per))
median (median(count per))
sum (count per)
log2sum (count per)
log10sum (count per)
sumLength (count per) / number of windows
meanPos (mean per position per gene) used in scaledWindowPositions
sumPos (sum per position per gene) used in scaledWindowPositions
frameSum (sum per frame per gene) used in ORFScore
frameSumPerL (sum per frame per read length)
frameSumPerLG (sum per frame per read length per gene)
fracPos (fraction of counts per position per gene)
periodic (Fourier transform periodicity of meta coverage per fraction)
NULL (no grouping, return input directly)
a data.table with new scores (size dependent on score used)
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