calculateJCCScores: Calculate scaled coverages and JCC scores

In csoneson/jcc: Calculate JCC Scores

Calculate scaled coverages and JCC scores

Description

This function calculates the scaled predicted junction coverage for each junction, and the JCC score for each gene.

Usage

calculateJCCScores(junctionCovs, geneQuants, mmFracThreshold = 0.25)

Arguments

junctionCovs	A data.frame with observed and predicted junction counts
geneQuants	A data.frame with gene-level abundances.
mmFracThreshold	Scalar value in [0, 1] indicating the maximal fraction of multimapping reads a junction can have and still contribute to the score.

Value

A list with two elements:

junctionCovs:

A data.frame with predicted and observed junction coverages.

geneScores:

A data.frame with gene scores.

Author(s)

Charlotte Soneson

References

Soneson C, Love MI, Patro R, Hussain S, Malhotra D, Robinson MD: A junction coverage compatibility score to quantify the reliability of transcript abundance estimates and annotation catalogs. bioRxiv doi:10.1101/378539 (2018)

Examples

## Not run: 
gtf <- system.file("extdata/Homo_sapiens.GRCh38.90.chr22.gtf.gz",
                   package = "jcc")
bam <- system.file("extdata/reads.chr22.bam", package = "jcc")
biasMod <- fitAlpineBiasModel(gtf = gtf, bam = bam,
                              organism = "Homo_sapiens",
                              genome = Hsapiens, genomeVersion = "GRCh38",
                              version = 90, minLength = 230,
                              maxLength = 7000, minCount = 10,
                              maxCount = 10000, subsample = TRUE,
                              nbrSubsample = 30, seed = 1, minSize = NULL,
                              maxSize = 220, verbose = TRUE)
tx2gene <- readRDS(system.file("extdata/tx2gene.sub.rds", package = "jcc"))
predCovProfiles <- predictTxCoverage(biasModel = biasMod$biasModel,
                                     exonsByTx = biasMod$exonsByTx,
                                     bam = bam, tx2gene = tx2gene,
                                     genome = Hsapiens,
                                     genes = c("ENSG00000070371",
                                               "ENSG00000093010"),
                                     nCores = 1, verbose = TRUE)
txQuants <- readRDS(system.file("extdata/quant.sub.rds", package = "jcc"))
txsc <- scaleTxCoverages(txCoverageProfiles = predCovProfiles,
                         txQuants = txQuants, tx2gene = tx2gene,
                         strandSpecific = TRUE, methodName = "Salmon",
                         verbose = TRUE)
jcov <- read.delim(system.file("extdata/sub.SJ.out.tab", package = "jcc"),
                   header = FALSE, as.is = TRUE) %>%
 setNames(c("seqnames", "start", "end", "strand", "motif", "annot",
           "uniqreads", "mmreads", "maxoverhang")) %>%
 dplyr::mutate(strand = replace(strand, strand == 1, "+")) %>%
 dplyr::mutate(strand = replace(strand, strand == 2, "-")) %>%
 dplyr::select(seqnames, start, end, strand, uniqreads, mmreads) %>%
 dplyr::mutate(seqnames = as.character(seqnames))
combCov <- combineCoverages(junctionCounts = jcov,
                            junctionPredCovs = txsc$junctionPredCovs,
                            txQuants = txsc$txQuants)
jcc <- calculateJCCScores(junctionCovs = combCov$junctionCovs,
                          geneQuants = combCov$geneQuants)

## End(Not run)

csoneson/jcc documentation built on March 26, 2024, 1:24 a.m.