Plot counts per feature

Generally, we expect similar count spreads for all genes between samples unless the library sizes or total RNA expression are different.

plotCountsPerFeature(object, ...)

# S4 method for bcbioRNASeq
plotCountsPerFeature(object,
  normalized = c("tmm", "tpm", "sf", "fpkm", "vst", "rlog", "rle"), ...)

plotCountDensity(object, ...)

Arguments

object	Object.
normalized	character(1) or logical(1). Normalization method to apply: FALSE: Raw counts. When using a tximport-compatible caller, these are length scaled by default (see countsFromAbundance argument). When using a featureCounts-compatible caller, these are integer. tximport caller-specific normalizations: "tpm": Transcripts per million. Additional gene-level-specific normalizations: TRUE / "sf": Size factor (i.e. library size) normalized counts. See DESeq2::sizeFactors() for details. "fpkm": Fragments per kilobase per million mapped fragments. Requires fpkm = TRUE in bcbioRNASeq() call and gene annotations in rowRanges() with defined width. See DESeq2::fpkm() for details. "vst": Variance-stabilizing transformation (log2). Requires vst = TRUE to be set during bcbioRNASeq() call. See DESeq2::varianceStabilizingTransformation for more information. "rlog": Regularized log transformation (log2). Requires rlog = TRUE to be set during bcbioRNASeq() call. See DESeq2::rlog() for details. "tmm": Trimmed mean of M-values. Calculated on the fly. See edgeR::calcNormFactors() for details. "rle": Relative log expression transformation. Calculated on the fly. See relativeLogExpression() for details. Note that logical(1) support only applies to counts(). Other functions in the package require character(1) and use match.arg() internally.
...	Passthrough to SummarizedExperiment method defined in acidplots. See acidplots::plotCountsPerFeature() for details.

Value

ggplot.

Note

Updated 2019-09-16.

Methods (by class)

• SingleCellExperiment: Applies aggregateCellsToSamples() calculation to summarize at sample level prior to plotting.
  Passes ... to SummarizedExperiment method.

Examples

data(bcb)
plotCountsPerFeature(bcb)
#> Using tmm counts.
#> Applying trimmed mean of M-values (TMM) normalization.
#> Filtered zero count rows and columns:
#>   - 90 / 100 rows (90.0%)
#>   - 6 / 6 columns (100%)
#> Applying 'log2(x + 1L)' transformation.