Plot counts

Plot counts

plotCounts(object, ...)

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

Arguments

object	Object.
genes	character. Gene identifiers.
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::plotCounts() for details.

Value

• style = "facet": ggplot grouped by sampleName, with ggplot2::facet_wrap() applied to panel the samples.

• style = "wide": ggplot in wide format, with genes on the x-axis.

Note

Updated 2019-09-18.

See also

DESeq2::plotCounts().

Examples

data(bcb)

g2s <- basejump::Gene2Symbol(bcb)
geneIDs <- head(g2s[["geneID"]])
print(geneIDs)
#> [1] "ENSMUSG00000000001" "ENSMUSG00000000003" "ENSMUSG00000000028"
#> [4] "ENSMUSG00000000049" "ENSMUSG00000000058" "ENSMUSG00000000078"
geneNames <- head(g2s[["geneName"]])
print(geneNames)
#> [1] "Gnai3" "Pbsn"  "Cdc45" "Apoh"  "Cav2"  "Klf6" 

plotCounts(bcb, genes = geneIDs, style = "facet")
#> Using tpm counts.
#> Applying 'log2(x + 1L)' transformation.
plotCounts(bcb, genes = geneNames, style = "wide")
#> Using tpm counts.
#> Applying 'log2(x + 1L)' transformation.