plot read coverages

Plot the average read coverages for each length bin or a perticular isoform

Usage

plot_coverage(
  x,
  quantiles = c(0, 0.2375, 0.475, 0.7125, 0.95, 1),
  length_bins = c(0, 1, 2, 5, 10, Inf),
  weight_fn = weight_transcripts,
  filter_fn,
  detailed = FALSE
)

Arguments

x,

path to the BAM file (aligning reads to the transcriptome), or the (GenomicAlignments::readGAlignments) parsed GAlignments object, or the tibble returned by get_coverage, or the filtered tibble returned by filter_coverage.

quantiles

numeric vector to specify the quantiles to bin the transcripts lengths by if length_bins is missing. The length bins will be determined such that the read counts are distributed acording to the quantiles.

length_bins,

numeric vector to specify the sizes to bin the transcripts by

weight_fn

function to calculate the weights for the transcripts. The function should take a numeric vector of read counts and return a numeric vector of weights. The default function is weight_transcripts, you can change its default parameters by passing an anonymous function like function(x) weight_transcripts(x, type = 'equal').

filter_fn

Optional filter function to filter the transcripts before plotting. See the filter_fn parameter in filter_coverage for more details. Providing a filter fucntion here is the same as providing it in filter_coverage and then passing the result to this function.

detailed

logical, if TRUE, also plot the top 10 transcripts with the highest read counts for each length bin.

Value

a ggplot2 object of the coverage plot(s)

Examples

ppl <- example_pipeline("BulkPipeline")
#> Writing configuration parameters to:  /tmp/RtmpA5lNG6/file329037be016d/config_file_12944.json 
#> Configured steps: 
#> 	genome_alignment: TRUE
#> 	isoform_identification: TRUE
#> 	read_realignment: TRUE
#> 	transcript_quantification: TRUE
#> samtools not found, will use Rsamtools package instead
steps(ppl)["isoform_identification"] <- FALSE
ppl <- run_step(ppl, "read_realignment")
#> ── Running step: read_realignment @ Mon Jun 23 02:42:02 2025 ───────────────────
#> Using reference annotation for transcriptome assembly.
#> Import genomic features from the file as a GRanges object ... 
#> OK
#> Prepare the 'metadata' data frame ... 
#> OK
#> Make the TxDb object ... 
#> Warning: The "phase" metadata column contains non-NA values for features of type
#>   stop_codon. This information was ignored.
#> Warning: genome version information is not available for this TxDb object
#> OK
#> Realigning sample sample1 -> /tmp/RtmpA5lNG6/file329037be016d/sample1_realign2transcript.bam
#> Warning: samtools not found, using Rsamtools instead, this could be slower and might fail for large BAM files.
#> Skipped sorting BAM files.
#> Realigning sample sample2 -> /tmp/RtmpA5lNG6/file329037be016d/sample2_realign2transcript.bam
#> Warning: samtools not found, using Rsamtools instead, this could be slower and might fail for large BAM files.
#> Skipped sorting BAM files.
#> Realigning sample sample3 -> /tmp/RtmpA5lNG6/file329037be016d/sample3_realign2transcript.bam
#> Warning: samtools not found, using Rsamtools instead, this could be slower and might fail for large BAM files.
#> Skipped sorting BAM files.
# Plot the coverages directly from the BAM file
plot_coverage(ppl@transcriptome_bam[[1]])
#> Using quantiles to bin transcripts.
#> The number of transcripts is less than the inflection index, returning equal weights for the current bin.


# Get the coverage information first
coverage <- get_coverage(ppl@transcriptome_bam[[1]]) |>
  dplyr::filter(read_counts > 2) |> # Filter out transcripts with read counts < 3
  filter_coverage(filter_fn = convolution_filter) # Filter out transcripts with sharp drops / rises
#> 1 transcripts found in the BAM file.
#> 0(0%) transcripts failed the filter.
#> Failed transcripts account for 0 reads, out of 83(0%) reads in total.
# Plot the filtered coverages
plot_coverage(coverage, detailed = TRUE)
#> Using quantiles to bin transcripts.
#> The number of transcripts is less than the inflection index, returning equal weights for the current bin.

# filtering function can also be passed directly to plot_coverage
plot_coverage(ppl@transcriptome_bam[[1]], filter_fn = convolution_filter)
#> 1 transcripts found in the BAM file.
#> 0(0%) transcripts failed the filter.
#> Failed transcripts account for 0 reads, out of 83(0%) reads in total.
#> Using quantiles to bin transcripts.
#> The number of transcripts is less than the inflection index, returning equal weights for the current bin.