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Read the inferential uncertainty that Salmon writes

Source: R/uncertainty-io.R
inferential-io.Rd

Salmon quantifies how well each transcript abundance is determined by the reads, and writes that information next to quant.sf:

  • read_inferential_replicates() reads the Gibbs (--numGibbsSamples) or bootstrap (--numBootstraps) replicates from aux_info/bootstrap/ (the format is identical in Salmon 1.x and in the Rust rewrite, Salmon 2). It returns a list with one transcript x sample matrix of counts per replicate, the layout used by productive_fraction() and add_uncertainty().

  • equivalence_support() reads the equivalence classes (--dumpEq, aux_info/eq_classes.txt.gz) and reports, for every transcript, how many of its fragments are compatible with no other transcript.

  • salmon_run_qc() reads aux_info/meta_info.json and reports the mapping rate, the library type, whether the optimiser converged and how many replicates were drawn.

  • inferential_replicates_table() and as_inferential_replicates() convert the list of replicates to and from a long table (one row per transcript, sample and replicate), which can be stored as Parquet.

Usage

read_inferential_replicates(
  paths,
  transcripts = NULL,
  n_draws = NULL,
  ignore_after_bar = TRUE
)

equivalence_support(paths, transcripts = NULL, ignore_after_bar = TRUE)

salmon_run_qc(paths, min_percent_mapped = 50)

inferential_replicates_table(reps)

as_inferential_replicates(table, samp_type = "gibbs")

Arguments

paths

Character vector of Salmon output directories (containing quant.sf) or paths to quant.sf files. Names, when present, are the sample IDs; otherwise the directory name is used.

transcripts

Optional transcript IDs to keep. Reading every transcript of a human annotation for many samples takes gigabytes of memory, so add_uncertainty() only reads the expressed ones.

n_draws

Number of replicates to keep (the first ones). NULL keeps all of them.

ignore_after_bar

Logical. Keep only the part of transcript names before the first | (GENCODE FASTA headers used as Salmon references).

min_percent_mapped

Samples below this mapping rate (percent) get the low_mapping_rate flag in salmon_run_qc().

reps

A list of transcript x sample count matrices, one per replicate.

table

A long table with transcript_id, sample_id, draw and count columns, as returned by inferential_replicates_table().

samp_type

Replicate type recorded on the result, "gibbs" or "bootstrap".

Value

  • read_inferential_replicates(): a list of numeric matrices (transcripts by samples, counts) with the replicate type ("gibbs" or "bootstrap") in the samp_type attribute.

  • inferential_replicates_table(): a tibble with transcript_id, sample_id, draw and count. as_inferential_replicates() is its inverse.

  • equivalence_support(): a tibble with one row per transcript: transcript_id, n_classes (mean number of equivalence classes per sample), n_fragments and n_unique_fragments (summed over samples), unique_fraction (share of the fragments of the transcript that map to it alone) and n_samples.

  • salmon_run_qc(): a tibble with one row per sample.

Examples

quant_dirs <- list.files(
  system.file("extdata", "mini_quants", package = "isoformic"),
  full.names = TRUE
)
names(quant_dirs) <- sub("_quant$", "", basename(quant_dirs))
# The bundled quantifications were made without replicates
salmon_run_qc(quant_dirs)[, c("sample_id", "percent_mapped", "qc_flag")]
#> # A tibble: 6 × 3
#>   sample_id   percent_mapped qc_flag      
#>   <chr>                <dbl> <chr>        
#> 1 SRR11498039            100 no_replicates
#> 2 SRR11498040            100 no_replicates
#> 3 SRR11498041            100 no_replicates
#> 4 SRR11498079            100 no_replicates
#> 5 SRR11498080            100 no_replicates
#> 6 SRR11498081            100 no_replicates