prior_precision {BANDITS} | R Documentation

## Infer an informative prior for the precision

### Description

`prior_precision` uses `DRIMSeq`'s pipeline to infer an informative prior for the precision parameter of the Dirichlet-Multinomial distribution. The function computes the genewise estimates for the precision via `DRIMSeq::dmPrecision`, and calculates the mean and standard deviation of the log-precision estimates.

### Usage

    prior_precision(gene_to_transcript, transcript_counts, n_cores = 1,
      transcripts_to_keep = NULL)

### Arguments

`gene_to_transcript` | a matrix or data.frame with a list of gene-to-transcript correspondances. The first column represents the gene id, while the second one contains the transcript id.
`transcript_counts` | a matrix or data.frame, with 1 column per sample and 1 row per transcript, containing the estimated abundances for each transcript in each sample.
`n_cores` | the number of cores to parallelize the tasks on.
`transcripts_to_keep` | a vector containing the list of transcripts to keep. Ideally, created via `filter_transcripts`.

### Value

A list with 2 objects containing:

- prior: a vector containing the mean and standard deviation of the log-precision, used to formulate an informative prior in `test_DTU`;

- genewise_log_precision: a numeric vector with the individual genewise estimates for the log-precision.

### Author(s)

Simone Tiberi simone.tiberi@uzh.ch

### See Also

`test_DTU`, `plot_precision`

### Examples

    # specify the directory of the internal data:
    data_dir = system.file("extdata", package = "BANDITS")

    # load gene_to_transcript matching:
    data("gene_tr_id", package = "BANDITS")

    # Load the transcript level estimated counts via tximport:
    library(tximport)
    quant_files = file.path(data_dir, "STAR-salmon", paste0("sample", seq_len(4)), "quant.sf")
    txi = tximport(files = quant_files, type = "salmon", txOut = TRUE)
    counts = txi$counts

    # Optional (recommended): transcript pre-filtering
    transcripts_to_keep = filter_transcripts(gene_to_transcript = gene_tr_id,
                                             transcript_counts = counts,
                                             min_transcript_proportion = 0.01,
                                             min_transcript_counts = 10,
                                             min_gene_counts = 20)

    # Infer an informative prior for the precision parameter
    # Use the same filtering criteria as in 'create_data', by choosing the same argument for 'transcripts_to_keep'.
    # If transcript pre-filtering is not performed, leave 'transcripts_to_keep' unspecified.
    set.seed(61217)
    precision = prior_precision(gene_to_transcript = gene_tr_id, transcript_counts = counts,
                                n_cores = 2, transcripts_to_keep = transcripts_to_keep)
    precision$prior
    head(precision$genewise_log_precision)

[Package BANDITS version 1.6.0 Index]