prior_precision {BANDITS} | R Documentation |
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.
prior_precision(gene_to_transcript, transcript_counts, n_cores = 1, transcripts_to_keep = NULL)
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 |
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.
Simone Tiberi simone.tiberi@uzh.ch
# 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)