prior_precision: Infer an informative prior for the precision
In BANDITS: BANDITS: Bayesian ANalysis of DIfferenTial Splicing

Description Usage Arguments Value Author(s) See Also Examples

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.

1 2	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 `filter_transcripts`.

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

test_DTU, plot_precision

# 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)