create_data: Create a 'BANDITS_data' object
In BANDITS: BANDITS: Bayesian ANalysis of DIfferenTial Splicing

Description Usage Arguments Value Author(s) See Also Examples

create_data imports the equivalence classes and create a 'BANDITS_data' object.

create_data(salmon_or_kallisto, gene_to_transcript,
  salmon_path_to_eq_classes = NULL, kallisto_equiv_classes = NULL,
  kallisto_equiv_counts = NULL, kallisto_counts = NULL, eff_len,
  n_cores = NULL, transcripts_to_keep = NULL,
  max_genes_per_group = 50)

`salmon_or_kallisto`	a character string indicating the input data: 'salmon' or 'kallisto'.
`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.
`salmon_path_to_eq_classes`	(for salmon input only) a vector of length equals to the number of samples: each element indicates the path to the equivalence classes of the respective sample (computed by salmon).
`kallisto_equiv_classes`	(for kallisto input only) a vector of length equals to the number of samples: each element indicates the path to the equivalence classes ('.ec' files) of the respective sample (computed by kallisto).
`kallisto_equiv_counts`	(for kallisto input only) a vector of length equals to the number of samples: each element indicates the path to the counts of the equivalence classes ('.tsv' files) of the respective sample (computed by kallisto).
`kallisto_counts`	(for kallisto input only) 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, computed by kallisto. The matrix must be unfiltered and the order or rows must be unchanged.
`eff_len`	a vector containing the effective length of transcripts; the vector names indicate the transcript ids. Ideally, created via `eff_len_compute`.
`n_cores`	the number of cores to parallelize the tasks on. It is highly suggested to use at least one core per sample (default if not specificied by the user).
`transcripts_to_keep`	a vector containing the list of transcripts to keep. Ideally, created via `filter_transcripts`.
`max_genes_per_group`	an integer number specifying the maximum number of genes that each group can contain. When equivalence classes contain transcripts from distinct genes, these genes are analyzed together. For computational reasons, 'max_genes_per_group' sets a limit to the number of genes that each group can contain.

A BANDITS_data object.

Simone Tiberi simone.tiberi@uzh.ch

eff_len_compute, filter_transcripts, filter_genes, BANDITS_data

# 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")

# Specify the directory of the transcript level estimated counts.
sample_names = paste0("sample", seq_len(4))
quant_files = file.path(data_dir, "STAR-salmon", sample_names, "quant.sf")

# Load the transcript level estimated counts via tximport:
library(tximport)
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)

# compute the Median estimated effective length for each transcript:
eff_len = eff_len_compute(x_eff_len = txi$length)

# specify the path to the equivalence classes:
equiv_classes_files = file.path(data_dir, "STAR-salmon", sample_names, "aux_info", "eq_classes.txt")

# create data from 'salmon' and filter internally lowly abundant transcripts:
input_data = create_data(salmon_or_kallisto = "salmon",
                         gene_to_transcript = gene_tr_id,
                         salmon_path_to_eq_classes = equiv_classes_files,
                         eff_len = eff_len, 
                         n_cores = 2,
                         transcripts_to_keep = transcripts_to_keep)
input_data

# create data from 'kallisto' and filter internally lowly abundant transcripts:
kallisto_equiv_classes = file.path(data_dir, "kallisto", sample_names, "pseudoalignments.ec")
kallisto_equiv_counts  = file.path(data_dir, "kallisto", sample_names, "pseudoalignments.tsv")

input_data_2 = create_data(salmon_or_kallisto = "kallisto",
                          gene_to_transcript = gene_tr_id,
                          kallisto_equiv_classes = kallisto_equiv_classes,
                          kallisto_equiv_counts = kallisto_equiv_counts,
                          kallisto_counts = counts,
                          eff_len = eff_len, n_cores = 2,
                          transcripts_to_keep = transcripts_to_keep)
input_data_2