test_DTU {BANDITS} | R Documentation

## Perform differential splicing

### Description

`test_DTU` performs differential splicing, via differential transcript usage (DTU), between 2 or more groups. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts. Warning: the samples in samples_design must have the same order as those in the 'path_to_eq_classes' parameter of the `create_data` function.

### Usage

    test_DTU(BANDITS_data, precision = NULL, R = 10^4, burn_in = 2 *
      10^3, samples_design, group_col_name = "group", n_cores = 1,
      gene_to_transcript, theshold_pval = 0.1)

### Arguments

`BANDITS_data` | a 'BANDITS_data' object.
`precision` | a vector with the mean and standard deviation of the log-precision parameter.
`R` | the number of iterations for the MCMC algorithm (after the burn-in). Min 10^4. Albeit no difference was observed in simulation studies when increasing 'R' above 10^4, we encourage users to possibly use higher values of R (e.g., 2*10^4), if the computational time allows it, particularly for comparisons between 3 or more groups.
`burn_in` | the length of the burn-in to be discarded (before convergence is reached). Min 2*10^3. Albeit no difference was observed in simulation studies when increasing 'burn_in' above 2*10^3, we encourage users to possibly use higher values of R (e.g., double) if the computational time allows it.
`samples_design` | a `data.frame` indicating the design of the experiment with one row for each sample: samples_design must contain a column with the sample id and one with the group id. Warning: the samples in samples_design must have the same order as those in the 'path_to_eq_classes' parameter of the `create_data` function.
`group_col_name` | the name of the column of 'samples_design' containing the group id. By default group_col_name = "group".
`n_cores` | the number of cores to parallelize the tasks on.
`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.
`theshold_pval` | is a threshold between 0 and 1; when running `test_DTU`, if the p.value of a gene is < theshold_pval, a second (independent) MCMC chain is run and the p.value is re-computed on the aggregation of the two chains. By defauls theshold_pval = 0.1, while theshold_pval = 1 corresponds to running all chains twice, and theshold_pval = 0 means all chains will only run once.

### Value

A `BANDITS_test` object.

### Author(s)

Simone Tiberi simone.tiberi@uzh.ch

### See Also

`create_data`, `BANDITS_data`, `BANDITS_test`

### Examples

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

    # We define the design of the study
    samples_design = data.frame(sample_id = paste0("sample", seq_len(4)),
                                group = c("A", "A", "B", "B"))

    # load the pre-computed data:
    data("input_data", package = "BANDITS")
    input_data

    # Filter lowly abundant genes:
    input_data = filter_genes(input_data, min_counts_per_gene = 20)

    # load the pre-computed precision estimates:
    data(precision, package = "BANDITS")

    ## Test for DTU
    set.seed(61217)
    results = test_DTU(BANDITS_data = input_data,
                       precision = precision$prior,
                       samples_design = samples_design,
                       R = 10^4, burn_in = 2*10^3, n_cores = 2,
                       gene_to_transcript = gene_tr_id)
    results

[Package BANDITS version 1.8.0 Index]