parameters_are_good: Check input parameters.
In bartongroup/RATS: Relative Abundance of Transcripts

parameters_are_good

R Documentation

Check input parameters.

Description

Check input parameters.

Usage

parameters_are_good(
  annot,
  count_data_A,
  count_data_B,
  boot_data_A,
  boot_data_B,
  TARGET_COL,
  PARENT_COL,
  correction,
  testmode,
  scaling,
  threads,
  seed,
  p_thresh,
  abund_thresh,
  dprop_thresh,
  qboot,
  qbootnum,
  qrep_thresh,
  rboot,
  rrep_thresh,
  reckless,
  lean,
  verbose,
  use_sums
)

Arguments

`annot`	Annotation dataframe.
`count_data_A`	A dataframe of estimated counts.
`count_data_B`	A dataframe of estimated counts.
`boot_data_A`	A list of dataframes, one per sample, each with all the bootstrapped estimates for the sample.
`boot_data_B`	A list of dataframes, one per sample, each with all the bootstrapped estimates for the sample.
`TARGET_COL`	Name of transcript id column in annotation.
`PARENT_COL`	Name of gene id column in annotation.
`correction`	P-value correction method.
`testmode`	Which test to run.
`scaling`	Abundance scaling factor.
`threads`	Number of threads.
`seed`	Seed for random engine.
`p_thresh`	Significance level.
`abund_thresh`	Minimum transcript abundance per sample.
`dprop_thresh`	Minimum change in proportion.
`qboot`	Whether to bootstrap against quantifications.
`qbootnum`	Number of bootstrap iterations.
`qrep_thresh`	Confidence threshold.
`rboot`	Whether to bootstrap against samples.
`rrep_thresh`	Confidence threshold.
`reckless`	whether to ignore detected annotation discrepancies.
`lean`	whether to report descriptive statistics for Dprop and pval.
`verbose`	Verbose status.
`use_sums`	Whether to use sums or means.