bioadaptest: Data Adaptive Multiple Testing for Computational Biology
In adaptest: Data-Adaptive Statistics for High-Dimensional Multiple Testing

Description Usage Arguments Value Examples

A thin wrapper that implements the main data-adaptive multiple hypothesis testing strategy for data structures commonly found in computational biology experiments, using the popular SummarizedExperiment container class.

bioadaptest(data_in, var_int, cntrl_set = NULL, n_top = 25,
  n_fold = 10, parameter_wrapper = rank_DE,
  learning_library = c("SL.mean", "SL.glm"), absolute = FALSE,
  negative = FALSE, p_cutoff = 0.05, q_cutoff = 0.05)

`data_in`	An object of class `SummarizedExperiment`, a common container class for computational biology and bioinformatics. This object is used to construct the output object of class `adaptmle`.
`var_int`	A `numeric` vector of binary treatment assignment whose effect on the biological units is to be assessed. The data-adpative target parameter approach finds any biological sites strongly impacted by this quantity across the observed experimental units (subjects).
`cntrl_set`	A `matrix` of discrete variables representing baseline covariates that are controlled for in the estimation of the data-adaptive target parameter via targeted maximum likelihood estimation. If `NULL`, an identity vector is generated internally.
`n_top`	(integer vector) - value for the number of candidate covariates to generate using the data-adaptive estimation algorithm.
`n_fold`	(integer vector) - number of cross-validation folds.
`parameter_wrapper`	(function) - user-defined function that takes input (Y, A, W, absolute, negative) and outputs a (integer vector) containing ranks of biomarkers (outcome variables). For detail, please refer to the documentation for `rank_DE`.
`learning_library`	(character vector) - library of learning algorithms to be used in fitting the "Q" and "g" step of the standard TMLE procedure.
`absolute`	(logical) - whether or not to test for absolute effect size. If `FALSE`, test for directional effect. This overrides argument `negative`.
`negative`	(logical) - whether or not to test for negative effect size. If `FALSE` = test for positive effect size. This is effective only when `absolute = FALSE`.
`p_cutoff`	The minimum p-value required to evaluate a given biological unit (e.g., gene) as statistically significant.
`q_cutoff`	The minimum p-value required to evaluate a given biological unit (e.g., gene) as statistically significant after applying a correction for multiple hypothesis testing.

An object of class adaptmle, sub-classed from the popular container class SummarizedExperiment, containing information about the experiment being analyzed as well as results from applying the TMLE for the data-adaptive target parameter as produced by adpatest.

library(SummarizedExperiment)
library(airway)
set.seed(5678)
data(airway)
genes_sub <- order(sample(seq_len(100)))
air_reduced <- airway[genes_sub, ]
simple_air <- cbind(air_reduced, air_reduced)
dex_var = as.numeric(as.matrix(colData(simple_air))[, 3] - 1)
airway_out <- bioadaptest(data_in = simple_air,
                          var_int = dex_var,
                          cntrl_set = NULL,
                          n_top = 5,
                          n_fold = 2,
                          parameter_wrapper = rank_DE)