bioadaptest {adaptest} | R Documentation

## Data Adaptive Multiple Testing for Computational Biology

### Description

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.

### Usage

    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)

### Arguments

`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.

### Value

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`.

### Examples

    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)

[Package adaptest version 1.0.0 Index]