MultiGroupPower: Optimal sample size estimation for multiple group...

In ConesaLab/MultiPower: Estimation of sample size in multi-omic experiments

Optimal sample size estimation for multiple group comparisons.

Description

MultiGroupPower estimates the optimal sample size for a multi-omic experiment when pilot multi-omic data sets are available to estimate the parameters required to compute power and there are multiple groups to be compared.

Usage

MultiGroupPower(data, groups, type, comparisons = NULL, omicPower = 0.6, averagePower = 0.85,
                fdr = 0.05, cost = 1, equalSize = TRUE, max.size = 200, omicCol = NULL,
                powerPlots = FALSE, summaryPlot = TRUE)

Arguments

data	List with as many elements as omic data types. The names of the omics should be the names of the list. Each element in this list must be a raw count data matrix, and in this case MultiGroupPower will take into account the library sizes to estimate power; a normally distributed data matrix which must have been already pre-processed and normalized; or a binary data matrix (with 0/1 or TRUE/FALSE values). In any case, for each one of these matrices, rows must correspond to omic features (genes, methylation sites, ChIP-seq peaks, etc.) and columns to observations (biological samples, patients, etc.).
groups	List with as many elements as omic data types. The names of the omics should be the names of the list. Each element in this list must be a vector with length equal to the number of observations for that omic in data argument. Each element of this vector must indicate the experimental group where each observation belong.
type	Vector with length equal to the number of omic data types. Each element of this vector must be a 1, 2 or 3 to indicate whether the omic data are count data (1), continuous data approximately following a normal distribution (2) or binary data (3).
comparisons	Pairwise comparisons to be done between groups. If NULL (default option), the function will generate all the possible comparisons between the groups that are available for all omics. If users wish to indicate the comparisons to be done, they must provide a matrix with two rows and as many columns as comparisons. Each column will be then a two-element vector with the two groups to be compared. An easy way to generate this matrix is using the combn() function that returns a matrix with all the possible comparisons. Users can then remove the columns of the comparisons that are not interesting for them.
omicPower	The minimum power that must be achieved for each omic. It must be a vector with length equal to the number of omics. If it is a single number, this same number will be used for all the omics. By default, omicPower = 0.6.
averagePower	The minimum average power that must be globally achieved. By default, averagePower = 0.85.
fdr	False Discovery Rate level to be used. It is the significance level after multiple testing correction. By default, fdr = 0.05.
cost	The cost to generate a replicate (a sample) for each omic. It must be a vector with length equal to the number of omics. If it is a single number, this same number will be used for all the omics. This argument will only be used when a different sample size per omic is allowed. By default, cost = 1 (which means that all the omics will be assumed to have the same cost).
equalSize	If TRUE (default), the same optimal sample size will be estimated for all the omics. If FALSE, omics are allowed to have different sample sizes.
max.size	Maximum allowed sample size. By default, max.size = 200.
omicCol	The color that will be used to plot each omic. It must be a vector with length equal to the number of omics. If it is NULL (default), default colors are used.
powerPlots	If TRUE (FALSE is the default), power plots will be generated for each individual comparison as in MultiPower function.
summaryPlot	If TRUE (default), summary plots for sample size and power will be generated including the results for all comparisons and the global result, that is, the maximum sample size for all comparisons ("optimal" sample size) and the corresponding statistical power for each omic.

Value

When applying MultiGroupPower, the result is a list containing as many elements as omic data types and an additional element containing the global summary (GlobalSummary) of the results. The elements corresponding to omic data types are lists with the following elements:

parameters	List with as many elements as omic data types. For each omic, each element of the list is another list containing the different parameters used to compute power, estimated from the pilot data.
optimalSampleSize	List containing the following elements: n0 (sample size to achieve the minimum omic power, omicPower, for each omic), n (optimal sample size), finalPower (power at the optimal sample size for each omic), fdr (see fdr argument), omicPower (see omicPower argument), averagePower (see averagePower argument), and cost (see cost argument).
summary	Table summarizing MultiPower results.
data2plot	Data generated to create the power plots that are also returned by the function.

The GlobalSummary element is a summary table very similar to the summary table described above.

Author(s)

Sonia Tarazona; David Gomez-Cabrero

ConesaLab/MultiPower documentation built on April 16, 2023, 11:39 a.m.