iasva: A function for iteratively adjusted surrogate variable...

Description Usage Arguments Value Examples

View source: R/iasva.R

Description

The iterative procedure of IA-SVA is implemented in this function (iasva). iasva() function iteratively runs iasva_unit() function to identify a hidden factor for unwanted variation while accounting for all known factors and test the significance of its contribution on the unmodeled variation in the data. If the test statistic of detected factor is significant, iasva() includes the factor as a known variable in the next iteration to find further hidden factors.

Usage

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iasva(Y, X, intercept = TRUE, num.sv = NULL, permute = TRUE,
  num.p = 100, sig.cutoff = 0.05, threads = 1, num.sv.permtest = NULL,
  tol = 1e-10, verbose = FALSE)

Arguments

Y

A SummarizedExperiment class containing read counts where rows represent genes and columns represent samples.

X

A model matrix of known variables including the primary variables of interest.

intercept

If intercept = FALSE, the linear intercept is not included in the model.

num.sv

number of surrogate variables to estimate.

permute

If permute = TRUE, a permutation test (Buja and Eyuboglu 1992, Leek and Storey 2008) is conducted to assess the significance of the putative hidden factor.

num.p

number of permutations to be used to calculate the permuation test p-value.

sig.cutoff

significance threshold for the permutation test

threads

number of cores to be used in permutation test.

num.sv.permtest

num of top singular values to be used in computing the permutation test statistic. If num.sv.permtest = NULL, all singular values are used.

tol

stopping tolerance for the augmented implicitly restarted Lanczos bidiagonalization algorithm

verbose

If verbose=TRUE, the function outputs detailed messages.

Value

sv matrix of estimated surrogate variables, one column for each surrogate variable.

pc.stat.obs vector of PC test statistic values, one value for each surrogate variable.

pval vector of permuation p-values, one value for each surrogate variable.

n.sv number of significant/obtained surrogate variables.

Examples

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counts_file <- system.file("extdata", "iasva_counts_test.Rds",
 package = "iasva")
counts <- readRDS(counts_file)
anns_file <- system.file("extdata", "iasva_anns_test.Rds",
 package = "iasva")
 anns <- readRDS(anns_file)
Geo_Lib_Size <- colSums(log(counts + 1))
Patient_ID <- anns$Patient_ID
mod <- model.matrix(~Patient_ID + Geo_Lib_Size)
summ_exp <- SummarizedExperiment::SummarizedExperiment(assays = counts)
iasva.res<- iasva(summ_exp, mod[, -1],verbose = FALSE, 
 permute = FALSE, num.sv = 5)

UcarLab/iasva documentation built on Feb. 3, 2020, 8:28 a.m.