iasva: A function for iteratively adjusted surrogate variable...
In UcarLab/iasva: Iteratively Adjusted Surrogate Variable Analysis

Description Usage Arguments Value Examples

View source: R/iasva.R

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.

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
)

`Y`	A SummarizedExperiment or SingleCellExperiment 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.

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.

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)