GSAR_boot: Perform differential coexpression analysis on original and...

In hui-sheen/MetaGSCA: Meta-analysis of Gene Set differential Co-expression Analysis

Perform differential coexpression analysis on original and bootstrap datasets

Description

Differential coexpression analysis is conducted on one original dataset and multiple bootstrap datasets.

1. Genes/rows in original data failing STD check are simply dropped. Genes/rows in bootstrap data failing STD check are simply dropped, we did not bother trying a second bootstrap for presence of certain minimal STD genes. 2. When original dataset does not contain sufficient genes for running GSNCA on one particular gene set, warning will be shown as GSET: Results NOT Captured!!! The output list simply does not contain component for that GSET.

Usage

GSAR_boot(
  R,
  gsets,
  object,
  group,
  nperm = 100,
  cor.method = "pearson",
  max.skip = 50,
  min.sd = 0.001,
  minGsize = 3
)

Arguments

R	number of bootstrap times.
gsets	list of multiple gene sets.
object	gene expression matrix covering two groups. Row names are gene symbols.
group	original groupping of samples, vector of 1's and 2's.
nperm	times of sample indix permutation, necessitated by GSNCA
cor.method	correlation method
max.skip	maximum number of repeated permutation/bootstrap times to avoid zero STD
min.sd	a valid data matrix per group must have at least this much per-feature STD
minGsize	considered gene set must have this minimum size after overlaying with gene expression matrix.

Value

list of GSARboot_format() results for multiple gene sets. Component has multiple elements plus a dsetRow which cover all ultimate output

hui-sheen/MetaGSCA documentation built on April 9, 2022, 7:24 p.m.