View source: R/geseca-multilevel.R
geseca | R Documentation |
This function is based on the adaptive multilevel splitting Monte Carlo approach and allows to estimate arbitrarily small P-values for the task of analyzing variance along a set of genes.
geseca(
pathways,
E,
minSize = 1,
maxSize = nrow(E) - 1,
center = TRUE,
scale = FALSE,
sampleSize = 101,
eps = 1e-50,
nproc = 0,
BPPARAM = NULL,
nPermSimple = 1000
)
pathways |
List of gene sets to check. |
E |
expression matrix, rows corresponds to genes, columns corresponds to samples. |
minSize |
Minimal size of a gene set to test. All pathways below the threshold are excluded. |
maxSize |
Maximal size of a gene set to test. All pathways above the threshold are excluded. |
center |
a logical value indicating whether the gene expression should be centered to have zero mean before the analysis takes place. The default is TRUE. The value is passed to scale. |
scale |
a logical value indicating whether the gene expression should be scaled to have unit variance before the analysis takes place. The default is FALSE. The value is passed to scale. |
sampleSize |
sample size for conditional sampling. |
eps |
This parameter sets the boundary for calculating P-values. |
nproc |
If not equal to zero sets BPPARAM to use nproc workers (default = 0). |
BPPARAM |
Parallelization parameter used in bplapply. |
nPermSimple |
Number of permutations in the simple geseca implementation for preliminary estimation of P-values. |
A table with GESECA results. Each row corresponds to a tested pathway. The columns are the following
pathway – name of the pathway as in 'names(pathways)';
pctVar – percent of explained variance along gene set;
pval – P-value that corresponds to the gene set score;
padj – a BH-adjusted p-value;
size – size of the pathway after removing genes not present in 'rownames(E)'.
data("exampleExpressionMatrix")
data("examplePathways")
gr <- geseca(examplePathways, exampleExpressionMatrix, minSize=15, maxSize=500)
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