TabulatepValues {pathwayPCA} | R Documentation |
Adjust the pathway p-values, then return a data frame of the relevant pathway information, sorted by adjusted significance.
TabulatepValues(pVals_vec, genesets_ls, adjust = TRUE, proc_vec = c("BH", "Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BY", "ABH", "TSBH"), ...)
pVals_vec |
A named vector of permutation p-values returned by the
|
genesets_ls |
A list of known gene pathways, trimmed to match the given
assay data by the
|
adjust |
Should you adjust the p-values for multiple comparisons? Defaults to TRUE. |
proc_vec |
Character vector of procedures. The returned data frame will
be sorted in ascending order by the first procedure in this vector, with
ties broken by the unadjusted p-value. If only one procedure is
selected, then it is necessarily the first procedure. Defaults to
|
... |
Additional arguments to pass to the |
This is a wrapper function for the ControlFDR
function. The number of p-values passed to the pVals_vec
argument must equal the number of pathways and set size values in
the genesets_ls
argument. If you trimmed a pathway from p-
value calculation, then pad this missing value with an NA
.
A data frame with columns
pathways
: The names of the pathways in the Omics*
object (stored in object@trimPathwayCollection$pathways
).
n_tested
: The number of genes in each pathway after being
trimmed to match the assay. Given in the n_tested
element of the
trimmed pathway collection.
terms
: The pathway title, as stored in the
object@trimPathwayCollection$TERMS
object.
description
: The pathway description, if it is stored in
the object@trimPathwayCollection$description
object.
rawp
: The unadjusted p-values of each pathway.
...
: Additional columns as specified through the
adjustment
argument.
The data frame will be sorted in ascending order by the method specified
first in the adjustment
argument. If adjustpValues = FALSE
,
then the data frame will be sorted by the raw p-values. If you have
the suggested tidyverse
package suite loaded, then this data frame
will print as a tibble
. Otherwise, it will stay a
simple data frame.
# DO NOT CALL THIS FUNCTION DIRECTLY. # Call this function through AESPCA_pVals() or SuperPCA_pVals() instead. ## Not run: ### Load the Example Data ### data("colonSurv_df") data("colon_pathwayCollection") ### Create an OmicsSurv Object ### colon_Omics <- CreateOmics( assayData_df = colonSurv_df[, -(2:3)], pathwayCollection_ls = colon_pathwayCollection, response = colonSurv_df[, 1:3], respType = "surv" ) ### Extract Pathway PCs and Loadings ### colonPCs_ls <- ExtractAESPCs( object = colon_Omics, parallel = TRUE, numCores = 2 ) ### Pathway p-Values ### pVals <- PermTestSurv( OmicsSurv = colon_Omics, pathwayPCs_ls = colonPCs_ls$PCs, parallel = TRUE, numCores = 2 ) ### Create Table of p-Values ### trimmed_PC <- getTrimPathwayCollection(colon_Omics) TabulatepValues( pVals_vec = pVals, genesets_ls = trimmed_PC ) ## End(Not run)