AESPCA_pVals.Rd
Given a supervised OmicsPath
object (one of
OmicsSurv
, OmicsReg
, or OmicsCateg
), extract the
first \(k\) adaptive, elasticnet, sparse principal components (PCs)
from each pathwaysubset of the features in the Omics assay design
matrix, test their association with the response matrix, and return a
data frame of the adjusted \(p\)values for each pathway.
AESPCA_pVals( object, numPCs = 1, numReps = 0L, parallel = FALSE, numCores = NULL, asPCA = FALSE, adjustpValues = TRUE, adjustment = c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH", "TSBH"), ... ) # S4 method for OmicsPathway AESPCA_pVals( object, numPCs = 1, numReps = 1000, parallel = FALSE, numCores = NULL, asPCA = FALSE, adjustpValues = TRUE, adjustment = c("Bonferroni", "Holm", "Hochberg", "SidakSS", "SidakSD", "BH", "BY", "ABH", "TSBH"), ... )
object  An object of class 

numPCs  The number of PCs to extract from each pathway. Defaults to 1. 
numReps  How many permutations to estimate the \(p\)value? Defaults
to 0 (that is, to estimate the \(p\)value parametrically). If

parallel  Should the computation be completed in parallel? Defaults to

numCores  If 
asPCA  Should the computation return the eigenvectors and eigenvalues
instead of the adaptive, elasticnet, sparse principal components and their
corresponding loadings. Defaults to 
adjustpValues  Should you adjust the \(p\)values for multiple comparisons? Defaults to TRUE. 
adjustment  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. See the documentation
for the 
...  Dots for additional internal arguments. 
A results list with class aespcOut
. This list has three
components: a data frame of pathway details, pathway \(p\)values, and
potential adjustments to those values (pVals_df
); a list of the
first numPCs
score vectors for each pathway (PCs_ls
);
and a list of the first numPCs
feature loading vectors for each
pathway (loadings_ls
). The \(p\)value data frame has columns:
pathways
: The names of the pathways in the Omics*
object (given in object@trimPathwayCollection$pathways
.)
setsize
: The number of genes in each of the original
pathways (given in the object@trimPathwayCollection$setsize
object).
n_tested
: The number of genes in each of the trimmed
pathways (given in the object@trimPathwayCollection$n_tested
object).
terms
: The pathway description, as given in the
object@trimPathwayCollection$TERMS
object.
rawp
: The unadjusted \(p\)values of each pathway.
...
: Additional columns of adjusted \(p\)values 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 print as
a data frame.
This is a wrapper function for the ExtractAESPCs
,
PermTestSurv
, PermTestReg
, and
PermTestCateg
functions.
Please see our Quickstart Guide for this package: https://gabrielodom.github.io/pathwayPCA/articles/Supplement1Quickstart_Guide.html
### 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" )#> #>#>#>#>#> #>#>#> #>### Calculate Pathway pValues ### colonSurv_aespc < AESPCA_pVals( object = colon_Omics, numReps = 0, parallel = TRUE, numCores = 2, adjustpValues = TRUE, adjustment = c("Hoch", "SidakSD") )#>#>#>#>#>#>#>#>#>#>#>#>#>#>