pathway_tControl.Rd
Parametrically resample the response vector before model
analysis. Then extract principal components (PCs) from the gene pathway,
and return the test statistics associated with the first numPCs
principal components at a set of threshold values based on the resampled
values of the response.
pathway_tControl( pathway_vec, geneArray_df, response_mat, responseType = c("survival", "regression", "categorical"), n.threshold = 20, numPCs = 1, min.features = 3 )
pathway_vec | A character vector of the measured -Omes in the chosen gene pathway. These should match a subset of the rownames of the gene array. |
---|---|
geneArray_df | A "tall" pathway data frame (\(p \times N\)). Each subject or tissue sample is a column, and the rows are the -Ome measurements for that sample. |
response_mat | A response matrix corresponding to |
responseType | A character string. Options are |
n.threshold | The number of bins into which to split the feature scores
in the |
numPCs | The number of PCs to extract from the pathway. |
min.features | What is the smallest number of genes allowed in each pathway? This argument must be kept constant across all calls to this function which use the same pathway list. Defaults to 3. |
A matrix with numPCs
rows and n.threshold
columns.
The matrix values are model \(t\)-statisics for each PC included (rows)
at each threshold level (columns).
This is a wrapper function to call superpc.train
and superpc.st
after response parametric bootstrapping with
the RandomControlSample
suite of functions. This response
sampling will act as a null distribution against which to compare
the results from the pathway_tScores
function.
This wrapper is designed to facilitate apply calls (in parallel or
serially) of these two functions over a list of gene pathways. When
numPCs
is equal to 1, we recommend using a simplify-style apply
variant, such as sapply
(shown in lapply
) or
parSapply
(shown in clusterApply
), then
transposing the resulting matrix.
# DO NOT CALL THIS FUNCTION DIRECTLY. # Use SuperPCA_pVals() instead if (FALSE) { data("colon_pathwayCollection") data("colonSurv_df") colon_OmicsSurv <- CreateOmics( assayData_df = colonSurv_df[, -(2:3)], pathwayCollection_ls = colon_pathwayCollection, response = colonSurv_df[, 1:3], respType = "surv" ) asthmaGenes_char <- getTrimPathwayCollection(colon_OmicsSurv)[["KEGG_ASTHMA"]]$IDs resp_mat <- matrix( c(getEventTime(colon_OmicsSurv), getEvent(colon_OmicsSurv)), ncol = 2 ) pathway_tControl( pathway_vec = asthmaGenes_char, geneArray_df = t(getAssay(colon_OmicsSurv)), response_mat = resp_mat, responseType = "survival" ) }