pathway_tScores {pathwayPCA} | R Documentation

## Calculate pathway-specific Student's t-scores for supervised PCA

### Description

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.

### Usage

    pathway_tScores(pathway_vec, geneArray_df, response_mat,
      responseType = c("survival", "regression", "categorical"),
      n.threshold = 20, numPCs = 1, min.features = 3)

### Arguments

`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`. For `"regression"` and `"categorical"`, this will be an N \times 1 matrix of response values. For `"survival"`, this will be an N \times 2 matrix with event times in the first column and observed event indicator in the second.
`responseType` | A character string. Options are `"survival"`, `"regression"`, and `"categorical"`.
`n.threshold` | The number of bins into which to split the feature scores in the `fit` object returned internally by the `superpc.train` function.
`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.

### Details

This is a wrapper function to call `superpc.train` and `superpc.st`. 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.

### Value

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).

### See Also

`superpc.train`; `superpc.st`

### Examples

      # DO NOT CALL THIS FUNCTION DIRECTLY.
      # Use SuperPCA_pVals() instead

    ## Not run:
      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_tScores(
        pathway_vec = asthmaGenes_char,
        geneArray_df = t(getAssay(colon_OmicsSurv)),
        response_mat = resp_mat,
        responseType = "survival"
      )

    ## End(Not run)

[Package pathwayPCA version 1.2.0 Index]