PathwaytValues {pathwayPCA} | R Documentation

## Calculate pathway-specific Student's t-scores from a null distribution or the true distribution for supervised PCA

### Description

If we sample from the null, distribution, first parametrically resample the response vector before model analysis (f we calculate Student t statistics from the true distribution instead, the response matrix is untouched). 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 values of the parametrically resampled response (for the null distribution) or the response itself (for the true distribution).

### Usage

    PathwaytValues(
      pathway_vec,
      geneArray_df,
      response_mat,
      responseType = c("survival", "regression", "categorical"),
      control = FALSE,
      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 factor 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. You can create a factor matrix of a factor `a` with the command `dim(a) <- c(k, 1)`, where `k = length(a)`.
`responseType` | A character string. Options are `"survival"`, `"regression"`, and `"categorical"`.
`control` | Should the responses be parametrically resampled to generate a control distribution? Defaults to `FALSE`.
`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.

If `control = TRUE`, the `RandomControlSample` suite of functions first parametrically bootstrapps the response. This control response will be used to contrstruct a null distribution against which to compare the results calculated with the original response values.

### Value

If `control = TRUE`, 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).

If `control = TRUE`, the same matrix as above is contained as the `tscor` element of a list (the first element). The other list elements are `PCs_mat` (the matrix of PCs) and `loadings` (the matrix of -Ome loadings corresponding to the PCs).

### See Also

`pathway_tScores`; `pathway_tControl`; `RandomControlSample`; `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
      )

      PathwaytValues(
        pathway_vec = asthmaGenes_char,
        geneArray_df = t(getAssay(colon_OmicsSurv)),
        response_mat = resp_mat,
        responseType = "survival"
      )

      PathwaytValues(
        pathway_vec = asthmaGenes_char,
        geneArray_df = t(getAssay(colon_OmicsSurv)),
        response_mat = resp_mat,
        responseType = "survival",
        control = TRUE
      )

    ## End(Not run)

[Package pathwayPCA version 1.8.0 Index]