mysvd: Singular Value Decomposition wrapper for supervised PCA

Description Usage Arguments Details Value Examples

View source: R/superPC_modifiedSVD.R

Description

Center and compute the SVD of a matrix

Usage

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mysvd(mat, method = svd, n.components = NULL)

Arguments

mat

A matrix of data frame in "tall" format (p \times n).

method

What function should be used to extract the left- and right- singular vectors and singular values? Any function that returns the values as a list with components u, v, and d is appropriate. Defaults to svd.

n.components

How many singular values / vectors to return? Must be an integer less than min(p, n). Best performance increase is for values much less than min(p, n). Defaults to NULL.

Details

The mysvd function takes in a tall -Omics data matrix, extracts the feature means, centers the matrix on this mean vector, and calculates the Singular Value Decomposition (SVD) of the centered data matrix. Currently, the SVD is calculated via the fast.svd function from corpcor package. However, this function calculates all the singular vectors, even when n.components is non-NULL. We should experiment with other SVD functions, such as the rsvd function from the rsvd package. ENHANCEMENT.

Value

A list containing:

Examples

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  # 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
  
  mysvd(t(getAssay(colon_OmicsSurv))[asthmaGenes_char, ])

## End(Not run)
  

pathwayPCA documentation built on Dec. 15, 2020, 6:14 p.m.