hcpcv: Perform the HCP normalization algorithm on a grid of model...

In mvaniterson/Rhcpp: Normalizing RNA-Sequencing Data by Modeling Hidden Covariates with Prior Knowledge plus extentions to perform EWAS and TWAS

Description

Perform the HCP normalization algorithm on a grid of model parameters

Usage

hcpcv(Z, Y, kRange = c(10, 20), lambdaRange = c(1, 5, 10, 20),
  performance = NULL, iter = 100, stand = TRUE, log = TRUE,
  verbose = TRUE, fast = TRUE)

Arguments

Z	a matrix nxd of known covariates, where n is the number of subjects and d is the number of known covariates. *must be standardize (columns have 0 mean and constant SS).
Y	a matrix of nxg of expression data (must be standardized (columns scaled to have constant SS and mean 0). ** use standardize function to standardize F and Y.
kRange	multiple numbers of inferred hidden components (k is an integer)
lambdaRange	multiple model parameters
performance	function accepting res with res$Res the transformed Residuals
iter	(optional) iter: number of iterations (default = 100);
stand	default standardize data TRUE
log	default log-transformation TRUE
verbose	default TRUE
fast	default use fast RcppArmadillo implementation

Details

This function can be used to find the optimal model parameters with a used-defined performance function

Value

vector of performance measures with names indicating the model parameter

Author(s)

mvaniterson

Examples

## Not run: 
library(BiocParallel)
library(Rhcpp)
register(MulticoreParam(3))
kRange <- c(10, 20)
lambdaRange <- c(1, 5, 10, 20)
data(rhcppdata)
F <- rhcppdata$F
Y <- rhcppdata$Y
##not really meaning full performance function
res <- hcppcv(Z, Y, kRange, lambdaRange, performance=function(res) sum(res$Res))
hist(res)
which.min(res)

## End(Not run)

mvaniterson/Rhcpp documentation built on Feb. 24, 2020, 4:06 p.m.