fastLiquidAssociation (version 1.2.2)

fastboots.GLA-methods: Function to parallelize bootstrapping for MLA robust estimates.

Description

Function to increase speed of bootstrapping for MLA robust estimates. It performs the bootstrapping in parallel, so the time decrease will be dependent on the number of cores or CPUs available. Intended for use with results of triplets not processed sensibly by call to either CNM.full or CNM.simple in mass.CNM, though it can also be used with the direct results of fastMLA.

Usage

fastboots.GLA(tripmat, data, clust, boots = 30, perm = 100, cut = 4)

Arguments

tripmat

Matrix specifying the triplets to be estimated. Intended for use with the results of the mass.CNM or fastMLA functions.

data

Matrix of numeric data, with columns representing genes and rows representing observations.

clust

Cluster of CPU cores created by makeCluster. See parallel.

boots

Number of bootstrap iterations to estimate direct estimate SE.

perm

Number of permutations to use in estimating p-value.

cut

Value passed to the internal clusterGLA function to create buckets (equal to number of buckets+1). Values placing between 15-30 samples per bucket are optimal. Must be a positive integer>1. See GLA.

Value

fastboots.GLA returns a data.frame that specifies the genes in positions X1, X2 and X3; the rhodiff value of the triplet, the GLA value of the triplet, the direct estimate statistic, and the direct estimate p-value. More detailed explanation is available in the package vignette.

Warning

The data matrix must be numeric.

Details

Choosing the number of bins: For example, assume that our data has 100 observations. Since values between 15-30 observations per bin are optimal, good values to choose would be 5-7.

References

[1] Yen-Yi Ho, Giovanni Parmigiani, Thomas A Louis, and Leslie M Cope. Modeling liquid association. Biometrics, 67(1):133-141, 2011.

See Also

fastMLA, mass.CNM, LiquidAssociation, parallel

Examples

#to view the code for the function
selectMethod("fastboots.GLA", signature=c("ANY","matrix"))

#
library(LiquidAssociation)
library(yeastCC)
library(org.Sc.sgd.db)
library(WGCNA)
data(spYCCES)
lae <- spYCCES[,-(1:4)]
### get rid of samples with high NA elements
lae <- lae[apply(is.na(exprs(lae)),1,sum) < ncol(lae)*0.3,]
data <- t(exprs(lae))
data <- data[,1:50]
dim(data)

example <- fastMLA(data=data, topn=25, nvec=1:10, rvalue=1.0, cut=4)
clust <- makeCluster(4)
ex <- example[1:5,]
GLAeasy <- fastboots.GLA(ex, data=data, clust=clust, boots=30, perm=100, cut=4)
stopCluster(clust)
GLAeasy
closeAllConnections()