Function to more efficiently screen for gene triplets for those with a high liquid association value.

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Description

Function reduces the processing power and memory needed to calculate modified liquid association (MLA) values for a genome by using a pre-screening method to reduce the candidate pool to triplets likely to have a high MLA value. It does this using matrix algebra to create an approximation to the direct MLA estimate for all possible pairs of X1X2|X3.

Usage

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fastMLA(data, topn = 2000, nvec = 1, rvalue = 0.5, cut = 4, threads = detectCores())

Arguments

data

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

topn

Number of results to return, ordered from highest |MLA| value descending.

nvec

Numeric vector of the gene(s) to use in the X3 position of the X1X2|X3 screening. This should be a numeric vector representing the column #(s) of the gene.

rvalue

Tolerance value for LA approximation. Lower values of rvalue will cause a more thorough search, but take longer.

cut

Value passed to the GLA 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.

threads

Number of cores to use for multi-threading in correlation calculation (enableWGCNAThreads argument). See WGCNA.

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 for cut would be 5-7.

Value

A data frame with 5 variables: the genes in positions X1, X2 and X3; the rhodiff value of the triplet; and the GLA value of the triplet. A more comprehensive discussion of these values is available in the vignette.

Warning

The data matrix must be numeric.

Note

While this is intended to significantly reduce processing time for identifying high MLA values (and in our estimates did so by >90

Author(s)

Tina Gunderson

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

LiquidAssociation, parallel, WGCNA

Examples

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#to view function code
selectMethod("fastMLA", "matrix")

#
library(fastLiquidAssociation)
library(yeastCC)
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]

example <- fastMLA(data=data, topn=25, nvec=1:10, rvalue=1.0, cut=4)
example[1:5,]
closeAllConnections()