permutationMultipleLm: permutationMultipleLm

In zhushijia/GIGSEA: Genotype Imputed Gene Set Enrichment Analysis

Description

permutationMultipleLm is a permutation test to calculate the empirical p values for a weighted multiple linear regression.

Usage

permutationMultipleLm(fc, net, weights = rep(1, nrow(net)), num = 100,
  verbose = TRUE)

Arguments

fc	a vector of numeric values representing gene expression fold change
net	a matrix of numeric values in the size of gene number x gene set number, representing the connectivity between genes and gene sets
weights	a vector of numeric values representing the weights of permuated genes
num	an integer value representing the number of permutations
verbose	an boolean value indicating whether or not to print output to the screen

Value

a data frame comprising the following columns:

• term a vector of character incidating the names of gene sets.

• usedGenes a vector of numeric values indicating the number of genes used in the model.

• Estimate a vector of numeric values indicating the regression coefficients.

• Std..Error a vector of numeric values indicating the standard errors of regression coefficients.

• t.value a vector of numeric values indicating the t-statistics of regression coefficients.

• observedPval a vector of numeric values [0,1] indicating the p values from the multiple weighted regression model.

• empiricalPval a vector of numeric values [0,1] indicating the empirical p values from the permutation test.

Author(s)

Shijia Zhu, [e-mail]

See Also

orderedIntersect; permutationMultipleLmMatrix;

Examples

# load data
data(heart.metaXcan)
gene <- heart.metaXcan$gene_name

# extract the imputed Z-score of differential gene expression, which follows 
# the normal distribution
fc <- heart.metaXcan$zscore

# use as weights the prediction R^2 and the fraction of imputation-used SNPs 
usedFrac <- heart.metaXcan$n_snps_used / heart.metaXcan$n_snps_in_cov
r2 <- heart.metaXcan$pred_perf_r2
weights <- usedFrac*r2

# build a new data frame for the following weighted linear regression-based 
# enrichment analysis
data <- data.frame(gene,fc,weights)
head(data)

net <- MSigDB.KEGG.Pathway$net

# intersect the imputed genes with the gene sets of interest
data2 <- orderedIntersect( x = data[,c("fc","weights")] , by.x = data$gene , 
by.y = rownames(net)  )
net2 <- orderedIntersect( x = net , by.x = rownames(net) , 
by.y = data$gene  )
all( rownames(net2) == rownames(data2) )

# the MGSEA.res1 uses the weighted multiple linear regression to do 
# permutation test, 
# while MGSEA.res2 used the solution of weighted matrix operation. The 
# latter one takes substantially less time.
# system.time( MGSEA.res1<-permutationMultipleLm(fc=data2$fc, net=net2, 
# weights=data2$weights, num=1000))
# system.time( MGSEA.res2<-permutationMultipleLmMatrix(fc=data2$fc, 
# net=net2, weights=data2$weights, num=1000))
# head(MGSEA.res2)

zhushijia/GIGSEA documentation built on May 3, 2019, 10:45 p.m.