permutationSimpleLmMatrix: permutationSimpleLmMatrix

Description Usage Arguments Value Author(s) See Also Examples

Description

permutationSimpleLmMatrix is a permutation test to calculate the empirical p values for the weighted simple linear regression model based on the weighted Pearson correlation.

Usage

1
2
permutationSimpleLmMatrix(fc, net, weights = rep(1, nrow(net)),
  num = 100, step = 1000, verbose = TRUE)

Arguments

fc

a vector of numeric values representing the gene expression fold change

net

a matrix of numeric values in the size of gene number x gene set number, representing the connectivity betwen genes and gene sets

weights

a vector of numeric values representing the weights of permuted genes

num

an integer value representing the number of permutations

step

an integer value representing the number of permutations in each step

verbose

an boolean value indicating whether or not to print output to the screen

Value

a data frame comprising following columns:

Author(s)

Shijia Zhu, shijia.zhu@mssm.edu

See Also

orderedIntersect; permutationSimpleLm;

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
# load data
data(heart.metaXcan)
gene <- heart.metaXcan$gene_name

# extract the imputed Z-score of gene differential 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 permuted 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 SGSEA.res1 uses the weighted simple linear regression model, 
# while SGSEA.res2 used the weighted Pearson correlation. The latter one 
# takes substantially less time.
# system.time(SGSEA.res1<-permutationSimpleLm(fc=data2$fc, net=net2, 
# weights=data2$weights, num=1000))
system.time(SGSEA.res2<-permutationSimpleLmMatrix(fc=data2$fc, net=net2, 
weights=data2$weights, num=1000))
head(SGSEA.res2)

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