permutationSimpleLmMatrix {GIGSEA} | R Documentation |
permutationSimpleLmMatrix is a permutation test to calculate the empirical p values for the weighted simple linear regression model based on the weighted Pearson correlation.
permutationSimpleLmMatrix(fc, net, weights = rep(1, nrow(net)), num = 100, step = 1000, verbose = TRUE)
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 |
a data frame comprising following columns:
term a vector of character values incidating the name of gene set.
usedGenes a vector of numeric values indicating the number of gene used in the model.
observedCorr a vector of numeric values indicating the observed weighted Pearson correlation coefficients.
empiricalPval a vector of numeric values [0,1] indicating the permutation-based empirical p values.
BayesFactor a vector of numeric values indicating the Bayes Factor for the multiple test correction.
Shijia Zhu, shijia.zhu@mssm.edu
orderedIntersect
; permutationSimpleLm
;
# 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 , by.x = data$gene , by.y = rownames(net) ) net2 <- orderedIntersect( x = net , by.x = rownames(net) , by.y = data$gene ) all( rownames(net2) == as.character(data2$gene) ) # the SGSEA.res1 uses the weighted simple linear regression model, # while SGSEA.res2 used the weighted Pearson correlation. The latter one # takes substantially less time. # system.time(SGSEA.res1<-permutationSimpleLm(fc=data2$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)