permutationSimpleLm {GIGSEA} | R Documentation

## permutationSimpleLm

### Description

permutationSimpleLm is a permutation test to calculate the empirical p values for a weighted simple linear regression.

### Usage

    permutationSimpleLm(fc, net, weights = rep(1, nrow(net)), num = 100,
      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 betweeen genes and gene sets
`weights` | a vector of numeric values representing the weights of permuted genes
`num` | a vector of integer values 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 values incidating the name of gene set.

- 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 weighted simple regression model.

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

### Author(s)

Shijia Zhu, shijia.zhu@mssm.edu

### See Also

`orderedIntersect`; `permutationSimpleLmMatrix`;

### Examples

    # 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)

[Package GIGSEA version 1.8.0 Index]