weightedPearsonCorr {GIGSEA} | R Documentation

## weightedPearsonCorr

### Description

weightedPearsonCorr caculates the weighted Pearson correlation

### Usage

    weightedPearsonCorr(x, y, w = rep(1, nrow(x))/nrow(x))

### Arguments

`x` | a matrix of numeric values in the size of genes x featureA
`y` | a matrix of numeric values in the size of genes x featureB
`w` | a vector of numeric values indicating the weights of genes

### Value

a matrix of numeric values in the size of featureA*featureB, indicating the weighted Pearson correlation coefficients

### Author(s)

Shijia Zhu, shijia.zhu@mssm.edu

### See Also

`orderedIntersect`; `matrixPval`;

### 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 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 simple 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 , 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) )

    # calculate the weighted Pearson correlation
    observedCorr = weightedPearsonCorr( x=net2 , y=data2$fc, w=data2$weights )

    # calculate the p values of the weighted Pearson correlation
    observedPval = matrixPval( observedCorr, df=sum(weights>0,na.rm=TRUE)-2 )

    res = data.frame( observedCorr , observedPval )
    head(res)

[Package GIGSEA version 1.4.0 Index]