connectivityScore {PharmacoGx} | R Documentation

## Function computing connectivity scores between two signatures

### Description

A function for finding the connectivity between two signatures, using either the GSEA method based on the KS statistic, or the gwc method based on a weighted spearman statistic. The GSEA analysis is implemented in the piano package.

### Usage

    connectivityScore(
      x,
      y,
      method = c("gsea", "fgsea", "gwc"),
      nperm = 10000,
      nthread = 1,
      gwc.method = c("spearman", "pearson"),
      ...
    )

### Arguments

`x` | A `matrix` with the first gene signature. In the case of GSEA the vector of values per gene for GSEA in which we are looking for an enrichment. In the case of gwc, this should be a matrix, with the per gene responses in the first column, and the significance values in the second.
`y` | A `matrix` with the second signature. In the case of GSEA, this is the vector of up and down regulated genes we are looking for in our signature, with the direction being determined from the sign. In the case of gwc, this should be a matrix of identical size to x, once again with the per gene responses in the first column, and their significance in the second.
`method` | `character` string identifying which method to use, out of 'fgsea' and 'gwc'
`nperm` | `numeric`, how many permutations should be done to determine significance through permutation testing? The minimum is 100, default is 1e4.
`nthread` | `numeric`, how many cores to run parallel processing on.
`gwc.method` | `character`, should gwc use a weighted spearman or pearson statistic?
`...` | Additional arguments passed down to gsea and gwc functions

### Value

`numeric` a numeric vector with the score and the p-value associated with it

### References

F. Pozzi, T. Di Matteo, T. Aste, 'Exponential smoothing weighted correlations', The European Physical Journal B, Vol. 85, No 6, 2012. DOI: 10.1140/epjb/e2012-20697-x

Varemo, L., Nielsen, J. and Nookaew, I. (2013) Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Research. 41 (8), 4378-4391. doi: 10.1093/nar/gkt111

### Examples

    xValue <- c(1,5,23,4,8,9,2,19,11,12,13)
    xSig <- c(0.01, 0.001, .97, 0.01,0.01,0.28,0.7,0.01,0.01,0.01,0.01)
    yValue <- c(1,5,10,4,8,19,22,19,11,12,13)
    ySig <- c(0.01, 0.001, .97,0.01, 0.01,0.78,0.9,0.01,0.01,0.01,0.01)
    xx <- cbind(xValue, xSig)
    yy <- cbind(yValue, ySig)
    rownames(xx) <- rownames(yy) <- c('1','2','3','4','5','6','7','8','9','10','11')
    data.cor <- connectivityScore(xx,yy,method='gwc', gwc.method='spearman', nperm=300)

[Package PharmacoGx version 2.4.0 Index]