connectivityScoreBroad: Function computing connectivity scores between two signatures
In bhklab/CMapBox:

Description Usage Arguments Value References Examples

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.

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connectivityScoreBroad(x, y, method = c("fgsea", "gwc", "xsum",
  "gwcCmapBox"), nperm = 10000, nthread = 1,
  gwc.method = c("spearman", "pearson"), ...)

`x`	A `matrix` with the first gene signature. column one being the values for the genes and column two being their corresponding significance values.
`y`	A `matrix` with the second signature. column one being the values for the genes and column two being their corresponding significance values.
`method`	`character` string identifying which method to use, out of 'gsea', 'xsum' 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

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

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

Cheng, Jie, et al. "Systematic evaluation of connectivity map for disease indications." Genome medicine 6.12 (2014): 95.

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)