createVectors: Create Pearson's correlation coefficient vectors based on...
In discordant: The Discordant Method: A Novel Approach for Differential Correlation

Description Usage Arguments Details Value Author(s) References Examples

Calculates correlation coefficients based on two groups of omics bivariate data. Currently, only two groups of samples can be specified. Used to make input for discordantRun().

1	createVectors(x, y = NULL, groups, cor.method = c("spearman"))

`x`	ExpressionSet of -omics data
`y`	optional second ExpressionSet of -omics data, induces dual -omics analysis
`groups`	n-length vector of 1s and 2s matching samples belonging to groups 1 and 2
`cor.method`	correlation method to measure association. Options are "spearman", "pearson", "bwmc" and "sparcc"

Creates vectors of correlation coefficents based on feature pairs within x or between x and y. The names of the vectors are the feature pairs taken from x and y.

`v1`	List of correlation coefficients for group 1
`v2`	List of correlation coefficients for group 2

Charlotte Siska <siska.charlotte@gmail.com>

Siska C, Bowler R and Kechris K. The Discordant Method: A Novel Approach for Differential Correlation. (2015) Bioinformatics. 32(5): 690-696. Friedman J and Alm EJ. Inferring Correlation Networks from Genomic Survey Data. (2012) PLoS Computational Biology. 8:9, e1002687.

## load data
data("TCGA_GBM_miRNA_microarray") # loads matrix called TCGA_GBM_miRNA_microarray
data("TCGA_GBM_transcript_microarray") # loads matrix called TCGA_GBM_transcript_microarray
print(colnames(TCGA_GBM_transcript_microarray)) # look at groups

groups <- c(rep(1,10), rep(2,20))

# transcript-transcript pairs

vectors <- createVectors(TCGA_GBM_transcript_microarray, groups = groups, cor.method = c("pearson"))

# miRNA-transcript pairs

vectors <- createVectors(TCGA_GBM_transcript_microarray, TCGA_GBM_miRNA_microarray, groups = groups)