createVectors {discordant} | R Documentation |
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().
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)