Description Usage Arguments Details Value Author(s) References Examples
View source: R/createVectors.R
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 2 3 4 5 6 | createVectors(
x,
y = NULL,
groups,
cor.method = c("spearman", "pearson", "bwmc", "sparcc")
)
|
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.
List of two named numeric vectors. Vectors give the correlation coefficients for groups 1 and 2 respectively, and vector names give the each feature for the resptive feature pair seperated by an underscore.
Charlotte Siska siska.charlotte@gmail.com
Max McGrath max.mcgrath@ucdenver.edu
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.
1 2 3 4 5 6 7 8 9 10 11 12 | ## load data
data("TCGA_GBM_miRNA_microarray")
data("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)
|
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