cross.correlate: Cross-correlate columns of the input matrices

Description Usage Arguments Details Value Author(s) References Examples

View source: R/cross.correlate.R

Description

Cross-correlate columns of the input matrices

Usage

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cross.correlate(x, y = NULL, method = "pearson", p.adj.threshold = Inf,
  cth = NULL, order = FALSE, n.signif = 0, mode = "table",
  p.adj.method = "fdr", verbose = FALSE, filter.self.correlations = FALSE)

Arguments

x

matrix (samples x features if annotation matrix)

y

matrix (samples x features if cross-correlated with annotations)

method

association method ('pearson', 'spearman', or 'bicor' for continuous; categorical for discrete)

p.adj.threshold

q-value threshold to include features

cth

correlation threshold to include features

order

order the results

n.signif

mininum number of significant correlations for each element

mode

Specify output format ('table' or 'matrix')

p.adj.method

p-value multiple testing correction method. One of the methods in p.adjust function ('BH' and others; see help(p.adjust)). Default: 'fdr'

verbose

verbose

filter.self.correlations

Filter out correlations between identical items.

Details

As the method=categorical (discrete) association measure for nominal (no order for levels) variables we use Goodman and Kruskal tau based on r-bloggers.com/measuring-associations-between-non-numeric-variables/ The 'bicor' method is from the WGCNA package.

Value

List with cor, pval, pval.adjusted

Author(s)

Contact: Leo Lahti microbiome-admin@googlegroups.com

References

See citation('microbiome')

Examples

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data(peerj32)
  d1 <- peerj32$microbes[1:20, 1:10]
  d2 <- peerj32$lipids[1:20,1:10]
  cc <- cross.correlate(d1, d2)

microbiome/microbiomeold documentation built on May 22, 2019, 9:57 p.m.