Description Usage Arguments Value Author(s) References Examples
nc.score calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data.
It is an extension to Diamond's checkerboard score (See references below) to ordinal data and implements a framework for robust detection of species-level association patterns in metagenomic data.
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x |
A numeric vector, data frame, or matrix. The first entity to be processed. Columns are bugs, rows are samples. |
y |
NULL(default) or a umeric vector, data frame, or matrix with compatible dimensions to x. Columns are features, rows are samples. |
use |
An optional character string givinga method for computing covariances in the presence of missing values. This must be (an abbreviaion of) on of the strings "everything", "all.obs", "complete.obs","na.or.complete", or "pairwise.complete.obs". |
nbins |
A non-negative integer of the number of bins to generate (cutoffs will be generated by the discretize function from the infotheo package). |
bin.cutoffs |
A list of values demarcating the bin cutoffs. The binning is performed using the findInterval function. |
Matrix or vector of normalized scores.
Craig Bielski<craig.bielski@gmail.com>
Emma Schwager and Colleagues. Detecting statistically significant associtations between sparse and high dimensional compositioanl data. In Progress.
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data <- matrix(rlnorm(40,meanlog=0,sdlog=1),nrow=10)
data.rowsum <- apply(data,1,sum)
data.norm <- data/data.rowsum
testdata <- data.norm
dimnames(testdata) <- list(paste("Sample",seq(1,10)),paste("Feature",seq(1,4)))
nc.score.results <- nc.score( x=testdata )
nc.score.results.bins <- nc.score( x=testdata )
nc.score.results.bin.cutoffs <- nc.score( x=testdata )
nc.score.results
nc.score.results.bins
nc.score.results.bin.cutoffs
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