Learning Sparse Log-Ratios for Compositional Data
activeInputs
codacore
Metabolite relative abundances (Franzosa et al., 2019)
Micriobiome relative abundances (Franzosa et al., 2019)
getBinaryPartitions
getDenominatorParts
getLogRatios
getNumeratorParts
getNumLogRatios
getSlopes
getTidyTable
plot
plotROC
predict
simulateHTS
In the context of high-throughput genetic data, CoDaCoRe identifies a set of sparse biomarkers that are predictive of a response variable of interest (Gordon-Rodriguez et al., 2021) <doi:10.1093/bioinformatics/btab645>. More generally, CoDaCoRe can be applied to any regression problem where the independent variable is Compositional (CoDa), to derive a set of scale-invariant log-ratios (ILR or SLR) that are maximally associated to a dependent variable.