README.md
In codacore: Learning Sparse Log-Ratios for Compositional Data

codacore

A self-contained, up-to-date implementation of CoDaCoRe, in the R programming language, by the original authors.

The CoDaCoRe guide contains a detailed tutorial on installation, usage and functionality.

Note this repository is under active development. If you would like to use CoDaCoRe on your dataset, and have any questions regarding the installation, usage, implementation, or model itself, do not hesitate to contact eg2912@columbia.edu. Some previously asked questions are available on the Issues page. Contributions, fixes, and feature requests are also welcome - please create an issue, submit a pull request, or email me.

We can install CoDaCoRe by running (further details in the guide):

install.packages('codacore')

To fit codacore on some data and check the results (further details in the guide:

library("codacore")
help(codacore) # if in doubt, check documentation
data("Crohn") # load some data and apply codacore
x <- Crohn[, -ncol(Crohn)] + 1
y <- Crohn[, ncol(Crohn)]
model = codacore(
    x, # compositional input, e.g., HTS count data 
    y, # response variable, typically a 0/1 binary indicator 
    logRatioType = "balances", # can use "amalgamations" instead, or abbreviations "B" and "A"
    lambda = 1 # regularization strength (default corresponds to 1SE rule) 
)
print(model)
plot(model)

Gordon-Rodriguez, Elliott, Thomas P. Quinn, and John P. Cunningham. "Learning sparse log-ratios for high-throughput sequencing data." Bioinformatics 38.1 (2022): 157-163. [link]

Quinn, Thomas P., Elliott Gordon-Rodriguez, and Ionas Erb. "A critique of differential abundance analysis, and advocacy for an alternative." arXiv preprint arXiv:2104.07266 (2021). [link]

Thanks for your contributions to codacore!