MicrobiomeSurv

An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) doi:10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-31097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3), Elastic net approach by Hui Zou and Trevor Hastie (2005) doi:10.1111/j.1467-9868.2005.00503.x, supervised principle component analysis of Wold Svante et al. (1987) doi:10.1016/0169-7439(87)80084-9, and supervised partial least squares analysis by Inge S. Helland https://www.jstor.org/stable/4616159. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

Last 30 days

This package has been downloaded 169 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.

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Last 365 days

This package has been downloaded 1,867 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 23 downloads.

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