CoreOme

Taxonomy and prevalence filter pipeline for microbiome datasets adapted to SqueezeMeta_Reads output.

This pipeline is designed to pre-process SqueezeMeta_reads output by multiple steps:

1. Samples filter: Matches phenotypic data (metadata) and microbiome data sample names, listing and removing those with no coincidence in one of both databases. A genotype dataset can be optionally added.
2. Taxonomy filter: Used only with taxonomy SQMr files (not KEGG or COG files). Removes those taxa not interesting for post-analysis (normally, animalia, plants, virus and unclassified taxa). At the moment, this filter must be edited manually.
3. Prevalence filter: This is the main filter. It uses phenotypic data to create a RandomForest model (randomForest package in R) in order to take the microbiome data subcomposition classifying phenotypes with the lowest OOB-error. Data subcompositions are created removing the 5% lowest prevalent features (i.e., those features present in less than 5% of samples), and increasing percentage 5 to 5 to 95% of lowest prevalence (i.e., features present in less than 95% of samples). The subcomposition with the lowest OOB-error will be chosen as core microbiome.
4. Adding low-prev/high-VI features: We after perform another RandomForest with the inverse subcomposition of the chosen one, onlly using the discarded features. As an example, if our core microbiome contains features present at least in 40% of samples (threshold = 40%) our low-prevalence (LP) subset will be composed by features present in less than 40% of samples, whith which LP-hVI RandomForest is done. We take LP features with a variable importance (VI) higher than 1.0 and a prevalence higher than 5% (can be modified). These LP/hVI features are attached to core microbiome dataset.

How to use it

a) Use Rparams.R to customise paths and other parameters for the pipeline. More info inside the file.
b) Use Rphenotmods.R to modify R classes of phenotypic variables. Useful to convert continuous variables into categorical factors.
c) Run SQMr_exe.sh. It is required to check input, output and bin paths inside the script.