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tidymodels
recipes:Preprocessing and Feature Engineering Steps for Modeling
A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.
Maintained by Max Kuhn. Last updated 1 hours ago.
586 stars 18.82 score 7.2k scripts 386 dependents
bioc
dar:Differential Abundance Analysis by Consensus
Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.
Maintained by Francesc Catala-Moll. Last updated 19 days ago.
softwaresequencingmicrobiomemetagenomicsmultiplecomparisonnormalizationbioconductorbiomarker-discoverydifferential-abundance-analysisfeature-selectionmicrobiologyphyloseq
2 stars 5.98 score 8 scripts