RPC Strategy

Recurrent Pattern Classification (RPC) is an strategy to sort data in categories, which traces the relative mean abundances of compounds over time . RPC enabled us to visualize and identify recurrent patterns of chemicals abundance, even those exhibiting subtle increases in specific windows, allowing us to classify them into cumulative, reductive, u-shaped, or bell waves and exclude them from those that did not change over time. While the set is created for metabolomics data, other type of data like curated levels of gene expression by transcriptomics could be used with similar approach.
IMPORTANT: The datasets available here as uploaded files, include three examples of metabolomic profiles classified using RPC belonging to MB (myoblasts undergoing differentiation), NSCs (neural stem cells undergoing differentiation), and MSCs (mesenchymal stem cells undergoing differentiation), all in vitro setups (files Dataset_S1.5_RPC-Strategy_MBs, Dataset_S1.6_RPC-Strategy_NSCs, and Dataset_S1.7_RPC-Strategy_MSCs) plus empty file "to apply the strategy" with user-custom data (Dataset S1.1_RPC-Strategy). Finally the files that explain the content and rules of this strategy (files Dataset_S1.4_Formulas.txt, Dataset_S1.3_Code.txt, and Dataset_S1.2_Instructions for RPC-Strategy.pdf).