Fuzzifier*: Robust and Sensitive Multi-omics Data Analysis

Abstract Motivation Categorization is an important means for interpreting data and drawing conclusions. Often, the derived categories provide evidence for diagnostic or even therapeutic approaches. The standard pipelines for differential analysis of multi-omic high-throughput, and in particular single-cell data, yield (ranked) lists of possibly differential features after applying appropriate effect sizes or significance thresholds of computed p -value and/or foldchange. Results We propose the Fuzzifier * pipeline for the differential analysis of any type of high-throughput data, either raw input data or fold-change data of groups of a (small or large) number of replicates. In Fuzzifier * , categorization can be applied to any step of the analysis pipeline according to custom-designed fuzzy concepts ( Fuzzifier ). Thus, any (fuzzified) analysis option corresponds to a path in a ‘commutative diagram’ specifying the Fuzzifier * pipeline. Fuzzifier * computes a user-defined set of paths and presents an overview of the results, thereby identifying both highly reliable (consensus) and sensitive (path-specific) features. Fuzzifier * is a method that can be applied to any analysis pipeline to obtain different views on the data and yield more reliable results. This is demonstrated by the identification of context-specific miRNAs for individual cancer types from TCGA data. Fuzzifier * could both validate known cancer-specific miRNAs and identify novel candidates. In comparison to statistical tests, Fuzzifier * focuses on value distributions of tumor and normal samples as well as paired foldchange distributions and, thus, identifies condition-specific features from a relatively small number of replicates. Availability and Implementation https://github.com/zimmerlab/fuzzifier Contact offensperger@bio.ifi.lmu.de and zimmer@ifi.lmu.de