Analysis and visualization of expression patterns with fuzzy sets as FlowSets

Abstract The growing availability of high-dimensional, complex datasets demands analysis methods that are both interpretable and flexible. We introduce FlowSets, a novel framework for identifying and analysing flows—fuzzy, interpretable patterns—across multiple, diverse, and heterogeneous data sets. Views are constructed as an interpretation of biological features of the data sets by grouping absolute or relative values, summary statistics (such as fold changes or P-values), or higher-order comparisons into fuzzy, linguistically defined categories based on their underlying distributions. FlowSets builds on these fuzzy categorizations and then tracks how features transition between categories across different conditions or data types, uncovering structural patterns that conventional methods often overlook. The FlowSets framework enables users to define, analyse, and manipulate complex patterns across heterogeneous datasets in a flexible and interpretable manner. With FlowSets, users can visualize feature flows, quantify pattern memberships, and perform enrichment analysis explicitly designed for sets with gradual memberships. This approach offers a robust and customizable alternative to rigid clustering or hard thresholding, allowing for a more transparent and insightful interpretation of multidimensional biological data.