FUNKI: Interactive functional footprint-based analysis of omics data

TL;DR

FUNKI is an interactive, user-friendly tool for footprint-based analysis of omics data, enabling pathway activity estimation and causal reasoning with visualization, accessible via R and Shiny.

Contribution

It introduces a comprehensive, open-source platform for footprint analysis of omics data, combining ease of use with advanced visualization and causal inference features.

Findings

Supports bulk and single-cell data analysis

Provides visualizations and post-analysis options

Available as R package and web app

Abstract

Motivation: Omics data, such as transcriptomics or phosphoproteomics, are broadly used to get a snap-shot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signaling networks. Results: We have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of several omics data, either from bulk or single-cell experiments. FUNKI also features different options to visualise the results and run post-analyses, and is mirrored as a scripted version in R. Availability: FUNKI is a free and open-source application built on R and Shiny, available in GitHub at https://github.com/saezlab/ShinyFUNKI under GNU v3.0 license and accessible also in https://saezlab.shinyapps.io/funki/ Contact: pub.saez@uni-heidelberg.de Supplementary information: We provide data examples within the app, as well as extensive information about the different variables to select, the results, and the different plots in the help page.