CuNA ============ .. image:: img/cunaverse.png Complex diseases are characterized by their intricate etiology, combining a variety of factors and spanning a diverse set of omics data types. To obtain insights on disease subtypes, biomarkers associated with them,and predict disease states, we need to integrate and analyze multiomics data. Here, we propose a cumulant-based network analysis toolkit (*CuNA*) which finds higher-order interactions between multiomics variables and identify disease subtypes, discover biomarkers, visualize them in an interactive dashboard, and provide a novel method to compute cumulant-based risk scores (*CuRES*) from multiomics data. Framework ---------- .. image:: img/CuNA_pipeline.png Input ^^^^^^^^^^^^^^ CuNA takes a csv file as input with the features in columns and samples in rows (see `./docs/data/tcga_mixomics_new_train_forcuna.csv`). It is multithreaded and takes an argument for number of threads. Output ^^^^^^^^^^^^^^ cd CuNA outputs three components: Networks ---------- a) Network file with three columns, `v1`,`v2`,`count`, corresponding to two vertices and the interaction/edge term between them. b) A file with the communities corresponding to the features. c) A file with the node rank (importance of node) CuRES ---------- A risk score called *CuRES* computed from multi-omics data. CuNAviz ---------- An interactive visualizer, *CuNAviz* can be executed from 1. `_CuNAviz example 1 `_ 2. `_CuNAviz example 2 `_ 3. `_CuNAviz example 3 `_ 4. `_CuNAviz example 4 `_ Usage ------- See `tutorial `_ Contact ------- ``` Aritra Bose (a dot bose at ibm dot com) ``` Citation -------- Please cite the following article if you use CuNA: *Bose, A., Platt, D. E., Haiminen, N., & Parida, L. (2021). CuNA: Cumulant-based Network Analysis of genotype-phenotype associations in Parkinson’s Disease. medRxiv, 2021-08.*