Multidimensional omic datasets often have correlated features leading to the possibility of discovering multiple biological signatures with similar predictive performance for a phenotype. However, their exploration is limited by low sample size and the exponential nature of the combinatorial search leading to high computational cost. To address these issues, we have developed an algorithm muSignAl (multiple signature algorithm) which selects multiple signatures with similar predictive performance while systematically bypassing the requirement of exploring all the combinations of features. We demonstrated the workflow of this algorithm with an example of proteomics dataset. muSignAl is applicable in various bioinformatics driven explorations, such as understanding the relationship between multiple biological feature sets and phenotypes, and discovery and development of biomarker panels while providing the opportunity of optimising their development cost with the help of equally good multiple signatures. Source code of muSignAl is freely available at https://github.com/ShuklaLab/muSignAl.
Bibliographical noteFunding Information:
B.P. would like to acknowledge the funding support of Vice‐Chancellor's Research Scholarship (VCRS), Ulster University. A.J.B. would like to acknowledge funding support by a programme grant jointly from the European Union (EU) Regional Development Fund (ERDF) EU Sustainable Competitiveness Programme for Northern Ireland, the Northern Ireland Public Health Agency (HSC R&D) and Ulster University. P.S. would like to acknowledge funding support from Innovate UK NxNW ICURe programme and UKRI NCSi4P programme ‘Optimal cellular assays for SARS‐CoV‐2 T‐cell, B‐cell and innate immunity’. A.J.B. and P.S. would like to acknowledge funding support by a programme grant jointly from Science Foundation Ireland (SFI), Republic of Ireland and Department for the Economy (DfE), Northern Ireland, UK, ‘COVRES: Understanding the host‐virus response in patients with mild versus serious disease’. A.J.B. and P.S. would like to acknowledge funding support by Northern Ireland Public Health Agency (HSC R&D Division), ‘COVRES2: Identifying temporal immune responses associated with Covid‐19 severity’.
© 2022 The Authors. Proteomics published by Wiley-VCH GmbH.