KNeMAP: A Network Mapping Approach for Knowledge-driven Comparison of Transcriptomic Profiles

Autor: Alisa Pavel, Giusy del Giudice, Michele Fratello, Leo Ghemtio, Antonio Di Lieto, Jari Yli-Kauhaluoma, Henri Xhaard, Antonio Federico, Angela Serra, Dario Greco
Rok vydání: 2023
Předmět:
Zdroj: Bioinformatics.
ISSN: 1367-4811
1367-4803
Popis: Motivation Transcriptomic data can be used to describe the mechanism of action (MOA) of a chemical compound. However, omics data tend to be complex and prone to noise, making the comparison of different datasets challenging. Often, transcriptomic profiles are compared at the level of individual gene expression values, or sets of differentially expressed genes. Such approaches can suffer from underlying technical and biological variance, such as the biological system exposed on or the machine/method used to measure gene expression data, technical errors and further neglect the relationships between the genes. We propose a network mapping approach for knowledge-driven comparison of transcriptomic profiles (KNeMAP), which combines genes into similarity groups based on multiple levels of prior information, hence adding a higher level view onto the individual gene view. When comparing KNeMAP with fold change (expression) based and deregulated gene set based methods, KNeMAP was able to group compounds with higher accuracy with respect to prior information as well as is less prone to noise corrupted data. Result We applied KNeMAP to analyze the Connectivity Map dataset, where the gene expression changes of three cell lines were analyzed after treatment with 676 drugs as well as the Fortino et al. dataset where two cell lines with 31 nanomaterials were analyzed. Although the expression profiles across the biological systems are highly different, KNeMAP was able to identify sets of compounds that induce similar molecular responses when exposed on the same biological system. Availability Relevant data and the KNeMAP function is available at: https://github.com/fhaive/KNeMAP and 10.5281/zenodo.7334711. Supplementary information Supplementary data are available at Bioinformatics online.
Databáze: OpenAIRE
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