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Figueiredo RQ, Del Ser SD, Raschka T, Hofmann-Apitius M, Kodamullil AT, Mubeen S, Domingo-Fernández D. Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets. BMC Bioinformatics 2022; 23:231. [PMID: 35705903 PMCID: PMC9202106 DOI: 10.1186/s12859-022-04765-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/03/2022] [Indexed: 11/10/2022] Open
Abstract
Distinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein-protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at https://zenodo.org/record/5831786 and https://github.com/ContNeXt/ , respectively and developed ContNeXt ( https://contnext.scai.fraunhofer.de/ ), a web application to explore the networks generated in this work.
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Affiliation(s)
- Rebeca Queiroz Figueiredo
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Sara Díaz Del Ser
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany.,Fraunhofer Center for Machine Learning, Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757, Sankt Augustin, Germany
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757, Sankt Augustin, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany.,Fraunhofer Center for Machine Learning, Sankt Augustin, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53757, Sankt Augustin, Germany. .,Fraunhofer Center for Machine Learning, Sankt Augustin, Germany. .,Enveda Biosciences, Boulder, CO, 80301, USA.
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