SUPPLEMENTARY MATERIAL: Machine learning-based analysis of glioma grades reveals co-enrichment
SND-ID: 2024-339. Version: 1. DOI: https://doi.org/10.57804/6fa3-6v37
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Creator/Principal investigator(s)
Mateusz Garbulowski
- Uppsala University
Research principal
Description
Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts.
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The dataset was originally published in DiVA and moved to SND in 2024. Show less..
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Contributor(s)
Karolina Smolinska Garbulowska
- Uppsala University, Beräkningsbiologi och bioinformatik
Uğur Çabuk - Uppsala University, Institutionen för cell- och molekylärbiologi
Sara A. Yones - Uppsala University, Institutionen för cell- och molekylärbiologi
Ludovica Celli - Uppsala University, Institutionen för cell- och molekylärbiologi
Esmanur Yaz - Uppsala University, Institutionen för cell- och molekylärbiologi
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Karolina Smolinska Garbulowska
- Uppsala University, Beräkningsbiologi och bioinformatik
Uğur Çabuk - Uppsala University, Institutionen för cell- och molekylärbiologi
Sara A. Yones - Uppsala University, Institutionen för cell- och molekylärbiologi
Ludovica Celli - Uppsala University, Institutionen för cell- och molekylärbiologi
Esmanur Yaz - Uppsala University, Institutionen för cell- och molekylärbiologi
Fredrik Barrenäs - Uppsala University, Beräkningsbiologi och bioinformatik
Klev Diamanti
- Uppsala University, Beräkningsbiologi och bioinformatik
Claes Wadelius
- Uppsala University, Medicinsk genetik och genomik
Jan Komorowski
- Uppsala University, Beräkningsbiologi och bioinformatik
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Research area
Bioinformatics (computational biology) (Standard för svensk indelning av forskningsämnen 2011)
Keywords
Garbulowski M, Smolinska K, Çabuk U, Yones SA, Celli L, Yaz EN, Barrenäs F, Diamanti K, Wadelius C, Komorowski J. Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment. Cancers (Basel). 2022 Feb 17;14(4):1014. doi: 10.3390/cancers14041014. PMID: 35205761; PMCID: PMC8870250.
URN:
urn:nbn:se:uu:diva-455177
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