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Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data
Journal article   Open access   Peer reviewed

Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data

Edward Parkinson, Federico Liberatore , W. John Watkins , Robert Andrews , Sarah Edkins , Julie Hibbert , Tobias Strunk , Andrew Currie  and Peter Ghazal 
Frontiers in genetics, Vol.14, 1158352
2023
DOI: https://doi.org/10.3389/fgene.2023.1158352
PMID: 37113992
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Published1.07 MBDownloadView
CC BY V4.0,  Open Access

Abstract

Genetics
Machine learning (ML) algorithms are powerful tools that are increasingly being used for sepsis biomarker discovery in RNA-Seq data. RNA-Seq datasets contain multiple sources and types of noise (operator, technical and non-systematic) that may bias ML classification. Normalisation and independent gene filtering approaches described in RNA-Seq workflows account for some of this variability and are typically only targeted at differential expression analysis rather than ML applications. Pre-processing normalisation steps significantly reduce the number of variables in the data and thereby increase the power of statistical testing, but can potentially discard valuable and insightful classification features. A systematic assessment of applying transcript level filtering on the robustness and stability of ML based RNA-seq classification remains to be fully explored. In this report we examine the impact of filtering out low count transcripts and those with influential outliers read counts on downstream ML analysis for sepsis biomarker discovery using elastic net regularised logistic regression, L1-reguarlised support vector machines and random forests. We demonstrate that applying a systematic objective strategy for removal of uninformative and potentially biasing biomarkers representing up to 60% of transcripts in different sample size datasets, including two illustrative neonatal sepsis cohorts, leads to substantial improvements in classification performance, higher stability of the resulting gene signatures, and better agreement with previously reported sepsis biomarkers. We also demonstrate that the performance uplift from gene filtering depends on the ML classifier chosen, with L1-regularlised support vector machines showing the greatest performance improvements with our experimental data.

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1 Clinical & Life Sciences
1.54 Molecular & Cell Biology - Genetics
1.54.1543 Flow Cytometry
Web Of Science research areas
Genetics & Heredity
ESI research areas
Molecular Biology & Genetics
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