GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms

Dağ, Osman; Kasikci, Merve; Ilk, Ozlem; Yeşiltepe, Metin

doi:10.1007/s11517-022-02695-w

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…When various Machine Learning (ML) methods are applied to an RNASeq dataset to identify significant features, they generate different lists of selected features. The evaluation of these lists primarily relies on ML classification performance metrics such as accuracy or precision (Chiesa, Colombo, and Piacentini 2018;Dag et al 2022). However, particularly in complex datasets, due to the distinct working principles of various ML methods, they can select considerably different feature lists while still demonstrating similar classification performance metrics.…”

Section: Introductionmentioning

confidence: 99%

GeneSelectR: An R Package Workflow for Enhanced Feature Selection from RNA Sequencing Data

Zhakparov,

Moriarty,

Roqueiro

et al. 2024

Preprint

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Motivation: High-dimensional Bulk RNA sequencing (RNAseq) datasets pose a considerable challenge in identifying biologically relevant features for downstream analyses and data mining efforts. The standard approach involves differential gene expression (DGE) analysis, but its effectiveness can be limited depending on the data due to its univariate nature. In complex datasets, an alternative approach involves employing a variety of machine learning (ML) tools, which attempt to understand non-linear relationships between features and focus on generalizability rather than statistical significance. This approach will result in the generation of multiple feature lists, which might exhibit similarities in terms of classification performance metrics. Therefore, there is an urgent need for a cohesive workflow that seamlessly integrates robust feature selection using diverse ML methods while also evaluating the biological relevance of the resulting feature lists. This combined approach would enable the prioritization of the best-performing list, considering both sets of criteria. Results: We introduce GeneSelectR, an open-source R package that innovatively combines ML and bioinformatic data mining approaches for enhanced feature selection. With GeneSelectR, features can be selected from a normalized RNAseq dataset with a variety of ML methods and user-defined parameters. This is followed by an assessment of their biological relevance with Gene Ontology (GO) enrichment analysis, along with a semantic similarity analysis of the resulting GO terms. Additionally, similarity coefficients and fractions of the GO terms of interest are calculated. With this, GeneSelectR optimizes ML performance and rigorously assesses the biological relevance of the various lists, offering a means to prioritize feature lists with regard to the biological question. When applied to the TCGA-BRCA dataset, the GeneSelectR workflow generated several feature lists using different ML methods and a DGE analysis. By leveraging the various functions in GeneSelectR, the different lists could be evaluated based on both ML performance and biological relevance. This comprehensive evaluation facilitated the selection of the best-performing list, which exhibited both strong machine learning performance and high relevance to the biological question while maintaining a manageable number of highly specific features. Availability: The package is available on CRAN. To install it, run: install.packages('GeneSelectR').

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Section: Introductionmentioning

confidence: 99%

GeneSelectR: An R Package Workflow for Enhanced Feature Selection from RNA Sequencing Data

Zhakparov,

Moriarty,

Roqueiro

et al. 2024

Preprint

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“…Despite significant work in the area of differential expression analysis, to our knowledge, there is a lack of analysis of the impact of gene filtering on downstream ML analysis, specifically in gene selection for biomarker discovery. Although multiple software pipelines have been developed to aid researchers in conducting this type of analysis using high-throughput sequence data ( Chiesa et al, 2018 ; Goksuluk et al, 2019 ; Dag et al, 2022 ), gene filtering features are limited to removing low count and low variance genes and typically require users to provide their own arbitrary thresholds. Previous authors provide little justification for the use of these filters, nor do they provide recommended thresholds or guidance on how these might be adjusted to the ML algorithm being employed.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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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Circulating miRNAs modulating systemic low-grade inflammation and affecting neurodegeneration

Van der Auwera,

Ameling,

Wittfeld

et al. 2024

Progress in Neuro-Psychopharmacology and Biological Psychiatry

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GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms

Cited by 5 publications

References 42 publications

GeneSelectR: An R Package Workflow for Enhanced Feature Selection from RNA Sequencing Data

GeneSelectR: An R Package Workflow for Enhanced Feature Selection from RNA Sequencing Data

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

Circulating miRNAs modulating systemic low-grade inflammation and affecting neurodegeneration

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