Dataset Information

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.

ABSTRACT: BACKGROUND:Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features. METHODS:In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450?K methylation data were applied instead of the original 450?K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation. RESULTS:Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results. CONCLUSION:The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450?K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation.

SUBMITTER: Fan S

PROVIDER: S-EPMC6357346 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.

Fan Shicai S Tang Jianxiong J Tian Qi Q Wu Chunguo C

BMC medical genomics 20190131 Suppl 1

<h4>Background</h4>Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features.<h4>Methods</h4>In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expand ...[more]

PMID: 30704464

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Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most common cancers. The discovery of specific genes severing as biomarkers is of paramount significance for cancer diagnosis and prognosis. The high-throughput omics data generated by the cancer genome atlas (TCGA) consortium provides a valuable resource for the discovery of HCC biomarker genes. Numerous methods have been proposed to select cancer biomarkers. However, these methods have not investigated the robustness of identification with different feature selection techniques.MethodsWe use six different recursive feature elimination methods to select the gene signiatures of HCC from TCGA liver cancer data. The genes shared in the six selected subsets are proposed as robust biomarkers. Akaike information criterion (AIC) is employed to explain the optimization process of feature selection, which provides a statistical interpretation for the feature selection in machine learning methods. And we use several methods to validate the screened biomarkers.ResultsIn this paper, we propose a robust method for discovering biomarker genes for HCC from gene expression data. Specifically, we implement recursive feature elimination cross-validation (RFE-CV) methods based on six different classication algorithms. The overlaps in the discovered gene sets via different methods are referred as the identified biomarkers. We give an interpretation of the feature selection process based on machine learning using AIC in statistics. Furthermore, the features selected by the backward logistic stepwise regression via AIC minimum theory are completely contained in the identified biomarkers. Through the classification results, the superiority of interpretable robust biomarker discovery method is verified.ConclusionsIt is found that overlaps among gene subsets contain different quantitative features selected by the RFE-CV of 6 classifiers. The AIC values in the model selection provide a theoretical foundation for the feature selection process of biomarker discovery via machine learning. What's more, genes containing in more optimally selected subsets make better biological sense and implication. The quality of feature selection is improved by the intersections of biomarkers selected from different classifiers. This is a general method suitable for screening biomarkers of complex diseases from high-throughput data.

Project description:BackgroundDifferential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.ResultsIn this study, we propose a novel differential expression and feature selection method-GEOlimma-which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.ConclusionsOur results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

Dataset Information

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.

Publications

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.

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