Robust Significance Analysis of Microarrays by Minimum<i>β</i>-Divergence Method

Shahjaman,; Kumar, Nishith; Mollah, Md. Manir Hossain; Ahmed, Shakil; Begum, Anjuman Ara; Islam, S. M. Shahinul; Mollah, Md. Nurul Haque

doi:10.1155/2017/5310198

Cited by 14 publications

(14 citation statements)

References 32 publications

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“…However, both of them suffer from outliers. To prevail over the problems of classical t-test and SAM, robust SAM using the minimum β-divergence estimators was proposed [ 11 ]. In this paper, we employed robust SAM as a FS method along with classifiers.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the popularity of the statistical FS methods (t-test or SAM), they are sensitive to outliers. Therefore, in this paper, we used robust SAM [ 11 ] as a feature selection method to select the smaller number of informative features to train the classifiers Figure 4 . The detail procedure of patient classification is as follows:…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, the popular classifiers produce misleading results in presence of outliers when feature selection is performed using classical t-test or SAM. Recently, we have robustified the SAM approach by minimum β-divergence estimators to solve the allaforesaid problems of classical t-test and SAM [ 11 ]. Therefore, in this paper, we employ robust SAM as a feature selection method along with classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Robust Feature Selection Approach for Patient Classification using Gene Expression Data

et al. 2017

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Patient classification through feature selection (FS) based on gene expression data (GED) has already become popular to the research communities. T-test is the well-known statistical FS method in GED analysis. However, it produces higher false positives and lower accuracies for small sample sizes or in presence of outliers. To get rid from the shortcomings of t-test with small sample sizes, SAM has been applied in GED. But, it is highly sensitive to outliers. Recently, robust SAM using the minimum β-divergence estimators has overcome all the problems of classical t-test & SAM and it has been successfully applied for identification of differentially expressed (DE) genes. But, it was not applied in classification. Therefore, in this paper, we employ robust SAM as a feature selection approach along with classifiers for patient classification. We demonstrate the performance of the robust SAM in a comparison of classical t-test and SAM along with four popular classifiers (LDA, KNN, SVM and naive Bayes) using both simulated and real gene expression datasets. The results obtained from simulation and real data analysis confirm that the performance of the four classifiers improve with robust SAM than the classical t-test and SAM. From a real Colon cancer dataset we identified 21 additional DE genes using robust SAM that were not identified by the classical t-test or SAM. To reveal the biological functions and pathways of these 21 genes, we perform KEGG pathway enrichment analysis and found that these genes are involved in some important pathways related to cancer disease.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Feature Selection Approach for Patient Classification using Gene Expression Data

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“…However, they did not consider the problems of outliers in their studies. GEDs are often contaminated by outliers due to several steps involved in the data generating process from hybridization of DNA samples to image analysis (Shahjaman et al 2017a(Shahjaman et al , 2017b(Shahjaman et al and 2019. Therefore, in this paper, a comprehensive study has been carried out among five popular machine learning algorithms (SVM, RF, Naïve Bayes, k-NN and LDA) using both simulated and real gene expression datasets, in absence and presence of outliers.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of different machine learning algorithms in presence of outliers using gene expression data

et al. 2019

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Classification of samples into one or more populations is one of the main objectives of gene expression data (GED) analysis. Many machine learning algorithms were employed in several studies to perform this task. However, these studies did not consider the outliers problem. GEDs are often contaminated by outliers due to several steps involve in the data generating process from hybridization of DNA samples to image analysis. Most of the algorithms produce higher false positives and lower accuracies in presence of outliers, particularly for lower number of replicates in the biological conditions. Therefore, in this paper, a comprehensive study has been carried out among five popular machine learning algorithms (SVM, RF, Naïve Bayes, k-NN and LDA) using both simulated and real gene expression datasets, in absence and presence of outliers. Three different rates of outliers (5%, 10% and 50%) and six performance indices (TPR, FPR, TNR, FNR, FDR and AUC) were considered to investigate the performance of five machine learning algorithms. Both simulated and real GED analysis results revealed that SVM produced comparatively better performance than the other four algorithms (RF, Naïve Bayes, k-NN and LDA) for both small-and-large sample sizes. J. bio-sci. 28: 69-80, 2020

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“…Also, most of the comparative studies did not consider the problems of outliers in their datasets. Outliers are often occur in the gene expression data due to several steps involved in the data generating process from hybridization to image analysis (Shahjaman et al 2017). Thus in presence of outliers, the results of downstream analysis using the popular gene selection methods might be changed.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of biomarker gene selection methods in presence of outliers

et al. 2018

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The main purpose of gene expression data analysis is to identify the biomarker genes by comparing the gene expression levels between two different groups or conditions. There are several methods to select biomarker genes and many comparative studies have been performed to select the appropriate method. However, they did not consider the problems of outliers in their data sets though it is very essential to select the method from robustness point of view due to outliers may occur in the different steps of the gene expression data generating process. In this paper, it is evaluated the performance among five popular statistical biomarker gene selection methods viz. T-test, SAM, LIMMA, KW and FCROS using both simulated and real gene expression data sets in absence and presence of outliers. In the simulated data analysis, it was demonstrated the performance of these methods in terms of different performance measures such as TPR, TNR, FPR, FNR and AUC and based on these measures, it was found that in absence of outliers, for both small-and-large sample cases all the methods perform almost similar. Whereas, in presence of outliers, for small-sample case only the FCROS method perform well than other methods. From a real colon cancer data analysis, it was elucidated that FCROS method identified additional 59 genes that were not detected by the other methods and most of them belongs to the different cancer related pathways.J. bio-sci. 25: 9-16, 2017

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Robust Significance Analysis of Microarrays by Minimumβ-Divergence Method

Cited by 14 publications

References 32 publications

Robust Feature Selection Approach for Patient Classification using Gene Expression Data

Robust Feature Selection Approach for Patient Classification using Gene Expression Data

Performance evaluation of different machine learning algorithms in presence of outliers using gene expression data

A comparative study of biomarker gene selection methods in presence of outliers

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