Dincer Goksuluk1, Gokmen Zararsiz2, Selcuk Korkmaz3, Vahap Eldem4, Gozde Erturk Zararsiz5, Erdener Ozcetin6, Ahmet Ozturk7, Ahmet Ergun Karaagaoglu8. 1. Department of Biostatistics, School of Medicine, Hacettepe University, 06100, Ankara, Turkey; Turcosa Analytics Solutions Ltd. Co., Erciyes Teknopark 5, 38030, Kayseri, Turkey. 2. Department of Biostatistics, School of Medicine, Erciyes University, 38030, Kayseri, Turkey; Turcosa Analytics Solutions Ltd. Co., Erciyes Teknopark 5, 38030, Kayseri, Turkey. Electronic address: gokmenzararsiz@hotmail.com. 3. Department of Biostatistics, School of Medicine, Trakya University, 22030, Edirne, Turkey; Turcosa Analytics Solutions Ltd. Co., Erciyes Teknopark 5, 38030, Kayseri, Turkey. 4. Department of Biology, Faculty of Science, Istanbul University, 34452, Istanbul, Turkey. 5. Department of Biostatistics, School of Medicine, Erciyes University, 38030, Kayseri, Turkey. 6. Department of Industrial Engineering, Faculty of Engineering, Hitit University, 19030, Corum, Turkey. 7. Department of Biostatistics, School of Medicine, Erciyes University, 38030, Kayseri, Turkey; Turcosa Analytics Solutions Ltd. Co., Erciyes Teknopark 5, 38030, Kayseri, Turkey. 8. Department of Biostatistics, School of Medicine, Hacettepe University, 06100, Ankara, Turkey.
Abstract
BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network. METHODS: Classification of RNA-sequencing data is not straightforward since raw data should be preprocessed before downstream analysis. With MLSeq package, researchers can easily preprocess (normalization, filtering, transformation etc.) and classify raw RNA-sequencing data using two strategies: (i) to perform algorithms which are directly proposed for RNA-sequencing data structure or (ii) to transform RNA-sequencing data in order to bring it distributionally closer to microarray data structure, and perform algorithms which are developed for microarray data. Moreover, we proposed novel algorithms such as voom (an acronym for variance modelling at observational level) based nearest shrunken centroids (voomNSC), diagonal linear discriminant analysis (voomDLDA), etc. through MLSeq. MATERIALS: Three real RNA-sequencing datasets (i.e cervical cancer, lung cancer and aging datasets) were used to evalute model performances. Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) were selected as algorithms based on dicrete distributions, and voomNSC, nearest shrunken centroids (NSC) and support vector machines (SVM) were selected as algorithms based on continuous distributions for model comparisons. Each algorithm is compared using classification accuracies and sparsities on an independent test set. RESULTS: The algorithms which are based on discrete distributions performed better in cervical cancer and aging data with accuracies above 0.92. In lung cancer data, the most of algorithms performed similar with accuracies of 0.88 except that SVM achieved 0.94 of accuracy. Our voomNSC algorithm was the most sparse algorithm, and able to select 2.2% and 6.6% of all features for cervical cancer and lung cancer datasets respectively. However, in aging data, sparse classifiers were not able to select an optimal subset of all features. CONCLUSION: MLSeq is comprehensive and easy-to-use interface for classification of gene expression data. It allows researchers perform both preprocessing and classification tasks through single platform. With this property, MLSeq can be considered as a pipeline for the classification of RNA-sequencing data.
BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network. METHODS: Classification of RNA-sequencing data is not straightforward since raw data should be preprocessed before downstream analysis. With MLSeq package, researchers can easily preprocess (normalization, filtering, transformation etc.) and classify raw RNA-sequencing data using two strategies: (i) to perform algorithms which are directly proposed for RNA-sequencing data structure or (ii) to transform RNA-sequencing data in order to bring it distributionally closer to microarray data structure, and perform algorithms which are developed for microarray data. Moreover, we proposed novel algorithms such as voom (an acronym for variance modelling at observational level) based nearest shrunken centroids (voomNSC), diagonal linear discriminant analysis (voomDLDA), etc. through MLSeq. MATERIALS: Three real RNA-sequencing datasets (i.e cervical cancer, lung cancer and aging datasets) were used to evalute model performances. Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) were selected as algorithms based on dicrete distributions, and voomNSC, nearest shrunken centroids (NSC) and support vector machines (SVM) were selected as algorithms based on continuous distributions for model comparisons. Each algorithm is compared using classification accuracies and sparsities on an independent test set. RESULTS: The algorithms which are based on discrete distributions performed better in cervical cancer and aging data with accuracies above 0.92. In lung cancer data, the most of algorithms performed similar with accuracies of 0.88 except that SVM achieved 0.94 of accuracy. Our voomNSC algorithm was the most sparse algorithm, and able to select 2.2% and 6.6% of all features for cervical cancer and lung cancer datasets respectively. However, in aging data, sparse classifiers were not able to select an optimal subset of all features. CONCLUSION: MLSeq is comprehensive and easy-to-use interface for classification of gene expression data. It allows researchers perform both preprocessing and classification tasks through single platform. With this property, MLSeq can be considered as a pipeline for the classification of RNA-sequencing data.
Authors: Venkatesan Chandran; M G Sumithra; Alagar Karthick; Tony George; M Deivakani; Balan Elakkiya; Umashankar Subramaniam; S Manoharan Journal: Biomed Res Int Date: 2021-05-04 Impact factor: 3.411
Authors: Matthew A Scott; Amelia R Woolums; Cyprianna E Swiderski; Andy D Perkins; Bindu Nanduri Journal: Sci Rep Date: 2021-11-25 Impact factor: 4.379
Authors: Eddy H C Wang; Isha Monga; Brigitte N Sallee; James C Chen; Alexa R Abdelaziz; Rolando Perez-Lorenzo; Lindsey A Bordone; Angela M Christiano Journal: PNAS Nexus Date: 2022-07-11