A comprehensive simulation study on classification of RNA-Seq data

Abstract

Background RNA sequencing (RNA-Seq) is a powerful technique for transcriptome profiling of the organisms that uses the capabilities of next-generation sequencing (NGS) technologies. Recent advances in NGS let to measure the expression levels of tens to thousands of transcripts simultaneously. Using such information, developing expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of disease. Microarray based classifiers cannot be directly applied due to the discrete nature of RNA-Seq data. One way is to develop count-based classifiers, such as poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA). Other way is to transform the data hierarchically closer to microarrays and apply microarray-based classifiers. In most of the studies, the data overdispersion seems to be an another challenge in modeling RNA-Seq data. In this study, we aimed to examine the effect of dispersion parameter and classification algorithms on RNA-Seq classification. We also considered the effect of other parameters (i) sample size, (ii) number of genes, (iii) number of class, (iv) DE (differential expression) rate, (v) transformation method on classification performance.

Rewiring of gene expression in circulating white blood cells is associated with pregnancy outcome in heifers (Bos taurus)

“…We tested this hypothesis by applying machine learning algorithms on two groups of heifers that were bred in 2015 (year one) and 2016 (year two). Parallel random forest emerged as the algorithm with over 90% efficiency of classification nearly all trials executed, which confirms the potential of accurate classification of samples using RNA-seq data under the case-control framework 60,61 . The results show that while not one single gene emerges as a potential biomarker, the accumulated information of transcript abundance from multiple genes can be powerful for the identification of fertility potential in cattle.…”

Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk

“…Moreover, a number of studies reported that the miRNAs were stable both in the body and in paraffin blocks, which provided better biomarkers of tumor classification (Baker, 2010;Iqbal et al, 2015;Lu et al, 2005;Matamala et al, 2015;Raponi et al, 2009;Zen and Zhang, 2012). Thus, some researchers proposed several methods to find miRNA biomarkers of the cancers, such as miRNA instance-based approaches and miRNA feature-based approaches (Breiman, 2001;Breiman et al, 1984;Zararsiz et al, 2017). Similar to the performance of using gene biomarker classification (Perez-Diez et al, 2007;van 't Veer et al, 2002;Wang et al, 2005), the reproducibility of the miRNA biomarkers has been challenged (Dupuy and Simon, 2007;Ein-Dor et al, 2006).…”

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

“…PLDA may be a good choice for count based classifier after power transformation of data in all dispersion settings [47]. Recently, a few studies were performed to classify the sequencing data involving PLDA which concluded that PLDA classifier is somewhat similar to diagonal LDA and performs satisfactory over sequencing data [22,48].…”