NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data

RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated. In this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes' rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze four real RNA-Seq data sets to demonstrate the advantage of our method in real-world applications

aFold – using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data

Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene-and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq

Inferential considerations for low-count RNA-seq transcripts: a case study on an edaphic subspecies of dominant prairie grass Andropogon gerardii

Master of ScienceStatisticsNora M. BelloBig bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Sand bluestem exhibits phenotypic divergence related to epicuticular properties and enhanced drought tolerance relative to big bluestem. Understanding the mechanisms underlying differential drought tolerance is relevant in the face of climate change. For bluestem subspecies, presence or absence of these phenotypes may be associated with RNA transcripts characterized by low number of read counts. So called low-count transcripts pose particular inferential challenges and are thus usually filtered out at early steps of data management protocols and ignored for analyses. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on low-count transcripts as motivated by differential bluestem phenotypes. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60% of which are classified as low-count) collected from leaf tissue of 4 individual plants of big bluestem and 4 of sand bluestem. We also compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the performance of recently developed statistical methods for differential expression (DE) analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively. Our results indicate that proper specification of DE methods can remove the need for ad- hoc data filtering at arbitrary expression threshold, thus allowing for inference on low-count transcripts. Practical recommendations for inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications also focused on transcripts of low expression levels