Negative Binomial Additive Model for RNA-Seq Data Analysis

SUMMARYHigh-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and co-variates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for some phenotypes. In this paper, we introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulation and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. Our proposed NBAMSeq is available for download athttps://github.com/reese3928/NBAMSeqand in submission to Bioconductor repository.

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Adjusted Sample Size Calculation for RNA-seq Data in the Presence of Confounding Covariates

BioMedInformatics ◽

10.3390/biomedinformatics1020004 ◽

2021 ◽

Vol 1 (2) ◽

pp. 47-63

Author(s):

Xiaohong Li ◽

Shesh N. Rai ◽

Eric C. Rouchka ◽

Timothy E. O’Toole ◽

Nigel G. F. Cooper

Keyword(s):

Sample Size ◽

Differential Expression ◽

Expression Analysis ◽

Negative Binomial ◽

Differential Expression Analysis ◽

Sample Size Calculation ◽

Size Estimation ◽

Rna Seq ◽

Simulation Based ◽

The Relationship

Sample size calculation for adequate power analysis is critical in optimizing RNA-seq experimental design. However, the complexity increases for directly estimating sample size when taking into consideration confounding covariates. Although a number of approaches for sample size calculation have been proposed for RNA-seq data, most ignore any potential heterogeneity. In this study, we implemented a simulation-based and confounder-adjusted method to provide sample size recommendations for RNA-seq differential expression analysis. The data was generated using Monte Carlo simulation, given an underlined distribution of confounding covariates and parameters for a negative binomial distribution. The relationship between the sample size with the power and parameters, such as dispersion, fold change and mean read counts, can be visualized. We demonstrate that the adjusted sample size for a desired power and type one error rate of α is usually larger when taking confounding covariates into account. More importantly, our simulation study reveals that sample size may be underestimated by existing methods if a confounding covariate exists in RNA-seq data. Consequently, this underestimate could affect the detection power for the differential expression analysis. Therefore, we introduce confounding covariates for sample size estimation for heterogeneous RNA-seq data.

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zingeR: unlocking RNA-seq tools for zero-inflation and single cell applications

10.1101/157982 ◽

2017 ◽

Cited By ~ 7

Author(s):

Koen Van den Berge ◽

Charlotte Soneson ◽

Michael I. Love ◽

Mark D. Robinson ◽

Lieven Clement

Keyword(s):

Single Cell ◽

Differential Expression ◽

Expression Analysis ◽

Negative Binomial ◽

Differential Expression Analysis ◽

Negative Binomial Model ◽

Binomial Model ◽

Rna Seq ◽

Zero Inflation ◽

Zero Counts

AbstractDropout in single cell RNA-seq (scRNA-seq) applications causes many transcripts to go undetected. It induces excess zero counts, which leads to power issues in differential expression (DE) analysis and has triggered the development of bespoke scRNA-seq DE tools that 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 zingeR, a zero-inflated negative binomial model that identifies excess zero counts and generates observation weights to unlock bulk RNA-seq pipelines for zero-inflation, boosting performance in scRNA-seq differential expression analysis.

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Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads

10.1101/016196 ◽

2015 ◽

Author(s):

Hung-I Harry Chen ◽

Yuanhang Liu ◽

Yi Zou ◽

Zhao Lai ◽

Devanand Sarkar ◽

...

Keyword(s):

Differential Expression ◽

Rna Sequencing ◽

Expression Analysis ◽

Background Noise ◽

Negative Binomial ◽

Differential Expression Analysis ◽

Rna Seq ◽

Sequencing Data ◽

Expression Levels ◽

Differential Expressed Genes

Background RNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of them takes the misaligned reads that are mapped to non-exonic regions into account. We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes. Results We implemented our novel XBSeq algorithm and evaluated it by using a set of simulated expression datasets under different conditions, using a combination of negative binomial and Poisson distributions with parameters derived from real RNA-seq data. We compared the performance of our method with other commonly used differential expression analysis algorithms. We also evaluated the changes in true and false positive rates with variations in biological replicates, differential fold changes, and expression levels in non-exonic regions. We also tested the algorithm on a set of real RNA-seq data where the common and different detection results from different algorithms were reported. Conclusions In this paper, we proposed a novel XBSeq, a differential expression analysis algorithm for RNA-seq data that takes non-exonic mapped reads into consideration. When background noise is at baseline level, the performance of XBSeq and DESeq are mostly equivalent. However, our method surpasses DESeq and other algorithms with the increase of non-exonic mapped reads. Only in very low read count condition XBSeq had a slightly higher false discovery rate, which may be improved by adjusting the background noise effect in this situation. Taken together, by considering non-exonic mapped reads, XBSeq can provide accurate expression measurement and thus detect differential expressed genes even in noisy conditions.

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Best practices on the differential expression analysis of multi-species RNA-seq

Genome Biology ◽

10.1186/s13059-021-02337-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Matthew Chung ◽

Vincent M. Bruno ◽

David A. Rasko ◽

Christina A. Cuomo ◽

José F. Muñoz ◽

...

Keyword(s):

Best Practices ◽

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Single Species ◽

Rna Seq ◽

Species Analysis ◽

Differential Gene ◽

Multiple Species ◽

Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

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The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab028 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Xueyi Dong ◽

Luyi Tian ◽

Quentin Gouil ◽

Hasaru Kariyawasam ◽

Shian Su ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Transcriptomic Analysis ◽

Statistical Testing ◽

Rna Seq ◽

Sequencing Data ◽

Short Read ◽

Sequencing Platform ◽

Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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Survey of Methods Used for Differential Expression Analysis on RNA Seq Data

Learning and Analytics in Intelligent Systems - Biologically Inspired Techniques in Many-Criteria Decision Making ◽

10.1007/978-3-030-39033-4_21 ◽

2020 ◽

pp. 226-239

Author(s):

Reema Joshi ◽

Rosy Sarmah

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Rna Seq

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Alignment and mapping methodology influence transcript abundance estimation

10.1101/657874 ◽

2019 ◽

Cited By ~ 6

Author(s):

Avi Srivastava ◽

Laraib Malik ◽

Hirak Sarkar ◽

Mohsen Zakeri ◽

Fatemeh Almodaresi ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Computational Cost ◽

Simulated Data ◽

Transcript Abundance ◽

Mapping Method ◽

Rna Seq ◽

Transcript Quantification ◽

Quantification Model

AbstractBackgroundThe accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy.ResultsWe investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large, and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally-acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment.ConclusionWe observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification.

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