Improved RNA-Seq Partitions in Linear Models for Isoform Quantification

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate. Here, we analyze 58 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation. Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

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Platform-integrated mRNA isoform quantification

Bioinformatics ◽

10.1093/bioinformatics/btz932 ◽

2019 ◽

Vol 36 (8) ◽

pp. 2466-2473 ◽

Cited By ~ 2

Author(s):

Jiao Sun ◽

Jae-Woong Chang ◽

Teng Zhang ◽

Jeongsik Yong ◽

Rui Kuang ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Large Scale ◽

Cancer Cell Lines ◽

Supplementary Information ◽

Rna Seq ◽

Mrna Quantification ◽

Transcript Quantification ◽

Isoform Expression ◽

Isoform Quantification

Abstract Motivation Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. Results In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. Availability and implementation Source code is available at: https://github.com/CompbioLabUcf/IntMTQ. Supplementary information Supplementary data are available at Bioinformatics online.

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No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2017-0010 ◽

2017 ◽

Vol 16 (2) ◽

Cited By ~ 1

Author(s):

Aaron T. L. Lun ◽

Gordon K. Smyth

Keyword(s):

Software Package ◽

Error Control ◽

Degrees Of Freedom ◽

Linear Models ◽

Type I Error ◽

Real Data ◽

Type I ◽

Rna Seq ◽

Study Gene Expression ◽

Complex Models

AbstractRNA sequencing (RNA-seq) is widely used to study gene expression changes associated with treatments or biological conditions. Many popular methods for detecting differential expression (DE) from RNA-seq data use generalized linear models (GLMs) fitted to the read counts across independent replicate samples for each gene. This article shows that the standard formula for the residual degrees of freedom (d.f.) in a linear model is overstated when the model contains fitted values that are exactly zero. Such fitted values occur whenever all the counts in a treatment group are zero as well as in more complex models such as those involving paired comparisons. This misspecification results in underestimation of the genewise variances and loss of type I error control. This article proposes a formula for the reduced residual d.f. that restores error control in simulated RNA-seq data and improves detection of DE genes in a real data analysis. The new approach is implemented in the quasi-likelihood framework of the edgeR software package. The results of this article also apply to RNA-seq analyses that apply linear models to log-transformed counts, such as those in the limma software package, and more generally to any count-based GLM where exactly zero fitted values are possible.

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WemIQ: an accurate and robust isoform quantification method for RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btu757 ◽

2014 ◽

Vol 31 (6) ◽

pp. 878-885 ◽

Cited By ~ 13

Author(s):

Jing Zhang ◽

C.-C. Jay Kuo ◽

Liang Chen

Keyword(s):

Rna Seq ◽

Quantification Method ◽

Isoform Quantification

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Quantile regression for challenging cases of eQTL mapping

Briefings in Bioinformatics ◽

10.1093/bib/bbz097 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1756-1765

Author(s):

Bo Sun ◽

Liang Chen

Keyword(s):

Quantile Regression ◽

Large Scale ◽

High Throughput Sequencing ◽

Linear Models ◽

Real Data ◽

Type I ◽

Eqtl Mapping ◽

Size Estimation ◽

Rna Seq ◽

The Impact

Abstract Mapping of expression quantitative trait loci (eQTLs) facilitates interpretation of the regulatory path from genetic variants to their associated disease or traits. High-throughput sequencing of RNA (RNA-seq) has expedited the exploration of these regulatory variants. However, eQTL mapping is usually confronted with the analysis challenges caused by overdispersion and excessive dropouts in RNA-seq. The heavy-tailed distribution of gene expression violates the assumption of Gaussian distributed errors in linear regression for eQTL detection, which results in increased Type I or Type II errors. Applying rank-based inverse normal transformation (INT) can make the expression values more normally distributed. However, INT causes information loss and leads to uninterpretable effect size estimation. After comprehensive examination of the impact from overdispersion and excessive dropouts, we propose to apply a robust model, quantile regression, to map eQTLs for genes with high degree of overdispersion or large number of dropouts. Simulation studies show that quantile regression has the desired robustness to outliers and dropouts, and it significantly improves eQTL mapping. From a real data analysis, the most significant eQTL discoveries differ between quantile regression and the conventional linear model. Such discrepancy becomes more prominent when the dropout effect or the overdispersion effect is large. All the results suggest that quantile regression provides more reliable and accurate eQTL mapping than conventional linear models. It deserves more attention for the large-scale eQTL mapping.

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Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2016-0008 ◽

2016 ◽

Vol 15 (6) ◽

Cited By ~ 1

Author(s):

Chung-I Li ◽

Yu Shyr

Keyword(s):

Sample Size ◽

Linear Models ◽

Negative Binomial ◽

Differential Expression Analysis ◽

Sample Size Calculation ◽

Rna Seq ◽

Optimal Sample Size ◽

Optimal Sample ◽

Gene Count ◽

Downstream Analysis

AbstractAs RNA-seq rapidly develops and costs continually decrease, the quantity and frequency of samples being sequenced will grow exponentially. With proteomic investigations becoming more multivariate and quantitative, determining a study’s optimal sample size is now a vital step in experimental design. Current methods for calculating a study’s required sample size are mostly based on the hypothesis testing framework, which assumes each gene count can be modeled through Poisson or negative binomial distributions; however, these methods are limited when it comes to accommodating covariates. To address this limitation, we propose an estimating procedure based on the generalized linear model. This easy-to-use method constructs a representative exemplary dataset and estimates the conditional power, all without requiring complicated mathematical approximations or formulas. Even more attractive, the downstream analysis can be performed with current R/Bioconductor packages. To demonstrate the practicability and efficiency of this method, we apply it to three real-world studies, and introduce our on-line calculator developed to determine the optimal sample size for a RNA-seq study.

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LIQA: long-read isoform quantification and analysis

Genome Biology ◽

10.1186/s13059-021-02399-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yu Hu ◽

Li Fang ◽

Xuelian Chen ◽

Jiang F. Zhong ◽

Mingyao Li ◽

...

Keyword(s):

Alternative Splicing ◽

Myeloid Leukemia ◽

Expectation Maximization Algorithm ◽

Read Length ◽

Specific Gene ◽

Rna Seq ◽

Sequencing Data ◽

Long Read ◽

Isoform Quantification ◽

Universal Human Reference

AbstractLong-read RNA sequencing (RNA-seq) technologies can sequence full-length transcripts, facilitating the exploration of isoform-specific gene expression over short-read RNA-seq. We present LIQA to quantify isoform expression and detect differential alternative splicing (DAS) events using long-read direct mRNA sequencing or cDNA sequencing data. LIQA incorporates base pair quality score and isoform-specific read length information in a survival model to assign different weights across reads, and uses an expectation-maximization algorithm for parameter estimation. We apply LIQA to long-read RNA-seq data from the Universal Human Reference, acute myeloid leukemia, and esophageal squamous epithelial cells and demonstrate its high accuracy in profiling alternative splicing events.

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Bioinformatics Tools for RNA-seq Gene and Isoform Quantification

Journal of Next Generation Sequencing & Applications ◽

10.4172/2469-9853.1000140 ◽

2016 ◽

Vol 03 (03) ◽

Cited By ~ 2

Author(s):

Chi Zhang ◽

Baohong Zhang ◽

Michael S Vincent ◽

Shanrong Zhao

Keyword(s):

Rna Seq ◽

Bioinformatics Tools ◽

Isoform Quantification

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A large-scale comparative study of isoform expressions measured on four platforms

10.21203/rs.2.11536/v2 ◽

2019 ◽

Author(s):

Wei Zhang ◽

Raphael Petegrosso ◽

Jae Woong Chang ◽

Jiao Sun ◽

Jeongsik Yong ◽

...

Keyword(s):

Gene Expression ◽

Comparative Study ◽

Cell Lines ◽

Cancer Cell ◽

Large Scale ◽

Exon Array ◽

Cancer Cell Lines ◽

Rna Seq ◽

Gene Expressions ◽

Isoform Quantification

Abstract Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA-sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 403 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for evaluating expressions at both isoform and gene levels, we found that (1) the degree of agreement across platforms on quantifying isoform expressions is lower than gene expressions; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array quantification results on isoform-level compared to NanoString; (4) different RNA-seq isoform quantification algorithms showed inconsistent results, and two isoform quantification methods Net-RSTQ and eXpress are more consistent across the platforms in the comparison; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

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