Dataset Information

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

ABSTRACT: The deconvolution of isoform expression from RNA-seq remains challenging because of non-uniform read sampling and subtle differences among isoforms.We present a weighted-log-likelihood expectation maximization method on isoform quantification (WemIQ). WemIQ integrates an effective bias removal with a weighted expectation maximization (EM) algorithm to distribute reads among isoforms efficiently. The weight represents the oversampling or undersampling of sequence reads and is estimated through a generalized Poisson model without any presumption on the bias sources and formats. WemIQ significantly improves the quantification of isoform and gene expression as well as the derived exon inclusion rates. It provides robust expression estimates across different laboratories and protocols, which is valuable for the integrative analysis of RNA-seq. For the recent single-cell RNA-seq data, WemIQ also provides the opportunity to distinguish bias heterogeneity from true biological heterogeneity and uncovers smaller cell-to-cell expression variability.

SUBMITTER: Zhang J

PROVIDER: S-EPMC4380033 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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

Zhang Jing J Kuo C-C Jay CC Chen Liang L

Bioinformatics (Oxford, England) 20141117 6

<h4>Motivation</h4>The deconvolution of isoform expression from RNA-seq remains challenging because of non-uniform read sampling and subtle differences among isoforms.<h4>Results</h4>We present a weighted-log-likelihood expectation maximization method on isoform quantification (WemIQ). WemIQ integrates an effective bias removal with a weighted expectation maximization (EM) algorithm to distribute reads among isoforms efficiently. The weight represents the oversampling or undersampling of sequenc ...[more]

PMID: 25406327

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Project description:Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

Project description:BackgroundHigh throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here we apply robust statistics on RNA-seq data analysis.ResultsWe report the use of two robust principal component analysis (rPCA) methods, PcaHubert and PcaGrid, to detect outlier samples in multiple simulated and real biological RNA-seq data sets with positive control outlier samples. PcaGrid achieved 100% sensitivity and 100% specificity in all the tests using positive control outliers with varying degrees of divergence. We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granule layer in the cerebellum of control and conditional SnoN knockout mice. Both rPCA methods detected the same two outlier samples but cPCA failed to detect any. We performed differentially expressed gene detection before and after outlier removal as well as with and without batch effect modeling. We validated gene expression changes using quantitative reverse transcription PCR and used the result as reference to compare the performance of eight different data analysis strategies. Removing outliers without batch effect modeling performed the best in term of detecting biologically relevant differentially expressed genes.ConclusionsrPCA implemented in the PcaGrid function is an accurate and objective method to detect outlier samples. It is well suited for high-dimensional data with small sample sizes like RNA-seq data. Outlier removal can significantly improve the performance of differential gene detection and downstream functional analysis.

Dataset Information

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

Publications

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

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