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A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq data

Kefei Liu, Jieping Ye, Yang Yang, Li Shen, Hui Jiang
2018 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
We propose a unified statistical model for joint normalization and DE detection of log-transformed RNA-seq data.  ...  The RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels.  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.  ... 
doi:10.1109/tcbb.2018.2790918 pmid:29993952 fatcat:tbmkealgprdfled4somfjfgtzi

BADGE: A novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data

Jinghua Gu, Xiao Wang, Leena Halakivi-Clarke, Robert Clarke, Jianhua Xuan
2014 BMC Bioinformatics  
A novel Bayesian framework is developed for joint estimate of gene level mRNA abundance and differential state, which models the intrinsic variability in RNA-Seq to improve the estimation.  ...  However, precise quantification of mRNA abundance and identification of differentially expressed genes are complicated due to biological and technical variations in RNA-Seq data.  ...  BADGE is a unified computational method that extensively models variability in RNA-seq data to improve abundance quantification and DEG identification.  ... 
doi:10.1186/1471-2105-15-s9-s6 pmid:25252852 pmcid:PMC4168709 fatcat:smmaak7zoze6pnkwc7jp4a346q

Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical models

Tianzhou Ma, Faming Liang, George C. Tseng
2016 Journal of the Royal Statistical Society, Series C: Applied Statistics  
In this paper, we propose a full Bayesian hierarchical model (namely, BayesMetaSeq) for RNA-seq meta-analysis by modelling count data, integrating information across genes and across studies, and modelling  ...  Meta-analysis combining multiple transcriptomic studies increases statistical power and accuracy in detecting differentially expressed genes.  ...  Acknowledgments Research reported in this publication was supported by NCI of the National Institutes of Health under award number R01CA190766 to T.M. and G.C.T.  ... 
doi:10.1111/rssc.12199 pmid:28785119 pmcid:PMC5543999 fatcat:xwbgbzxwifd2lawyelqx6jmoli

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Franck Rapaport, Raya Khanin, Yupu Liang, Mono Pirun, Azra Krek, Paul Zumbo, Christopher E Mason, Nicholas D Socci, Doron Betel
2013 Genome Biology  
A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data.  ...  We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression.  ...  Our analysis focused on a number of measures that are most relevant for detection of differential gene expression from RNA-seq data: i) normalization of count data; ii) sensitivity and specificity of DE  ... 
doi:10.1186/gb-2013-14-9-r95 pmid:24020486 pmcid:PMC4054597 fatcat:stna7jnygzb6xcumlipilku3ou

How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review

Sunghee Oh
2015 Communications for Statistical Applications and Methods  
In a short history, RNA-seq data have established a revolutionary tool to directly decode various scenarios occurring on whole genome-wide expression profiles in regards with differential expression at  ...  The remarkable characteristics of highthroughput large-scale expression profile in RNA-seq are lied on expression levels of read counts, structure of correlated samples and genes, larger number of genes  ...  that have not been able to detect in unified gene level quantification.  ... 
doi:10.5351/csam.2015.22.2.181 fatcat:muzxjrnkdrhfhpd4pwrus3k2ee

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

Sunghee Oh, Seongho Song, Gregory Grabowski, Hongyu Zhao, James P. Noonan
2013 BioMed Research International  
Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden  ...  However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems.  ...  Acknowledgments This work was supported in part by NIH GM094780 (J. P. Noonan), GM59507 (H. Zhao) and NSF DMS 1106738 (H. Zhao).  ... 
doi:10.1155/2013/203681 pmid:23586021 pmcid:PMC3622290 fatcat:bojm6ntuwrg2vdzhz5dicq6rla

Biases in differential expression analysis of RNA-seq data: A matter of replicate type [article]

Sora Yoon, Dougu Nam
2015 arXiv   pre-print
In differential expression (DE) analysis of RNA-seq count data, it is known that genes with a larger read number are more likely to be differentially expressed.  ...  In conclusion, the current DE and enrichment analysis methods can be confidently used for biological replicate count data, while caution should be exercised when analysing technical replicate data.  ...  The analysis presented here provides a unified perspective that can explain the known biases in DE analysis and the false positives in gene-set analysis of RNA-seq data.  ... 
arXiv:1508.03719v1 fatcat:vr4jf5bzqrbyfoifndjf4ezuem

A Unified Model for Differential Expression Analysis of RNA-seq Data via L1-Penalized Linear Regression [article]

Kefei Liu, Jieping Ye, Yang Yang, Li Shen, Hui Jiang
2016 arXiv   pre-print
We propose a unified statistical model for joint normalization and DE detection of log-transformed RNA-seq data.  ...  The RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels.  ...  For sake of conciseness, the results are not shown here. DISCUSSION A unified statistical model is proposed for joint betweensample normalization and DE detection of RNA-seq data.  ... 
arXiv:1610.04078v1 fatcat:rxhqzuftrnbabcmhoaxiehzkly

A Hierarchical Bayesian Model for Estimating and Inferring Differential Isoform Expression for Multi-sample RNA-Seq Data

Saran Vardhanabhuti, Mingyao Li, Hongzhe Li
2011 Statistics in Biosciences  
We present in this paper a Poisson-Gamma hierarchical model for multi-sample RNA-Seq data analysis in order to simultaneously estimate isoform-specific expression and to identify differentially expressed  ...  Most of the available methods for RNA-Seq data focus on one sample at a time.  ...  Acknowledgments Supported by NIH grants R01ES009911 and R01CA127334 (H. Li), R01HG004517 and R01HG005854 (M. Li) and T32CA093283 (S. Vardhanabhuti).  ... 
doi:10.1007/s12561-011-9052-3 pmid:23737925 pmcid:PMC3669631 fatcat:d6d36spaqrbkbbbvygbo5frcqm

Unit-free and robust detection of differential expression from RNA-Seq data [article]

Hui Jiang, Tianyu Zhan
2016 arXiv   pre-print
In this paper we propose a unified statistical model for joint detection of differential gene expression and between-sample normalization.  ...  Due to the L0-penalized likelihood used in our model, it is able to reliably normalize the data and detect differential gene expression in some cases when more than 50% of the genes are differentially  ...  In this paper, we will propose a unified statistical model for joint detection of differential gene expression and between-sample normalization.  ... 
arXiv:1405.4538v3 fatcat:wumecdy7zfgtzblso3o6pupfxq

A Poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology

Weixing Feng, Yunlong Liu, Jiejun Wu, Kenneth P Nephew, Tim HM Huang, Lang Li
2008 BMC Genomics  
We present a mixture model-based analysis for identifying differences in the distribution of RNA polymerase II (Pol II) in transcribed regions, measured using ChIP-seq (chromatin immunoprecipitation following  ...  These results show that a Poisson mixture model can be used to analyze ChIP-seq data.  ...  In addition, they developed a log-normal model for the observational component and a normal model for the prior component.  ... 
doi:10.1186/1471-2164-9-s2-s23 pmid:18831789 pmcid:PMC2559888 fatcat:uoe2nwyv5nagpavfj2zv62kt4y

Computational resources for ribosome profiling: from database to Web server and software

Hongwei Wang, Yan Wang, Zhi Xie
2017 Briefings in Bioinformatics  
Here, we survey the recent computational advances guided by ribosome profiling, with a focus on databases, Web servers and software tools for storing, visualizing and analyzing ribosome profiling data.  ...  This review is intended to provide experimental and computational biologists with a reference to make appropriate choices among existing resources for the question at hand.  ...  approach for ribo-seq normalization [ Differential translation detection anota An R/Bioconductor package for applying per-gene analysis of partial variance coupled with variance shrinkage to identify  ... 
doi:10.1093/bib/bbx093 pmid:28968766 fatcat:uq5zk3rkhvhvlkrbtfjevc5uim

BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

Yuanhua Huang, Guido Sanguinetti
2021 Genome Biology  
RNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics.  ...  We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and  ...  Detecting differential splicing events or differential momentum genes BRIE2 (Mode 2-diff ), in a unified way, allows to detection of differential alternative splicing events or differential momentum genes  ... 
doi:10.1186/s13059-021-02461-5 pmid:34452629 pmcid:PMC8393734 fatcat:isvn3s65erh6nizmjagj2an4ke

Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data

Jean Fan, Hae-Ock Lee, Soohyun Lee, Da-eun Ryu, Semin Lee, Catherine Xue, Seok Jin Kim, Kihyun Kim, Nikolaos Barkas, Peter J. Park, Woong-Yang Park, Peter V. Kharchenko
2018 Genome Research  
and loss of heterozygosity in individual cells from single-cell RNA-sequencing data.  ...  By integrating allele and normalized expression information, HoneyBADGER is able to identify and infer the presence of subclone-specific alterations in individual cells and reconstruct the underlying subclonal  ...  Acknowledgments We thank Patrik Ernfors for helpful feedback on the manuscript. J.F. was supported by an NIH grant F99 CA222750-01. P.V.K. was supported by NIH R01HL131768 from NHLBI and CAREER  ... 
doi:10.1101/gr.228080.117 pmid:29898899 fatcat:ie6vpbakubaudmdrjwnuaemqri

Normalizing RNA-Sequencing Data by Modeling Hidden Covariates with Prior Knowledge

Sara Mostafavi, Alexis Battle, Xiaowei Zhu, Alexander E. Urban, Douglas Levinson, Stephen B. Montgomery, Daphne Koller, Panayiotis V. Benos
2013 PLoS ONE  
Our experiments demonstrate that accounting for known and hidden factors with appropriate models improves the quality of RNAsequencing data in two very different tasks: detecting genetic variations that  ...  Here, we consider the problem of modeling and removing the effects of known and hidden confounding factors from RNA-sequencing data.  ...  Normalizing RNA-seq measurements to account for both known and hidden biases is a central task in the analysis of such data.  ... 
doi:10.1371/journal.pone.0068141 pmid:23874524 pmcid:PMC3715474 fatcat:pfu5hbwwgjeijdc3lfg32al3xi
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