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A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq data
2018
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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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
2014
BMC Bioinformatics
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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
2016
Journal of the Royal Statistical Society, Series C: Applied Statistics
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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
2013
Genome Biology
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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
2015
Communications for Statistical Applications and Methods
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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
2013
BioMed Research International
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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]
2015
arXiv
pre-print
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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]
2016
arXiv
pre-print
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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
2011
Statistics in Biosciences
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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]
2016
arXiv
pre-print
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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
2008
BMC Genomics
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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
2017
Briefings in Bioinformatics
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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
2021
Genome Biology
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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
2018
Genome Research
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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
2013
PLoS ONE
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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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