Cargando…

A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data

Next-generation sequencing technologies have made RNA sequencing (RNA-seq) a popular choice for measuring gene expression level. To reduce the noise of gene expression measures and compare them between several conditions or samples, normalization is an essential step to adjust for varying sample seq...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhou, Yan, Wang, Guochang, Zhang, Jun, Li, Han
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2017
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5224994/ https://www.ncbi.nlm.nih.gov/pubmed/28072846 http://dx.doi.org/10.1371/journal.pone.0169594

Ejemplares similares

An iteration normalization and test method for differential expression analysis of RNA-seq data
por: Zhou, Yan, et al.
Publicado: (2014)

Erratum: An iteration normalization and test method for differential expression analysis of RNA-seq data
por: Zhou, Yan, et al.
Publicado: (2014)

A statistical normalization method and differential expression analysis for RNA-seq data between different species
por: Zhou, Yan, et al.
Publicado: (2019)

A scaling normalization method for differential expression analysis of RNA-seq data
por: Robinson, Mark D, et al.
Publicado: (2010)

Fused inverse-normal method for integrated differential expression analysis of RNA-seq data
por: Prasad, Birbal, et al.
Publicado: (2022)

An integrative method to normalize RNA-Seq data
por: Filloux, Cyril, et al.
Publicado: (2014)

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
por: Bullard, James H, et al.
Publicado: (2010)

clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution
por: Li, Hongxiang, et al.
Publicado: (2023)

The Impact of Normalization Methods on RNA-Seq Data Analysis
por: Zyprych-Walczak, J., et al.
Publicado: (2015)

Stability of methods for differential expression analysis of RNA-seq data
por: Lin, Bingqing, et al.
Publicado: (2019)

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data
por: Li, Peipei, et al.
Publicado: (2015)

Model-based compound hypothesis testing for snATAC-seq data with PACS
por: Miao, Zhen, et al.
Publicado: (2023)

Statistical methods on detecting differentially expressed genes for RNA-seq data
por: Chen, Zhongxue, et al.
Publicado: (2011)

A comparison of methods for differential expression analysis of RNA-seq data
por: Soneson, Charlotte, et al.
Publicado: (2013)

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods
por: Quinn, Thomas P., et al.
Publicado: (2018)

Differential Expression Analysis for RNA-Seq Data
por: Gupta, Rashi, et al.
Publicado: (2012)

Variance component testing for identifying differentially expressed genes in RNA-seq data
por: Yang, Sheng, et al.
Publicado: (2017)

A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data
por: Li, Xiaohong, et al.
Publicado: (2017)

A Zipf-plot based normalization method for high-throughput RNA-seq data
por: Wang, Bin
Publicado: (2020)

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
por: Rapaport, Franck, et al.
Publicado: (2013)

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data
por: Gao, Zhihua, et al.
Publicado: (2018)

Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey
por: Lytal, Nicholas, et al.
Publicado: (2020)

Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data
por: Li, Chung-I, et al.
Publicado: (2013)

The Level of Residual Dispersion Variation and the Power of Differential Expression Tests for RNA-Seq Data
por: Mi, Gu, et al.
Publicado: (2015)

Differential expression analysis of RNA-seq data at single-base resolution
por: Frazee, Alyssa C., et al.
Publicado: (2014)

A graph-based algorithm for RNA-seq data normalization
por: Tran, Diem-Trang, et al.
Publicado: (2020)

A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data
por: Zhou, Yan, et al.
Publicado: (2021)

On Differential Gene Expression Using RNA-Seq Data
por: Lee, Juhee, et al.
Publicado: (2011)

Differential expression analysis for paired RNA-seq data
por: Chung, Lisa M, et al.
Publicado: (2013)

GC-Content Normalization for RNA-Seq Data
por: Risso, Davide, et al.
Publicado: (2011)

An Integrated Approach for RNA-seq Data Normalization
por: Yang, Shengping, et al.
Publicado: (2016)

NBBt-test: a versatile method for differential analysis of multiple types of RNA-seq data
por: Tan, Yuan-De, et al.
Publicado: (2022)

Evaluation of methods for differential expression analysis on multi-group RNA-seq count data
por: Tang, Min, et al.
Publicado: (2015)

Erratum to: Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
por: Rapaport, Franck, et al.
Publicado: (2015)

Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data
por: Baik, Bukyung, et al.
Publicado: (2020)

Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster
por: Lin, Yanzhu, et al.
Publicado: (2016)

rSeqDiff: Detecting Differential Isoform Expression from RNA-Seq Data Using Hierarchical Likelihood Ratio Test
por: Shi, Yang, et al.
Publicado: (2013)

Corrigendum: Differential expression analysis of RNA-seq data at single-base resolution
por: Frazee, Alyssa C., et al.
Publicado: (2014)

Assessment of Single Cell RNA-Seq Normalization Methods
por: Ding, Bo, et al.
Publicado: (2017)

Novel Data Transformations for RNA-seq Differential Expression Analysis
por: Zhang, Zeyu, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_hpj35golqci462751fo3pvav8d