Cargando…

DGEclust: differential expression analysis of clustered count data

We present a statistical methodology, DGEclust, for differential expression analysis of digital expression data. Our method treats differential expression as a form of clustering, thus unifying these two concepts. Furthermore, it simultaneously addresses the problem of how many clusters are supporte...

Descripción completa

Detalles Bibliográficos
Autores principales:	Vavoulis, Dimitrios V, Francescatto, Margherita, Heutink, Peter, Gough, Julian
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2015
Materias:	Method
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4365804/ https://www.ncbi.nlm.nih.gov/pubmed/25853652 http://dx.doi.org/10.1186/s13059-015-0604-6

Ejemplares similares

Differential expression analysis for sequence count data
por: Anders, Simon, et al.
Publicado: (2010)

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership
por: Carbonetto, Peter, et al.
Publicado: (2023)

UMI-count modeling and differential expression analysis for single-cell RNA sequencing
por: Chen, Wenan, et al.
Publicado: (2018)

Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts
por: Ntranos, Vasilis, et al.
Publicado: (2016)

Consensus clustering and functional interpretation of gene-expression data
por: Swift, Stephen, et al.
Publicado: (2004)

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data
por: Gupta, Krishan, et al.
Publicado: (2021)

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

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

Clustering analysis of SAGE data using a Poisson approach
por: Cai, Li, et al.
Publicado: (2004)

Nonparametric expression analysis using inferential replicate counts
por: Zhu, Anqi, et al.
Publicado: (2019)

IDEAS: individual level differential expression analysis for single-cell RNA-seq data
por: Zhang, Mengqi, et al.
Publicado: (2022)

Flimma: a federated and privacy-aware tool for differential gene expression analysis
por: Zolotareva, Olga, et al.
Publicado: (2021)

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
por: Sun, Tianyi, et al.
Publicado: (2021)

The ‘dnet’ approach promotes emerging research on cancer patient survival
por: Fang, Hai, et al.
Publicado: (2014)

Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
por: L. Lun, Aaron T., et al.
Publicado: (2016)

Exploratory differential gene expression analysis in microarray experiments with no or limited replication
por: Loguinov, Alexander V, et al.
Publicado: (2004)

scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data
por: Wang, Zongqin, et al.
Publicado: (2023)

Differential analysis of high-throughput quantitative genetic interaction data
por: Bean, Gordon J, et al.
Publicado: (2012)

Reconstructing differentiation networks and their regulation from time series single-cell expression data
por: Ding, Jun, et al.
Publicado: (2018)

Gclust: A Parallel Clustering Tool for Microbial Genomic Data
por: Li, Ruilin, et al.
Publicado: (2019)

HBA-DEALS: accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis
por: Karlebach, Guy, et al.
Publicado: (2020)

Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering
por: Fang, Jiyuan, et al.
Publicado: (2022)

MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering
por: Kim, Chanwoo, et al.
Publicado: (2022)

Copy-number-aware differential analysis of quantitative DNA sequencing data
por: Robinson, Mark D., et al.
Publicado: (2012)

GeneCount: genome-wide calculation of absolute tumor DNA copy numbers from array comparative genomic hybridization data
por: Lyng, Heidi, et al.
Publicado: (2008)

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data
por: Thibodeau, Asa, et al.
Publicado: (2021)

CSS: cluster similarity spectrum integration of single-cell genomics data
por: He, Zhisong, et al.
Publicado: (2020)

voom: precision weights unlock linear model analysis tools for RNA-seq read counts
por: Law, Charity W, et al.
Publicado: (2014)

How to normalize metatranscriptomic count data for differential expression analysis
por: Klingenberg, Heiner, et al.
Publicado: (2017)

LinDA: linear models for differential abundance analysis of microbiome compositional data
por: Zhou, Huijuan, et al.
Publicado: (2022)

C9orf72 is differentially expressed in the central nervous system and myeloid cells and consistently reduced in C9orf72, MAPT and GRN mutation carriers
por: Rizzu, Patrizia, et al.
Publicado: (2016)

Impulse model-based differential expression analysis of time course sequencing data
por: Fischer, David S, et al.
Publicado: (2018)

Ultrafast clustering of single-cell flow cytometry data using FlowGrid
por: Ye, Xiaoxin, et al.
Publicado: (2019)

Classification and clustering of RNA crosslink-ligation data reveal complex structures and homodimers
por: Zhang, Minjie, et al.
Publicado: (2022)

RADAR: differential analysis of MeRIP-seq data with a random effect model
por: Zhang, Zijie, et al.
Publicado: (2019)

Corset: enabling differential gene expression analysis for de novo assembled transcriptomes
por: Davidson, Nadia M, et al.
Publicado: (2014)

Tools and best practices for data processing in allelic expression analysis
por: Castel, Stephane E., et al.
Publicado: (2015)

OrthoClust: an orthology-based network framework for clustering data across multiple species
por: Yan, Koon-Kiu, et al.
Publicado: (2014)

ComBat-seq: batch effect adjustment for RNA-seq count data
por: Zhang, Yuqing, et al.
Publicado: (2020)

Searching for differentially expressed gene combinations
por: Dettling, Marcel, et al.
Publicado: (2005)

Cannot write session to /tmp/vufind_sessions/sess_3iq0uoc0tg8le8hcsn3unpe6m9