Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods

Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods

The sequencing of the transcriptomes of single-cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types and for the study of stochastic gene expression. In recent years, various tools for analyzing single-cell RNA-sequencing data have be...

Full description

Bibliographic Details
Main Authors:	Alessandra Dal Molin, Giacomo Baruzzo, Barbara Di Camillo
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2017-05-01
Series:	Frontiers in Genetics
Subjects:	single-cell RNA-seq differential expression differential distributions benchmark assessment
Online Access:	http://journal.frontiersin.org/article/10.3389/fgene.2017.00062/full

Similar Items

Impact of human gene annotations on RNA-seq differential expression analysis
by: Yu Hamaguchi, et al.
Published: (2021-10-01)

The shaky foundations of simulating single-cell RNA sequencing data
by: Helena L. Crowell, et al.
Published: (2023-03-01)

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking
by: Jake Gagnon, et al.
Published: (2022-06-01)

IDEAS: individual level differential expression analysis for single-cell RNA-seq data
by: Mengqi Zhang, et al.
Published: (2022-01-01)

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis
by: Zhuorui Xie, et al.
Published: (2023-11-01)

A comparison of Illumina and Ion Torrent sequencing platforms in the context of differential gene expression
by: Nicholas F. Lahens, et al.
Published: (2017-08-01)

LFCseq: a nonparametric approach for differential expression analysis of RNA-seq data
by: Lin, Bingqing, et al.
Published: (2015)

FastCAR: fast correction for ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets
by: Marijn Berg, et al.
Published: (2023-11-01)

A benchmark of batch-effect correction methods for single-cell RNA sequencing data
by: Hoa Thi Nhu Tran, et al.
Published: (2020-01-01)

scMEB: a fast and clustering-independent method for detecting differentially expressed genes in single-cell RNA-seq data
by: Jiadi Zhu, et al.
Published: (2023-05-01)

M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data
by: Yu Zhang, et al.
Published: (2019-12-01)

Reproducibility of Methods to Detect Differentially Expressed Genes from Single-Cell RNA Sequencing
by: Tian Mou, et al.
Published: (2020-01-01)

Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
by: Jennifer Westoby, et al.
Published: (2018-11-01)

A systematic evaluation of single-cell RNA-sequencing imputation methods
by: Wenpin Hou, et al.
Published: (2020-08-01)

RNA-seq Reveals Differentially Expressed Genes between Two <i>indica</i> Inbred Rice Genotypes Associated with Drought-Yield QTLs
by: Nelzo C. Ereful, et al.
Published: (2020-04-01)

A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data
by: Hongyu Li, et al.
Published: (2021-10-01)

Fused inverse-normal method for integrated differential expression analysis of RNA-seq data
by: Birbal Prasad, et al.
Published: (2022-08-01)

A Double-weighted Normalization Method for Identifying Differential Expression of RNA-seq Data
by: Xiaohui Wu, et al.
Published: (2014-12-01)

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership
by: Peter Carbonetto, et al.
Published: (2023-10-01)

The bench scientist's guide to statistical analysis of RNA-Seq data
by: Yendrek Craig R, et al.
Published: (2012-09-01)

Integrative Differential Expression Analysis for Multiple EXperiments (IDEAMEX): A Web Server Tool for Integrated RNA-Seq Data Analysis
by: Verónica Jiménez-Jacinto, et al.
Published: (2019-03-01)

Best practices on the differential expression analysis of multi-species RNA-seq
by: Matthew Chung, et al.
Published: (2021-04-01)

Robustness of differential gene expression analysis of RNA-seq
by: A. Stupnikov, et al.
Published: (2021-01-01)

Error estimates for the analysis of differential expression from RNA-seq count data
by: Conrad J. Burden, et al.
Published: (2014-09-01)

Effect of method of deduplication on estimation of differential gene expression using RNA-seq
by: Anna V. Klepikova, et al.
Published: (2017-03-01)

A Two-Part Mixed Model for Differential Expression Analysis in Single-Cell High-Throughput Gene Expression Data
by: Yang Shi, et al.
Published: (2022-02-01)

RNA-Sequencing Reveals Upregulation and a Beneficial Role of Autophagy in Myoblast Differentiation and Fusion
by: Pengcheng Lyu, et al.
Published: (2022-11-01)

<i>De novo</i> Sequencing and Analysis of <i>Salvia hispanica</i> Tissue-Specific Transcriptome and Identification of Genes Involved in Terpenoid Biosynthesis
by: James Wimberley, et al.
Published: (2020-03-01)

RNA sequencing identifies novel non-coding RNA and exon-specific effects associated with cigarette smoking
by: Margaret M. Parker, et al.
Published: (2017-10-01)

Simplicity DiffExpress: A Bespoke Cloud-Based Interface for RNA-seq Differential Expression Modeling and Analysis
by: Cintia C. Palu, et al.
Published: (2019-05-01)

Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types
by: Tianyuan Lu, et al.
Published: (2020-11-01)

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data
by: Takayuki Osabe, et al.
Published: (2021-10-01)

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges
by: Samarendra Das, et al.
Published: (2022-07-01)

Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing
by: Robles José A, et al.
Published: (2012-09-01)

Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data
by: Matteo Calgaro, et al.
Published: (2020-08-01)

Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection
by: Thomas Eder, et al.
Published: (2022-05-01)

A statistical approach for identifying primary substrates of ZSWIM8-mediated microRNA degradation in small-RNA sequencing data
by: Peter Y. Wang, et al.
Published: (2023-05-01)

Differentially expressed genes from RNA-Seq and functional enrichment results are affected by the choice of single-end versus paired-end reads and stranded versus non-stranded protocols
by: Susan M. Corley, et al.
Published: (2017-05-01)

One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data
by: Chloe X. Wang, et al.
Published: (2022-04-01)

A comparison of automatic cell identification methods for single-cell RNA sequencing data
by: Tamim Abdelaal, et al.
Published: (2019-09-01)