SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data.
TLDR
SwarnSeq as mentioned in this paper is an improved method for DE, and other downstream analysis that considers the molecular capture process in scRNA-seq data modeling, which has improved performance over the 11 existing methods.About:
This article is published in Genomics.The article was published on 2021-03-01 and is currently open access. It has received 7 citations till now. The article focuses on the topics: Data modeling.read more
Citations
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Journal ArticleDOI
Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose–response study designs
Rance Nault,Satabdi Saha,Sudin Bhattacharya,Jack P. Dodson,Samiran Sinha,Tapabrata Maiti,Timothy R. Zacharewski +6 more
TL;DR: To benchmark DGEA methods for dose–response scRNAseq experiments, a multiplicity corrected Bayesian testing approach is proposed and compared against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data.
Journal ArticleDOI
Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges
TL;DR: The underlying statistical principles of the differential expression analysis approaches are critically discussed and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches.
Posted ContentDOI
Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs.
Rance Nault,Satabdi Saha,Sudin Bhattacharya,Jack P. Dodson,Samiran Sinha,Tapabrata Maiti,Timothy R. Zacharewski +6 more
TL;DR: In this article, the authors proposed a multiplicity corrected Bayesian testing approach and compared it with 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data.
Journal ArticleDOI
Statistical Methods for Analysis of single-cell RNA-sequencing Data
TL;DR: In this paper, a novel statistical approach for various analyses of the scRNA-seq Unique Molecular Identifier (UMI) counts data is presented, which considers the biological process that leads to severe dropout events in modeling the observed UMI counts of the genes.
Journal ArticleDOI
UIPBC: An effective clustering for scRNA-seq data analysis without user input
TL;DR: Wadhury et al. as mentioned in this paper proposed an effective clustering method, which integrates required preprocessing steps for data cleaning, followed by robust cell group identification method from scRNA-seq data.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Journal ArticleDOI
limma powers differential expression analyses for RNA-sequencing and microarray studies
Matthew E. Ritchie,Belinda Phipson,Di Wu,Yifang Hu,Charity W. Law,Wei Shi,Gordon K. Smyth,Gordon K. Smyth +7 more
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Journal ArticleDOI
Differential expression analysis for sequence count data.
Simon Anders,Wolfgang Huber +1 more
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.
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