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      Statistical methods for analysis of single-cell RNA-sequencing data

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          Abstract

          Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput genomic technology used to study the expression dynamics of genes at single-cell level. Analyzing the scRNA-seq data in presence of biological confounding factors including dropout events is a challenging task. Thus, this article presents a novel statistical approach for various analyses of the scRNA-seq Unique Molecular Identifier (UMI) counts data. The various analyses include modeling and fitting of observed UMI data, cell type detection, estimation of cell capture rates, estimation of gene specific model parameters, estimation of the sample mean and sample variance of the genes, etc. Besides, the developed approach is able to perform differential expression, and other downstream analyses that consider the molecular capture process in scRNA-seq data modeling. Here, the external spike-ins data can also be used in the approach for better results. The unique feature of the method is that it considers the biological process that leads to severe dropout events in modeling the observed UMI counts of genes.

          • The differential expression analysis of observed scRNA-seq UMI counts data is performed after adjustment for cell capture rates.

          • The statistical approach performs downstream differential zero inflation analysis, classification of influential genes, and selection of top marker genes.

          • Cell auxiliaries including cell clusters and other cell variables ( e.g., cell cycle, cell phase) are used to remove unwanted variation to perform statistical tests reliably.

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          Comparative Analysis of Single-Cell RNA Sequencing Methods.

          Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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            Maximum Likelihood from Incomplete Data Via the EM Algorithm

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              Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

              Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq. Electronic supplementary material The online version of this article (10.1186/s13059-018-1406-4) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                MethodsX
                MethodsX
                MethodsX
                Elsevier
                2215-0161
                17 November 2021
                2021
                17 November 2021
                : 8
                Affiliations
                [a ]Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India
                [b ]Biostatistics and Bioinformatics Facility, JG Brown Cancer Center, University of Louisville, Louisville, KY 40202, USA
                [c ]School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY 40292, USA
                [d ]Hepatobiology and Toxicology Center, University of Louisville, Louisville, KY 40202, USA
                [e ]Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA
                [f ]Biostatistics and Informatics Facility, Center for Integrative Environmental Research Sciences, University of Louisville, Louisville, KY 40202, USA
                [g ]Christina Lee Brown Envirome Institute, University of Louisville, Louisville, KY 40202, USA
                Author notes
                [* ]Corresponding author at: Biostatistics and Bioinformatics Facility, JG Brown Cancer Center, University of Louisville, Louisville, KY 40202, USA. shesh.rai@ 123456louisville.edu
                Article
                S2215-0161(21)00370-8 101580
                10.1016/j.mex.2021.101580
                8720898
                76241312-b64e-47a8-a5a8-3faad8a2fb7f
                Published by Elsevier B.V.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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