Literature DB >> 33722930

How to Get Started with Single Cell RNA Sequencing Data Analysis.

Michael S Balzer1,2,3, Ziyuan Ma1,2,3, Jianfu Zhou1,2,3, Amin Abedini1,2,3, Katalin Susztak4,2,3.   

Abstract

Over the last 5 years, single cell methods have enabled the monitoring of gene and protein expression, genetic, and epigenetic changes in thousands of individual cells in a single experiment. With the improved measurement and the decreasing cost of the reactions and sequencing, the size of these datasets is increasing rapidly. The critical bottleneck remains the analysis of the wealth of information generated by single cell experiments. In this review, we give a simplified overview of the analysis pipelines, as they are typically used in the field today. We aim to enable researchers starting out in single cell analysis to gain an overview of challenges and the most commonly used analytical tools. In addition, we hope to empower others to gain an understanding of how typical readouts from single cell datasets are presented in the published literature.
Copyright © 2021 by the American Society of Nephrology.

Entities:  

Keywords:  analysis; kidney; single cell RNA-sequencing; transcriptomics

Mesh:

Year:  2021        PMID: 33722930      PMCID: PMC8259643          DOI: 10.1681/ASN.2020121742

Source DB:  PubMed          Journal:  J Am Soc Nephrol        ISSN: 1046-6673            Impact factor:   14.978


  87 in total

1.  SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data.

Authors:  Matthew D Young; Sam Behjati
Journal:  Gigascience       Date:  2020-12-26       Impact factor: 6.524

2.  Analysis of gene expression in single live neurons.

Authors:  J Eberwine; H Yeh; K Miyashiro; Y Cao; S Nair; R Finnell; M Zettel; P Coleman
Journal:  Proc Natl Acad Sci U S A       Date:  1992-04-01       Impact factor: 11.205

3.  Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data.

Authors:  Hannah A Pliner; Jonathan S Packer; José L McFaline-Figueroa; Darren A Cusanovich; Riza M Daza; Delasa Aghamirzaie; Sanjay Srivatsan; Xiaojie Qiu; Dana Jackson; Anna Minkina; Andrew C Adey; Frank J Steemers; Jay Shendure; Cole Trapnell
Journal:  Mol Cell       Date:  2018-08-02       Impact factor: 17.970

4.  (Re)Building a Kidney.

Authors:  Leif Oxburgh; Thomas J Carroll; Ondine Cleaver; Daniel R Gossett; Deborah K Hoshizaki; Jeffrey A Hubbell; Benjamin D Humphreys; Sanjay Jain; Jan Jensen; David L Kaplan; Carl Kesselman; Christian J Ketchum; Melissa H Little; Andrew P McMahon; Stuart J Shankland; Jason R Spence; M Todd Valerius; Jason A Wertheim; Oliver Wessely; Ying Zheng; Iain A Drummond
Journal:  J Am Soc Nephrol       Date:  2017-01-17       Impact factor: 10.121

5.  Hierarchical progressive learning of cell identities in single-cell data.

Authors:  Lieke Michielsen; Marcel J T Reinders; Ahmed Mahfouz
Journal:  Nat Commun       Date:  2021-05-14       Impact factor: 14.919

6.  Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

Authors:  Kelly Street; Davide Risso; Russell B Fletcher; Diya Das; John Ngai; Nir Yosef; Elizabeth Purdom; Sandrine Dudoit
Journal:  BMC Genomics       Date:  2018-06-19       Impact factor: 3.969

7.  Determining cell type abundance and expression from bulk tissues with digital cytometry.

Authors:  Aaron M Newman; Chloé B Steen; Chih Long Liu; Andrew J Gentles; Aadel A Chaudhuri; Florian Scherer; Michael S Khodadoust; Mohammad S Esfahani; Bogdan A Luca; David Steiner; Maximilian Diehn; Ash A Alizadeh
Journal:  Nat Biotechnol       Date:  2019-05-06       Impact factor: 54.908

8.  Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.

Authors:  Christoph Hafemeister; Rahul Satija
Journal:  Genome Biol       Date:  2019-12-23       Impact factor: 13.583

9.  MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.

Authors:  Greg Finak; Andrew McDavid; Masanao Yajima; Jingyuan Deng; Vivian Gersuk; Alex K Shalek; Chloe K Slichter; Hannah W Miller; M Juliana McElrath; Martin Prlic; Peter S Linsley; Raphael Gottardo
Journal:  Genome Biol       Date:  2015-12-10       Impact factor: 13.583

10.  A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity.

Authors:  Adi L Tarca; Gaurav Bhatti; Roberto Romero
Journal:  PLoS One       Date:  2013-11-15       Impact factor: 3.240

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  3 in total

1.  Single-cell analysis highlights differences in druggable pathways underlying adaptive or fibrotic kidney regeneration.

Authors:  Michael S Balzer; Tomohito Doke; Ya-Wen Yang; Daniel L Aldridge; Hailong Hu; Hung Mai; Dhanunjay Mukhi; Ziyuan Ma; Rojesh Shrestha; Matthew B Palmer; Christopher A Hunter; Katalin Susztak
Journal:  Nat Commun       Date:  2022-07-11       Impact factor: 17.694

2.  Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets.

Authors:  Zhen Miao; Michael S Balzer; Ziyuan Ma; Hongbo Liu; Junnan Wu; Rojesh Shrestha; Tamas Aranyi; Amy Kwan; Ayano Kondo; Marco Pontoglio; Junhyong Kim; Mingyao Li; Klaus H Kaestner; Katalin Susztak
Journal:  Nat Commun       Date:  2021-04-15       Impact factor: 14.919

Review 3.  Microfluidics Facilitates the Development of Single-Cell RNA Sequencing.

Authors:  Yating Pan; Wenjian Cao; Ying Mu; Qiangyuan Zhu
Journal:  Biosensors (Basel)       Date:  2022-06-24
  3 in total

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