Evidence map›Paper›PMID 33722930›Full record

ReviewJournal of the American Society of Nephrology : JASN2021

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

Michael S Balzer, Ziyuan Ma, Jianfu Zhou, Amin Abedini, Katalin Susztak

Abstract readReview
In one paragraph

Review in Journal of the American Society of Nephrology : JASN, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  16. NTCdb: Single-cell transcriptome database of human inflammatory-associated diseases.Computational and structural biotechnology journal · 2024
    Article
  17. Review
  18. Review
  19. A comparative scRNAseq data analysis to match mouse models with human kidney disease at the molecular level.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2024
    Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Michael S BalzerRenal Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.ORCID 0000-0003-0508-1260
Ziyuan MaRenal Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.ORCID 0000-0002-1512-8993
Jianfu ZhouRenal Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.
Amin AbediniRenal Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.ORCID 0000-0002-8875-0070
Katalin SusztakRenal Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.ORCID 0000-0002-1005-3726

Funding

VIRAL VECTOR COREP30DK019525 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI Patrick Seale · 1986 to 2026
$48.3M
Role of the Notch Pathway in Kidney InjuryR01DK076077 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI KATALIN SUSZTAK · 2007 to 2026
$8.0M
Epigenetics of Chronic Kidney DiseaseR01DK087635 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI KATALIN SUSZTAK · 2009 to 2026
$7.0M
APOL1 associated kidney diseaseR01DK105821 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI SUSZTAK, KATALIN · 2016 to 2024
$4.6M
NIDDK NIH HHS P30 DK019525NIDDK NIH HHS R01 DK076077NIDDK NIH HHS R01 DK087635NIDDK NIH HHS R01 DK105821
6 · The paper itself

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.

Indexed as

Data AnalysisSequence Analysis, RNASoftwareDatasets as TopicData VisualizationGene Expression ProfilingGene Expression RegulationGenomicsHumansPrincipal Component AnalysisQuality ControlSingle-Cell Analysisanalysiskidneysingle cell RNA-sequencingtranscriptomics

Identifiers

PMID33722930
PMCPMC8259643

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.