Evidence map›Paper›PMID 28482897›Full record

ReviewGenome biology2017

Challenges and emerging directions in single-cell analysis.

Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin and 5 more

Abstract readLetterReview
In one paragraph

Review in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
160citing 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

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

  1. Pooled it
  2. Trial
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  16. Review
  17. Gene set optimization for single cell transcriptomics.Computational intelligence methods for bioinformatics and biostatistics : ... international meeting, CIBB ... : revised selected papers. CIBB (Meeting) · 2025
    Article
  18. Article
  19. Article
  20. Single-cell Technology in Stem Cell Research.Current stem cell research & therapy · 2025
    Review

100 more citing papers are in PubMed but not listed here.

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

15 authors.

Guo-Cheng YuanDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA. gcyuan@jimmy.harvard.edu.
Long CaiDivision of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
Michael ElowitzDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
Tariq EnverCancer Institute, University College London, London, UK.
Guoping FanDepartment of Human Genetics, University of California, Los Angeles, CA, USA.
Guoji GuoCenter for Stem Cell and Regenerative Medicine, Zhejiang University, Hangzhou, China.
Rafael IrizarryDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Peter KharchenkoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Junhyong KimDepartment of Biology, University of Pennsylvania, Philadelphia, PA, USA.
Stuart OrkinDepartment of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
John QuackenbushDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Assieh SaadatpourDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
Timm SchroederDepartment of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Ramesh ShivdasaniDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Itay TiroshBroad Institute of MIT and Harvard, Cambridge, MA, USA.

Funding

Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Transcriptional control of gut epithelial homeostasis and regenerationR01DK081113 · NIDDK · DANA-FARBER CANCER INST · PI SHIVDASANI, RAMESH A · 2009 to 2022
$5.6M
Using Networks to Assign Gene Function in Lung DiseaseR01HL111759 · NHLBI · DANA-FARBER CANCER INST · PI QUACKENBUSH, JOHN, SILVERMAN, EDWIN K · 2012 to 2015
$3.4M
Dissecting molecular pathways underlying Human Overgrowth SyndromeR01DE025474 · NIDCR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FAN, GUOPING · 2016 to 2020
$1.9M
Conferences on Emerging Statistical Issues in Biomedical ResearchR13CA124365 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG · 2006 to 2015
$262k
Cancer Research UK 12796Medical Research Council MR/M009033/1Medical Research Council MR/N000838/1NCI NIH HHS R13 CA124365NIDDK NIH HHS R01 DK081113
6 · The paper itself

Abstract

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances and look to future opportunities.

Indexed as

AnimalsCell LineageGene Expression ProfilingHumansOptical ImagingSingle-Cell Analysis

Identifiers

PMID28482897
PMCPMC5421338

What OpenQuestion holds

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LicenceCC BY
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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.