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A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications

Overview of attention for article published in Genome Medicine, August 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#15 of 1,610)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
42 news outlets
blogs
11 blogs
twitter
156 X users
patent
1 patent
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

dimensions_citation
723 Dimensions

Readers on

mendeley
3110 Mendeley
citeulike
2 CiteULike
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Title
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Published in
Genome Medicine, August 2017
DOI 10.1186/s13073-017-0467-4
Pubmed ID
Authors

Ashraful Haque, Jessica Engel, Sarah A. Teichmann, Tapio Lönnberg

Abstract

RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology-the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 156 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3,110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3110 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 619 20%
Researcher 472 15%
Student > Bachelor 358 12%
Student > Master 322 10%
Student > Doctoral Student 152 5%
Other 310 10%
Unknown 877 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 832 27%
Agricultural and Biological Sciences 389 13%
Medicine and Dentistry 209 7%
Neuroscience 180 6%
Immunology and Microbiology 166 5%
Other 380 12%
Unknown 954 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 479. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 April 2023.
All research outputs
#56,604
of 25,728,855 outputs
Outputs from Genome Medicine
#15
of 1,610 outputs
Outputs of similar age
#1,160
of 327,951 outputs
Outputs of similar age from Genome Medicine
#1
of 26 outputs
Altmetric has tracked 25,728,855 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,610 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,951 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.