↓ Skip to main content

Single-Cell Transcriptional Analysis

Overview of attention for article published in Annual Review of Analytical Chemistry, March 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user
patent
1 patent
wikipedia
3 Wikipedia pages

Citations

dimensions_citation
96 Dimensions

Readers on

mendeley
245 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Single-Cell Transcriptional Analysis
Published in
Annual Review of Analytical Chemistry, March 2017
DOI 10.1146/annurev-anchem-061516-045228
Pubmed ID
Authors

Angela R Wu, Jianbin Wang, Aaron M Streets, Yanyi Huang

Abstract

Despite being a relatively recent technological development, single-cell transcriptional analysis through high-throughput sequencing has already been used in hundreds of fruitful studies to make exciting new biological discoveries that would otherwise be challenging or even impossible. Consequently, this has fueled a virtuous cycle of even greater interest in the field and compelled development of further improved technical methodologies and approaches. Thanks to the combined efforts of the research community, including the fields of biochemistry and molecular biology, technology and instrumentation, data science, computational biology, and bioinformatics, the single-cell RNA-sequencing field is advancing at a pace that is both astounding and unprecedented. In this review, we provide a broad introduction to this revolutionary technology by presenting the state-of-the-art in sample preparation methodologies, technology platforms, and computational analysis methods, while highlighting the key considerations for designing, executing, and interpreting a study using single-cell RNA-sequencing. Expected final online publication date for the Annual Review of Analytical Chemistry Volume 10 is June 12, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
South Africa 1 <1%
Unknown 244 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 62 25%
Researcher 48 20%
Student > Bachelor 29 12%
Student > Master 19 8%
Student > Postgraduate 11 4%
Other 33 13%
Unknown 43 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 88 36%
Agricultural and Biological Sciences 31 13%
Engineering 16 7%
Medicine and Dentistry 13 5%
Neuroscience 10 4%
Other 30 12%
Unknown 57 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 22 August 2023.
All research outputs
#5,242,603
of 25,382,440 outputs
Outputs from Annual Review of Analytical Chemistry
#66
of 215 outputs
Outputs of similar age
#87,102
of 322,508 outputs
Outputs of similar age from Annual Review of Analytical Chemistry
#6
of 10 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 215 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 68% 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 322,508 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.