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Exploiting single-cell expression to characterize co-expression replicability

Overview of attention for article published in Genome Biology, May 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 blogs
twitter
16 X users

Citations

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70 Dimensions

Readers on

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245 Mendeley
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3 CiteULike
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Title
Exploiting single-cell expression to characterize co-expression replicability
Published in
Genome Biology, May 2016
DOI 10.1186/s13059-016-0964-6
Pubmed ID
Authors

Megan Crow, Anirban Paul, Sara Ballouz, Z. Josh Huang, Jesse Gillis

Abstract

Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes. However, co-expression analysis is often treated as a black box with results being hard to trace to their basis in the data. Here, we use both published and novel single-cell RNA sequencing (RNA-seq) data to understand fundamental drivers of gene-gene connectivity and replicability in co-expression networks. We perform the first major analysis of single-cell co-expression, sampling from 31 individual studies. Using neighbor voting in cross-validation, we find that single-cell network connectivity is less likely to overlap with known functions than co-expression derived from bulk data, with functional variation within cell types strongly resembling that also occurring across cell types. To identify features and analysis practices that contribute to this connectivity, we perform our own single-cell RNA-seq experiment of 126 cortical interneurons in an experimental design targeted to co-expression. By assessing network replicability, semantic similarity and overall functional connectivity, we identify technical factors influencing co-expression and suggest how they can be controlled for. Many of the technical effects we identify are expression-level dependent, making expression level itself highly predictive of network topology. We show this occurs generally through re-analysis of the BrainSpan RNA-seq data. Technical properties of single-cell RNA-seq data create confounds in co-expression networks which can be identified and explicitly controlled for in any supervised analysis. This is useful both in improving co-expression performance and in characterizing single-cell data in generally applicable terms, permitting cross-laboratory comparison within a common framework.

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X Demographics

The data shown below were collected from the profiles of 16 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 245 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Sweden 1 <1%
Germany 1 <1%
Taiwan 1 <1%
Canada 1 <1%
Unknown 237 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 63 26%
Researcher 54 22%
Student > Master 24 10%
Student > Bachelor 23 9%
Professor 12 5%
Other 36 15%
Unknown 33 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 77 31%
Biochemistry, Genetics and Molecular Biology 49 20%
Computer Science 20 8%
Neuroscience 14 6%
Medicine and Dentistry 12 5%
Other 29 12%
Unknown 44 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 13 September 2018.
All research outputs
#1,826,253
of 25,373,627 outputs
Outputs from Genome Biology
#1,514
of 4,467 outputs
Outputs of similar age
#29,417
of 312,371 outputs
Outputs of similar age from Genome Biology
#38
of 77 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 66% 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 312,371 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 90% of its contemporaries.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.