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Integrated single cell data analysis reveals cell specific networks and novel coactivation markers

Overview of attention for article published in BMC Systems Biology, December 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
19 tweeters

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
63 Mendeley
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Title
Integrated single cell data analysis reveals cell specific networks and novel coactivation markers
Published in
BMC Systems Biology, December 2016
DOI 10.1186/s12918-016-0370-4
Pubmed ID
Authors

Shila Ghazanfar, Adam J. Bisogni, John T. Ormerod, David M. Lin, Jean Y. H. Yang

Abstract

Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression. We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons. Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data.

Twitter Demographics

The data shown below were collected from the profiles of 19 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Luxembourg 1 2%
Unknown 62 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 22%
Researcher 9 14%
Student > Bachelor 8 13%
Student > Doctoral Student 5 8%
Student > Master 4 6%
Other 9 14%
Unknown 14 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 22%
Agricultural and Biological Sciences 11 17%
Computer Science 8 13%
Mathematics 6 10%
Neuroscience 2 3%
Other 7 11%
Unknown 15 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 June 2017.
All research outputs
#1,422,181
of 12,378,936 outputs
Outputs from BMC Systems Biology
#71
of 1,040 outputs
Outputs of similar age
#58,120
of 342,324 outputs
Outputs of similar age from BMC Systems Biology
#2
of 16 outputs
Altmetric has tracked 12,378,936 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,040 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 93% 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 342,324 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.