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pcaReduce: hierarchical clustering of single cell transcriptional profiles

Overview of attention for article published in BMC Bioinformatics, March 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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Title
pcaReduce: hierarchical clustering of single cell transcriptional profiles
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0984-y
Pubmed ID
Authors

Justina žurauskienė, Christopher Yau

Abstract

Advances in single cell genomics provide a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression analysis is the identification of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied, but there is considerable scope for the development of novel statistical approaches that are better adapted to the challenges of inferring cellular hierarchies. We have developed a novel agglomerative clustering method that we call pcaReduce to generate a cell state hierarchy where each cluster branch is associated with a principal component of variation that can be used to differentiate two cell states. Using two real single cell datasets, we compared our approach to other commonly used statistical techniques, such as K-means and hierarchical clustering. We found that pcaReduce was able to give more consistent clustering structures when compared to broad and detailed cell type labels. Our novel integration of principal components analysis and hierarchical clustering establishes a connection between the representation of the expression data and the number of cell types that can be discovered. In doing so we found that pcaReduce performs better than either technique in isolation in terms of characterising putative cell states. Our methodology is complimentary to other single cell clustering techniques and adds to a growing palette of single cell bioinformatics tools for profiling heterogeneous cell populations.

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The data shown below were collected from the profiles of 10 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 261 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
United Kingdom 2 <1%
Switzerland 1 <1%
Sweden 1 <1%
China 1 <1%
Denmark 1 <1%
Unknown 253 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 25%
Researcher 45 17%
Student > Master 26 10%
Student > Bachelor 19 7%
Other 18 7%
Other 39 15%
Unknown 48 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 62 24%
Agricultural and Biological Sciences 56 21%
Computer Science 33 13%
Medicine and Dentistry 13 5%
Mathematics 9 3%
Other 36 14%
Unknown 52 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 May 2016.
All research outputs
#5,551,984
of 22,856,968 outputs
Outputs from BMC Bioinformatics
#1,999
of 7,293 outputs
Outputs of similar age
#78,741
of 300,114 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 127 outputs
Altmetric has tracked 22,856,968 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,293 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 72% 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 300,114 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 73% of its contemporaries.
We're also able to compare this research output to 127 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 68% of its contemporaries.