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Robust normalization protocols for multiplexed fluorescence bioimage analysis

Overview of attention for article published in BioData Mining, March 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 (80th percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

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7 Dimensions

Readers on

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26 Mendeley
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Title
Robust normalization protocols for multiplexed fluorescence bioimage analysis
Published in
BioData Mining, March 2016
DOI 10.1186/s13040-016-0088-2
Pubmed ID
Authors

Shan E Ahmed Raza, Daniel Langenkämper, Korsuk Sirinukunwattana, David Epstein, Tim W. Nattkemper, Nasir M. Rajpoot

Abstract

study of mapping and interaction of co-localized proteins at a sub-cellular level is important for understanding complex biological phenomena. One of the recent techniques to map co-localized proteins is to use the standard immuno-fluorescence microscopy in a cyclic manner (Nat Biotechnol 24:1270-8, 2006; Proc Natl Acad Sci 110:11982-7, 2013). Unfortunately, these techniques suffer from variability in intensity and positioning of signals from protein markers within a run and across different runs. Therefore, it is necessary to standardize protocols for preprocessing of the multiplexed bioimaging (MBI) data from multiple runs to a comparable scale before any further analysis can be performed on the data. In this paper, we compare various normalization protocols and propose on the basis of the obtained results, a robust normalization technique that produces consistent results on the MBI data collected from different runs using the Toponome Imaging System (TIS). Normalization results produced by the proposed method on a sample TIS data set for colorectal cancer patients were ranked favorably by two pathologists and two biologists. We show that the proposed method produces higher between class Kullback-Leibler (KL) divergence and lower within class KL divergence on a distribution of cell phenotypes from colorectal cancer and histologically normal samples.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 15%
Researcher 4 15%
Student > Doctoral Student 3 12%
Student > Bachelor 2 8%
Student > Postgraduate 2 8%
Other 3 12%
Unknown 8 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 15%
Computer Science 3 12%
Agricultural and Biological Sciences 3 12%
Engineering 2 8%
Medicine and Dentistry 1 4%
Other 2 8%
Unknown 11 42%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 31 March 2016.
All research outputs
#2,599,405
of 15,917,790 outputs
Outputs from BioData Mining
#77
of 252 outputs
Outputs of similar age
#52,312
of 268,904 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 15,917,790 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 252 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. 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 268,904 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 80% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them