↓ Skip to main content

Robust normalization protocols for multiplexed fluorescence bioimage analysis

Overview of attention for article published in BioData Mining, March 2016
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 (79th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

blogs
1 blog
twitter
2 X users

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
32 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
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 13%
Researcher 4 13%
Student > Doctoral Student 3 9%
Student > Ph. D. Student 3 9%
Student > Master 3 9%
Other 4 13%
Unknown 11 34%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 16%
Agricultural and Biological Sciences 3 9%
Computer Science 3 9%
Engineering 3 9%
Medicine and Dentistry 1 3%
Other 3 9%
Unknown 14 44%
Attention Score in Context

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
#3,928,233
of 22,854,458 outputs
Outputs from BioData Mining
#88
of 307 outputs
Outputs of similar age
#60,937
of 298,823 outputs
Outputs of similar age from BioData Mining
#5
of 12 outputs
Altmetric has tracked 22,854,458 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 307 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 70% 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 298,823 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 79% of its contemporaries.
We're also able to compare this research output to 12 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 58% of its contemporaries.