The Library
Robust normalization protocols for multiplexed fluorescence bioimage analysis
Tools
Tools
Tools
Raza, Shan-e-Ahmed, Langenkämper, Daniel, Sirinukunwattana, Korsuk, Epstein, D. B. A., Nattkemper, Tim W. and Rajpoot, Nasir M. (2016) Robust normalization protocols for multiplexed fluorescence bioimage analysis. BioData Mining, 9 (11). pp. 1-13. doi:10.1186/s13040-016-0088-2
|
PDF
WRAP_art%3A10.1186%2Fs13040-016-0088-2.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1808Kb) | Preview |
Official URL: http://dx.doi.org/10.1186/s13040-016-0088-2
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.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
| Divisions: | Faculty of Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Imaging systems in biology, Fluorescent probes | ||||||||
| Journal or Publication Title: | BioData Mining | ||||||||
| Publisher: | BioMed Central Ltd. | ||||||||
| ISSN: | 1756-0381 | ||||||||
| Official Date: | 5 March 2016 | ||||||||
| Dates: |
|
||||||||
| Date of first compliant deposit: | 1 April 2016 | ||||||||
| Volume: | 9 | ||||||||
| Number: | 11 | ||||||||
| Number of Pages: | 13 | ||||||||
| Page Range: | pp. 1-13 | ||||||||
| DOI: | 10.1186/s13040-016-0088-2 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Qatar National Research Fund (QNRF) | ||||||||
| Grant number: | BB/K018868/1 (BBSRC), NPRP 5-1345-1-228 (QNRF) |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
