Next Article in Journal
Genomic-Wide Analysis with Microarrays in Human Oncology
Previous Article in Journal
Aptamer-Based Screens of Human Body Fluids for Biomarkers
Previous Article in Special Issue
SPOTing Acetyl-Lysine Dependent Interactions
Article Menu

Export Article

From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessArticle
Microarrays 2015, 4(4), 432-453; doi:10.3390/microarrays4040432

A Synthetic Kinome Microarray Data Generator

Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Alexander Nesterov-MÃijller
Received: 9 June 2015 / Revised: 25 August 2015 / Accepted: 10 October 2015 / Published: 16 October 2015
(This article belongs to the Special Issue Peptide Microarrays)
View Full-Text   |   Download PDF [307 KB, uploaded 16 October 2015]   |  

Abstract

Cellular pathways involve the phosphorylation and dephosphorylation of proteins. Peptide microarrays called kinome arrays facilitate the measurement of the phosphorylation activity of hundreds of proteins in a single experiment. Analyzing the data from kinome microarrays is a multi-step process. Typically, various techniques are possible for a particular step, and it is necessary to compare and evaluate them. Such evaluations require data for which correct analysis results are known. Unfortunately, such kinome data is not readily available in the community. Further, there are no established techniques for creating artificial kinome datasets with known results and with the same characteristics as real kinome datasets. In this paper, a methodology for generating synthetic kinome array data is proposed. The methodology relies on actual intensity measurements from kinome microarray experiments and preserves their subtle characteristics. The utility of the methodology is demonstrated by evaluating methods for eliminating heterogeneous variance in kinome microarray data. Phosphorylation intensities from kinome microarrays often exhibit such heterogeneous variance and its presence can negatively impact downstream statistical techniques that rely on homogeneity of variance. It is shown that using the output from the proposed synthetic data generator, it is possible to critically compare two variance stabilization methods. View Full-Text
Keywords: kinome array; synthetic data; normalization; heteroscedasticity of variance kinome array; synthetic data; normalization; heteroscedasticity of variance
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Maleki, F.; Kusalik, A. A Synthetic Kinome Microarray Data Generator. Microarrays 2015, 4, 432-453.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Microarrays EISSN 2076-3905 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top