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A Synthetic Kinome Microarray Data Generator
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. Typ...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996406/ https://www.ncbi.nlm.nih.gov/pubmed/27600233 http://dx.doi.org/10.3390/microarrays4040432 |
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author | Maleki, Farhad Kusalik, Anthony |
author_facet | Maleki, Farhad Kusalik, Anthony |
author_sort | Maleki, Farhad |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4996406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-49964062016-09-06 A Synthetic Kinome Microarray Data Generator Maleki, Farhad Kusalik, Anthony Microarrays (Basel) Article 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. MDPI 2015-10-16 /pmc/articles/PMC4996406/ /pubmed/27600233 http://dx.doi.org/10.3390/microarrays4040432 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Maleki, Farhad Kusalik, Anthony A Synthetic Kinome Microarray Data Generator |
title | A Synthetic Kinome Microarray Data Generator |
title_full | A Synthetic Kinome Microarray Data Generator |
title_fullStr | A Synthetic Kinome Microarray Data Generator |
title_full_unstemmed | A Synthetic Kinome Microarray Data Generator |
title_short | A Synthetic Kinome Microarray Data Generator |
title_sort | synthetic kinome microarray data generator |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996406/ https://www.ncbi.nlm.nih.gov/pubmed/27600233 http://dx.doi.org/10.3390/microarrays4040432 |
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