Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Maleki, Farhad, Kusalik, Anthony
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
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
_version_ 1782449597469163520
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
work_keys_str_mv AT malekifarhad asynthetickinomemicroarraydatagenerator
AT kusalikanthony asynthetickinomemicroarraydatagenerator
AT malekifarhad synthetickinomemicroarraydatagenerator
AT kusalikanthony synthetickinomemicroarraydatagenerator