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...
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doaj-5f8ff04840404d9d977800021d2e528a2020-11-25T02:35:54ZengMDPI AGMicroarrays2076-39052015-10-014443245310.3390/microarrays4040432microarrays4040432A Synthetic Kinome Microarray Data GeneratorFarhad Maleki0Anthony Kusalik1Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, CanadaDepartment of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, CanadaCellular 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.http://www.mdpi.com/2076-3905/4/4/432kinome arraysynthetic datanormalizationheteroscedasticity of variance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Farhad Maleki Anthony Kusalik |
spellingShingle |
Farhad Maleki Anthony Kusalik A Synthetic Kinome Microarray Data Generator Microarrays kinome array synthetic data normalization heteroscedasticity of variance |
author_facet |
Farhad Maleki Anthony Kusalik |
author_sort |
Farhad Maleki |
title |
A Synthetic Kinome Microarray Data Generator |
title_short |
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_sort |
synthetic kinome microarray data generator |
publisher |
MDPI AG |
series |
Microarrays |
issn |
2076-3905 |
publishDate |
2015-10-01 |
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. |
topic |
kinome array synthetic data normalization heteroscedasticity of variance |
url |
http://www.mdpi.com/2076-3905/4/4/432 |
work_keys_str_mv |
AT farhadmaleki asynthetickinomemicroarraydatagenerator AT anthonykusalik asynthetickinomemicroarraydatagenerator AT farhadmaleki synthetickinomemicroarraydatagenerator AT anthonykusalik synthetickinomemicroarraydatagenerator |
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