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Discovering Biological Progression Underlying Microarray Samples

Overview of attention for article published in PLoS Computational Biology, April 2011
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Title
Discovering Biological Progression Underlying Microarray Samples
Published in
PLoS Computational Biology, April 2011
DOI 10.1371/journal.pcbi.1001123
Pubmed ID
Authors

Peng Qiu, Andrew J. Gentles, Sylvia K. Plevritis

Abstract

In biological systems that undergo processes such as differentiation, a clear concept of progression exists. We present a novel computational approach, called Sample Progression Discovery (SPD), to discover patterns of biological progression underlying microarray gene expression data. SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression), and that each sample represents one unknown point along the progression of that process. SPD aims to organize the samples in a manner that reveals the underlying progression and to simultaneously identify subsets of genes that are responsible for that progression. We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process. When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time order and identified many genes that have been associated with the cell cycle. When applied to B-cell differentiation data, SPD recovered the correct order of stages of normal B-cell differentiation and the linkage between preB-ALL tumor cells with their cell origin preB. When applied to mouse embryonic stem cell differentiation data, SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes. When applied to a prostate cancer microarray dataset, SPD identified gene modules that reflect a progression consistent with disease stages. SPD may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the candidate genes that regulate that progression.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 7%
India 2 1%
Spain 2 1%
Italy 1 <1%
France 1 <1%
Canada 1 <1%
Switzerland 1 <1%
United Kingdom 1 <1%
Russia 1 <1%
Other 0 0%
Unknown 122 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 30%
Researcher 40 28%
Professor > Associate Professor 10 7%
Student > Master 8 6%
Student > Bachelor 8 6%
Other 22 15%
Unknown 12 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 65 46%
Biochemistry, Genetics and Molecular Biology 20 14%
Computer Science 13 9%
Medicine and Dentistry 9 6%
Engineering 8 6%
Other 14 10%
Unknown 13 9%