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Harnessing the complexity of gene expression data from cancer: From single gene to structural pathway methods

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Harnessing the complexity of gene expression data from cancer : From single gene to structural pathway methods. / Emmert-Streib, Frank; Tripathi, Shailesh; Matos Simoes, Ricardo D.

In: Biology Direct, Vol. 7, 44, 10.12.2012.

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Emmert-Streib, Frank ; Tripathi, Shailesh ; Matos Simoes, Ricardo D. / Harnessing the complexity of gene expression data from cancer : From single gene to structural pathway methods. In: Biology Direct. 2012 ; Vol. 7.

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@article{73796dcfa0e34ccc93b850f4c0a50e4a,
title = "Harnessing the complexity of gene expression data from cancer: From single gene to structural pathway methods",
abstract = ": High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.Reviewers: This article was reviewed by Arcady Mushegian, Byung-Soo Kim and Joel Bader.",
keywords = "Cancer data, Cancer genomics, Correlation structure, Gene expression data, Pathway methods, Statistical analysis methods",
author = "Frank Emmert-Streib and Shailesh Tripathi and {Matos Simoes}, {Ricardo D.}",
year = "2012",
month = "12",
day = "10",
doi = "10.1186/1745-6150-7-44",
language = "English",
volume = "7",
journal = "Biology Direct",
issn = "1745-6150",
publisher = "BioMed Central",

}

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TY - JOUR

T1 - Harnessing the complexity of gene expression data from cancer

T2 - From single gene to structural pathway methods

AU - Emmert-Streib, Frank

AU - Tripathi, Shailesh

AU - Matos Simoes, Ricardo D.

PY - 2012/12/10

Y1 - 2012/12/10

N2 - : High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.Reviewers: This article was reviewed by Arcady Mushegian, Byung-Soo Kim and Joel Bader.

AB - : High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.Reviewers: This article was reviewed by Arcady Mushegian, Byung-Soo Kim and Joel Bader.

KW - Cancer data

KW - Cancer genomics

KW - Correlation structure

KW - Gene expression data

KW - Pathway methods

KW - Statistical analysis methods

UR - http://www.scopus.com/inward/record.url?scp=84872650417&partnerID=8YFLogxK

U2 - 10.1186/1745-6150-7-44

DO - 10.1186/1745-6150-7-44

M3 - Article

VL - 7

JO - Biology Direct

JF - Biology Direct

SN - 1745-6150

M1 - 44

ER -