Statistical Considerations for Analysis of Microarray Experiments

被引：20

作者：

Owzar, Kouros ^{[1
]}

Barry, William T. ^{[1
]}

Jung, Sin-Ho ^{[1
]}

机构：

[1] Duke Univ, Duke Univ CALGB Stat Ctr, Dept Biostat & Bioinformat, Durham, NC 27710 USA

来源：

CTS-CLINICAL AND TRANSLATIONAL SCIENCE | 2011年 / 4卷 / 06期

关键词：

microarrays; preprocessing; statistical inference; multiple testing; unsupervised learning; supervised learning; overfitting; validation; pathways; clinical trials; power; software; FALSE DISCOVERY RATE; GENE-EXPRESSION; FUNCTIONAL CATEGORIES; NORMALIZATION METHODS; SAMPLE-SIZE; CLASSIFICATION; CARCINOMAS;

D O I：

10.1111/j.1752-8062.2011.00309.x

中图分类号：

R-3 [医学研究方法]; R3 [基础医学];

学科分类号：

1001 ;

摘要：

Microarray technologies enable the simultaneous interrogation of expressions from thousands of genes from a biospecimen sample taken from a patient. This large set of expressions generates a genetic profile of the patient that may be used to identify potential prognostic or predictive genes or genetic models for clinical outcomes. The aim of this article is to provide a broad overview of some of the major statistical considerations for the design and analysis of microarrays experiments conducted as correlative science studies to clinical trials. An emphasis will be placed on how the lack of understanding and improper use of statistical concepts and methods will lead to noise discovery and misinterpretation of experimental results. Clin Trans Sci 2011; Volume 4: 466477

引用

页码：466 / 477

页数：12

共 50 条

[31] Visual analysis of statistical results from microarray studies of human breast cancer
Reif, DM
Moore, JH
ONCOLOGY REPORTS, 2006, 15 : 1043 - 1047
[32] Sample size determination in microarray experiments for class comparison and prognostic classification
Dobbin, K
Simon, R
BIOSTATISTICS, 2005, 6 (01) : 27 - 38
[33] Statistical analysis of a gene expression microarray experiment with replication
Kerr, MK
Afshari, CA
Bennett, L
Bushel, P
Martinez, J
Walker, NJ
Churchill, GA
STATISTICA SINICA, 2002, 12 (01) : 203 - 217
[34] Effects of filtering by Present call on analysis of microarray experiments
Jeanette N McClintick
Howard J Edenberg
BMC Bioinformatics, 7
[35] Statistical approaches for the analysis of DNA methylation microarray data
Siegmund, Kimberly D.
HUMAN GENETICS, 2011, 129 (06) : 585 - 595
[36] Statistical Learning Analysis of Thyroid Cancer Microarray Data
Petrini, Ivan
Cecchini, Rocio L.
Mascaro, Marilina
Ponzoni, Ignacio
Carballido, Jessica A.
BIOINFORMATICS AND BIOMEDICAL ENGINEERING, PT II, 2022, : 90 - 102
[37] Distributional fold change test - a statistical approach for detecting differential expression in microarray experiments
Farztdinov, Vadim
McDyer, Fionnuala
ALGORITHMS FOR MOLECULAR BIOLOGY, 2012, 7
[38] Microarray experiments and factors which affect their reliability
Jaksik, Roman
Iwanaszko, Marta
Rzeszowska-Wolny, Joanna
Kimmel, Marek
BIOLOGY DIRECT, 2015, 10
[39] A FLEXIBLE NONPARAMETRIC APPROACH TO FIND CANDIDATE GENES ASSOCIATED WITH DISEASE IN MICROARRAY EXPERIMENTS
Hossain, Ahmed
Willan, Andrew R.
Beyene, Joseph
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 2013, 11 (02)
[40] Statistical considerations for preclinical studies
Aban, Inmaculada B.
George, Brandon
EXPERIMENTAL NEUROLOGY, 2015, 270 : 82 - 87

← 1 2 3 4 5 →