An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles.

By Jeffrey G. Thomas, James M. Olson, Stephen J. Tapscott, Lue Ping Zhao. Genome Research, 2001

360 citations recorded by the source corpus.

We have developed a statistical regression modeling approach to discover genes that are differentially expressed between two predefined sample groups in DNA microarray experiments. Our model is based on well-defined assumptions, uses rigorous and well-characterized statistical measures, and accounts for the heterogeneity and genomic complexity of the data. In contrast to cluster analysis, which attempts to define groups of genes and/or samples that share common overall expression profiles, our modeling approach uses known sample group membership to focus on expression profiles of individual genes in a sensitive and robust manner. Further, this approach can be used to test statistical hypotheses about gene expression. To demonstrate this methodology, we compared the expression profiles of 11 acute myeloid leukemia (AML) and 27 acute lymphoblastic leukemia (ALL) samples from a previous study (Golub et al. 1999) and found 141 genes differentially expressed between AML and ALL with a 1% significance at the genomic level. Using this modeling approach to compare different sample groups within the AML samples, we identified a group of genes whose expression profiles correlated with that of thrombopoietin and found that genes whose expression associated with AML treatment outcome lie in recurrent chromosomal locations. Our results are compared with those obtained using t-tests or Wilcoxon rank sum statistics.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/40902c80298752cfba32714f8dd29966ba016016

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In Genetics

1. Wei Pan
2. Pedro Larrañaga
3. Chris H. Q. Ding
4. Iñaki Inza
5. Qing Chang
6. Yaoli Wang
7. Hanchuan Peng
8. Lipo Wang

In Molecular Biology

1. Yvan Saeys
2. Hanchuan Peng
3. Lipo Wang
4. Qing Chang
5. Yaoli Wang
6. Pedro Larrañaga
7. Iñaki Inza
8. Huan Liu

Citations in the corpus

• A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments (2002, methodology): “In this paper, we comparatively review the three methods, the t -test, the regression approach of Thomas et al. (2001), and the mixture model approach of Pan et al. (2001a).”
• Penalized Model-Based Clustering with Application to Variable Selection (2007, background): “…eight genes had their cluster-specific mean estimates as 0's across all seven clusters, and hence were regarded as non-informative; previous studies demonstrated that there were indeed a large number of the genes differentially expressed between ALL and AML samples (Thomas et al., 2001; Pan, 2002).”
• Feature selection methods for big data bioinformatics: A survey from the search perspective. (2016, background): “Although there are numerous types of classifiers and a feature subset best for one classifier may not be the best for another classifier, it is not a good idea to leave out classifiers altogether for FS. Examples of data-distilled feature importance measures include t-test [106,107], fold-change dif”
• Minimum redundancy feature selection from microarray gene expression data (2003, methodology): “More sophisticated regression models or tests along this line were also developed [22][19][26].”
• A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis (2012, neutral): “p-value with Bonferroni correction is an improved version of the classical p-value and consists in increasing the statistical threshold for declaring a gene significant by dividing the desired significance with the number of statistical tests performed [17].”
• A review of feature selection techniques in bioinformatics (2007, methodology): “Many model-free metrics, frequently borrowed from the statistics field, have demonstrated their usefulness in many gene expression studies, including the Wilcoxon rank-sum test [117], the between-within classes sum of squares (BSS/WSS) [29] and the rank products method [12].”
• Gene selection from microarray data for cancer classification - a machine learning approach (2005, methodology): “One straightforward approach to select rele genes is the application of standard parametric tests su the t-test(Thomas et al., 2001; Tsai et al., 2003) and a non parametric test such as the Wilcoxon score test (Thomas e al.”