Discovering statistically significant pathways in expression profiling studies.

By Lu Tian, Steven A. Greenberg, S. Kong, J. Altschuler, Isaac S. Kohane, P. Park. Proceedings of the National Academy of Sciences of the United States of America, 2005

677 citations recorded by the source corpus.

Accurate and rapid identification of perturbed pathways through the analysis of genome-wide expression profiles facilitates the generation of biological hypotheses. We propose a statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest. Several issues on proper hypothesis-testing procedures are clarified. In particular, it is shown that the differences in the correlation structure of each set of genes can lead to a biased comparison among gene sets unless a normalization procedure is applied. We propose statistical tests for two important but different aspects of association for each group of genes. This approach has more statistical power than currently available methods and can result in the discovery of statistically significant pathways that are not detected by other methods. This method is applied to data sets involving diabetes, inflammatory myopathies, and Alzheimer's disease, using gene sets we compiled from various public databases. In the case of inflammatory myopathies, we have correctly identified the known cytotoxic T lymphocyte-mediated autoimmunity in inclusion body myositis. Furthermore, we predicted the presence of dendritic cells in inclusion body myositis and of an IFN-alpha/beta response in dermatomyositis, neither of which was previously described. These predictions have been subsequently corroborated by immunohistochemistry.

Subfields: Epidemiology, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/37f47ce11ad87b569cab4acf6316df5dac7b433a

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Citations in the corpus

Inferring Pathway Activity toward Precise Disease Classification (2008, methodology): “[16] using the designated training data.”
Camera: a competitive gene set test accounting for inter-gene correlation (2012, methodology): “Most competitive gene set tests assume independence of genes (1,9), because they evaluate P-values by permutation of gene labels (2,10,11), or because they rely on parametric approximations that are asymptotically equivalent to gene permutation (12–16).”
Gene-set approach for expression pattern analysis (2008, methodology): “Thereafter, various GSA methods have been developed (Table 1) based on different null hypotheses and statistical methods [3, 10–13].”
Feature Screening via Distance Correlation Learning (2012, background): “There is a surged interest in pathway analyses in the recent literature (Ashburner et al. 2000; Mootha et al. 2003; Subramanian et al. 2005; Tian et al. 2005; Bild et al. 2006; Efron and Tibshirani 2007; Jones et al. 2008).”
LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data (2009, methodology): “Contact: mario.medvedovic@uc.edu Supplementary information: Supplementary data are available at Bioinformatics online and at http://eh3.uc.edu/lrpath.”
Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016, methodology): “Following upon the work of Tian and Kim 57,58 , we used the mean of the quantile-transformed rank positions as test statistic (enrichment score).”
A novel signaling pathway impact analysis (2009, methodology): “On this data, ORA identified nine significant pathways, SPIA identified eight, while GSEAnone (Tables 3–6 in the Supplementary Material).”
Identification of a Novel Biomarker, SEMA5A, for Non–Small Cell Lung Carcinoma in Nonsmoking Women (2010, methodology): “Gene set enrichment analysis (GSEA) (31, 32) and sigPathway (33) were used to analyze differences in genome-wide expression pathway patterns between tumor and normal tissues within the same individual.”
What can Statistics Contribute to a Causal Understanding? (2007, background): “The approach today is to try to discover groups of co-regulated genes, since this can suggest functional pathways and interactions in certain biological processes (Tian et al., 2005).”
Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, background): “This has previously been discussed by several authors (3,4,19,25), and Goeman and Bühlmann (4) introduced the distinction between competitive and self-contained tests.”
On testing the significance of sets of genes (2006, background): “We discuss these next, in a spirit closely related to “Q1” and “Q2” of Tian et al. (2005).”
GSVA: gene set variation analysis for microarray and RNA-Seq data (2013, methodology): “[6, 19], evaluated as the mean differential expression (e.”
Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges (2012, methodology): “When computing statistical significance, the null hypothesis tested by current pathway analysis approaches can be broadly divided into two categories: i) competitive null hypothesis and ii) self-contained null hypothesis [3,18,22,31].”
Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network (2010, methodology): “For this reason, we propose an algorithm for identifying effective subnet-work markers which is motivated by a simple scheme proposed in Tian et al. [18].”