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Pathway Analysis

Pathway Analysis

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Pathway Analysis

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  1. Pathway Analysis

  2. Goals • Characterize biological meaning of joint changes in gene expression • Organize expression (or other) changes into meaningful ‘chunks’ (themes) • Identify crucial points in process where intervention could make a difference • Why? Biology is Redundant! Often sets of genes doing related functions are changed

  3. Gene Sets • Gene Ontology • Biological Process • Molecular Function • Cellular Location • Pathway Databases • KEGG • BioCarta • Broad Institute

  4. Other Gene Sets • Transcription factor targets • All the genes regulated by particular TF’s • Protein complex components • Sets of genes whose protein products function together • Ion channel receptors • RNA / DNA Polymerase • Paralogs • Families of genes descended (in eukaryotic times) from a common ancestor

  5. Approaches • Univariate: • Derive summary statistics for each gene independently • Group statistics of genes by gene group • Multivariate: • Analyze covariation of genes in groups across individuals • More adaptable to continuous statistics

  6. Univariate Approaches • Discrete tests: enrichment for groups in gene lists • Select genes differentially expressed at some cutoff • For each gene group cross-tabulate • Test for significance (Hypergeometric or Fisher test) • Continuous tests: from gene scores to group scores • Compare distribution of scores within each group to random selections • GSEA (Gene Set Enrichment Analysis) • PAGE (Parametric Analysis of Gene Expression)

  7. Multivariate Approaches • Classical multivariate methods • Multi-dimensional Scaling • Hotelling’s T2 • Informativeness • Topological score relative to network • Prediction by machine learning tool • e.g. ‘random forest’

  8. Contingency Table – 2 X 2 P =

  9. Categorical Analysis • Fisher’s Exact Test • Condition on margins fixed • Of all tables with same margins, how many have dependence as or more extreme? • Hard to compute when n or k are large • Approximations • Binomial (when k/n is small) • Chi-square (when expected values > 5 ) • G2 (log-likelihood ratio; compare to c2)

  10. Issues in Assessing Significance • P-value or FDR? • Heuristic only; use FDR • If a child category is significant, how to assess significance of parent category? • Include child category • Consider only genes outside child category • What is appropriate Null Distribution? • Random sets of genes? Or • Random assignments of samples?

  11. Critiques of Discrete Approach • No use of information about size of change • Continuous procedures usually have twice the power of analogous discrete procedures on discretized continuous data • No use of covariation –knowing covariation usually improves power of test

  12. (2003)

  13. GSEA • Uses Kolmogorov-Smirnov (K-S) test of distribution equality to compare t-scores for selected gene group with all genes

  14. Update Fixes a Problem • Sometimes ranks concentrated in middle • Hack: Ad-hoc weighting by scores emphasizes peaks at extremes

  15. Group Z- or T- Scores • Under Null Hypothesis, each gene’s z-score (zi) is distributed N(0,1) • Hence the sum over genes in a group G: • Identify which groups have highest scores • Same issues as discrete: • Null Distribution: permute which indices? • Hierarchy

  16. Issues for Pathway Methods • How to assess significance? • Null distribution by permutations • Permute genes or samples? • How to handle activators and inhibitors in the same pathway? • Variance Test • Other approaches

  17. Pathway Analysis of Genotype Data

  18. The Pathways Proposal • Complex disease ensues from the malfunction of one or a few specific signaling pathways • Alternatives: • Common variants of several genes in the pathway each contribute moderate risk • Rare de novo variants confer great risk and persist for generations in LD with typed markers within unidentified subpopulations of the study group

  19. Approach 1 - Adaptation of GSEA • Order log-odds ratios or linkage p-values for all SNP’s • Map SNP’s to genes, and genes to groups • Use linkage p-values in place of t-scores in GSEA • Compare distribution of log-odds ratios for SNP’s in group to randomly selected SNP’s from the chip

  20. Possible Association Models • Each of several genes may have a variant that confers increased RR independent of other genes • Several genes in contribute additively to the malfunction of the pathway • There are several distinct combinations of gene variants that increase RR but only modest increases in risk for any single variant

  21. Approach 2 – Combining p-values • 1. Compute gene-wise p-value: • Select most likely variant - ‘best’ p-value • Selected minimum p-value is biased downward • Assign ‘gene-wise’ p-value by permutations (Westfall-Young) • Permute samples and compute ‘best’ p-value for each permutation • Compare candidate SNP pvalues to this null distribution of ‘best’ p-values • 2. Combine p-values by Fisher’s method

  22. Methods – 2 • Additive model: • Where ni indexes the number of allele B’s of a SNP in gene i in the gene set G • Select subset of most likely SNP’s • Fit by logistic regression (glm() in R) • Significance by permutations • Permute sample outcomes • Select genes and fit logistic regression again • Assess goodness of fit each time • Compare observed goodness of fit

  23. Multivariate Approaches to Gene Set Analysis

  24. Key Multivariate Ideas • PCA (Principal Components Analysis) • SVD (Singular Value Decomposition) • MDS (Multi-dimensional Scaling) • Hotelling T2

  25. PCA PCA1 lies along the direction of maximal correlation; PCA 2 at right angles with the next highest variation. Three correlated variables

  26. Multi-Dimensional Scaling • Aim: to represent graphically the most information about relationships among samples with multi-dimensional attributes in 2 (or 3) dimensions • Algorithm: • Transform distances into cross-product matrix • Initial PCA onto 2 (or 3) axes • Deform until better representation • Minimize ‘strain’ measure:

  27. Separating Using MDS Left: distributions of individual variables Right: MDS plot (in this case PCA)

  28. Multivariate Approaches to Selection • Visualizing differences by MDS • Hotelling’s T-squared

  29. MDS for Pathways • BAD pathway Normal IBC Other BC • Clear separation between groups • Variation differences

  30. Hotelling’s T2 • Compute distance between sample means using (common) metric of covariation • Where • Multidimensional analog of t (actually F) statistic

  31. Principles of Kong et al Method • Normal covariation generally acts to preserve homeostasis • The transcription of genes that participate in many processes will be changed • The joint changes in genes will be most distinctive for those genes active in pathways that are working differently

  32. Critiques of Hotelling’s T • Not robust to outliers • Assumes same covariance in each sample • S1 = S2 ? Usually not in disease • Small samples: unreliable S estimates • N < p