hub gene selection_ds

When Is Hub Gene Selection Better than Standard Meta-Analysis?

Peter Langfelder, Paul S. Mischel, and Steve Horvath

2013

Protein interaction network of Caenorhabditis elegans

Almaas E J Exp Biol 2007;210:1548-1558

©2007 by The Company of Biologists Ltd

Hub, Module, Bottleneckb. Red indicates higher expression and blue indicates lower expression. Larger nodes are major bridges between different parts of a network

c. Major hub

Law et al. 2013 Systems virology: host-directed approaches to viral pathogenesis and drug targeting

Background

• Highly connected hub nodes are central to network architecture

• Protein knockout experiments have shown that hub proteins tend to be essential for survival– Yeast, Fly, Worm

• There is a debate about hub importance, but authors argue hubs are often not important

Langfelder P, Mischel PS, Horvath S (2013) When Is Hub Gene Selection Better than Standard Meta-Analysis?. PLoS ONE 8(4): e61505. doi:10.1371/journal.pone.0061505http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061505

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061505

The Main Question

• Does hub gene selection lead to more meaningful gene lists than a standard statistical analysis based on significance testing?



rankPvalue: Scale Method

1. Scales the individual importance measures in each study to mean 0 and variance 1

2. Averages the statistics and relies on the central limit theorem to approximate the null distribution of the resulting meta-analysis statistic



rankPvalue: Rank Method

1. If assumptions of central limit theorem are not met, then use the Rank method.

2. Replaces the values of importance measures by rankings

3. Rankings are divided so that the resulting value lies in the unit interval

4. Sum of ranking is meta analysis test statistic5. Distribution can be estimated from convoluting the

distributions of K independent uniformly distributed variables



Table 1. Overview of meta-analysis methods used in this article.



Question 1

• Are whole-network hug genes relevant or should one exclusively focus on intramodular hubs?– Correlation network applications show that one

should focus on intramodular hubs in trait-related modules



Weighted Correlation Network Analysis (WGCNA)

Finding consensus modules and intramodular hubs

• Can calibrate weighted networks before combining them• It is straightforward to combine weighted networks across

independent data sets• It provides module eigengenes that can be used to relate

modules to sample traits• It affords measures of module membership, which can be used

for finding hub genes in consensus modules• A trait-related consensus module is selected across the

individual data sets• Variables with highest overall module membership are identifiedLangfelder P, Mischel PS, Horvath S (2013) When Is Hub Gene Selection Better than Standard Meta-Analysis?. PLoS ONE 8(4): e61505.

doi:10.1371/journal.pone.0061505http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061505


Table 2. Overview of data sets used in this article.



Biological Data Sets

• Genes associated with adenocarcinoma survival time in human expression data

• CpGs hypermethylated with age in human blood and brain methylation data

• Genes positively correlated with total cholesterol in mouse liver expression data



Figure S1: Lung Cancer Data

Figure S2: Lung Cancer Data

• Each row corresponds to one module identified in the consensus module analysis

• First column shows meta-analysis Z statistic and corresponding p-value

Question 4

• Do network-based gene selection strategies lead to gene lists that are biologically more informative than those based on standard marginal approaches?– Yes, gene selection based on intramodular

connectivity leads to biologically more informative gene lists. In contrast, whole-network connectivity leads to the least informative gene lists



Figure 1. Meta-analysis of module membership leads to gene lists with stronger functional enrichment.



Figure 2. Marginal meta-analysis tends to lead to gene lists with better validation in independent data.



Figure 3. Simulation studies of gene screening success of meta-analysis methods.



Key Findings

1. Hub genes defined with respect to whole-network connectivity are often uninteresting in correlation networks constructed from data from higher organisms

2. Selecting intramodular hubs in a relevant module often leads to gene lists with cleaner biological annotation.

3. Marginal meta-analysis leads to superior validation success of gene-trait associations in 2 of 3 applicationsLangfelder P, Mischel PS, Horvath S (2013) When Is Hub Gene Selection Better than Standard Meta-Analysis?. PLoS ONE 8(4): e61505.

doi:10.1371/journal.pone.0061505http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061505


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