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

<div><p>Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to gene expression data and presents novel R functions for carrying out consensus network analysis, network based screening, and meta analysis.</p></div

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

<p>The barplots show validation success of the various meta-analysis methods in simulated data with 2 different traits. Continuous clinical trait 1 is weakly related to a module eigengene that may, in real data, represent the state of a pathway. In this case meta-analysis of module membership outperforms marginal meta-analysis in identifying validated genes. In contrast, clinical trait 2 is simulated to be strongly correlated with the eigengene of a small submodule of one of the identified modules. Here marginal meta-analysis outperforms meta-analysis of module membership. Analogously to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061505#pone-0061505-g002" target="_blank">Figure 2</a>, each bar summarizes the gene screening success of the corresponding meta-analysis methods for each of the simulated traits. For each meta-analysis method we rank the genes based on the method and retain the top 50 genes. We define gene screening success as the average correlation of these top 50 genes with the trait of interest in an independent validation data set, averaged over the validation sets in each application. Each bar represents the gene screening success; error bars give the corresponding standard deviation of the observed gene–trait correlations in the top 50 genes.</p

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

<p>The 3 barplots show enrichment values, defined as negative of the enrichment p-value, , in our 3 applications. Each bar summarizes the best enrichment values obtained by the corresponding meta-analysis method. Specifically, for each method we computed the enrichment in the corresponding “gold standard” list of genes. The enrichment was calculated in the top 20, 40, 60, …, 1000 genes in the adenocarcinoma and mouse TC applications; and in 100, 200, …, 5000 genes in the aging application. The best 20% of enrichment values were retained. Each bar represents the mean of these best enrichment values, and error bars give the corresponding standard deviations. The standard deviations are not corrected for auto-correlation of enrichment values. The Kruskal-Wallis test p-value is indicated in the title. The figure shows that meta-analysis of membership in consensus modules leads to gene lists with higher enrichment and hence better biological interpretability.</p

Overview of data sets used in this article.

<p>Column # samples lists the number of samples (after our removal of potential outliers) in each data set. MSAS, Multi-Site Adenocarcinoma Study; HLM, Moffit Cancer Center; DFCI, Dana-Farber Cancer Institute; MSKCC, Memorial Sloan-Kettering Cancer Center; WB, whole blood; PMP, postmenopausal.</p

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

<p>The 3 barplots show validation success in our 3 applications. Each bar summarizes the gene screening success of the corresponding meta-analysis method. Specifically, we rank the genes using each meta-analysis method and retain the top 100 genes. We define gene screening success as the average correlation of these top 100 genes with the trait of interest in an independent validation data set, averaged over the validation sets in each application. Each bar represents the gene screening success; error bars give the corresponding standard deviation of the observed gene–trait correlations in the top 100 genes. This figure shows that, overall, marginal meta-analysis leads to gene lists with better validation success (i.e., higher correlation with the trait of interest in validation data). Adenocarcinoma expression data (panel A) present an exception in that meta-analysis of module membership results in gene lists with somewhat better validation.</p

Overview of meta-analysis methods used in this article.

<p>The Method and Variant columns list the names for each method that are used throughout the text and in our Figures. Var. imp. stands for a general variable importance measure; the Trafo. column indicates how the input is transformed before calculating a meta-analysis statistic; the Weights columns indicates the weights used in the calculation of the meta-analysis statistic via <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061505#pone.0061505.e058" target="_blank">Equations 4</a> or 5.</p

PTEN re-expression regulates transcription factor expression and activity in PTEN-inducible <i>Pten^{Δloxp/Δloxp}</i> MEFs.

<p>(A) Schematic illustration of rationale and approach used in this study. To identify PTEN-controlled TFs, their activities (TFAs) in PTEN inducible system were first derived from expression of their target genes by NCA. The perturbed TFs were then examined further in mouse models and human cancers. (B) Immunoblot showing PTEN expression levels at 0, 1, and 2 days after treatment with the indicated concentration of Doxycycline in <i>Pten^{Δloxp/Δloxp}</i> MEFs. Isogenic WT cells (<i>Pten^L/L</i>) were used as a positive control. (C) Heatmaps showing the changes of expression and activity (TFA) of transcription factors in fold and log₁₀ transformed p-value of the z-test, respectively, caused by PTEN re-expression for 1 day (1/0) or 2 days (2/0).</p

Enhanced robustness of TFA-based signatures in predicting PTEN status in human cancer.

<p>(A) The Kaplan-Meier survival curves of patients with brain tumors stratified according to PTEN-controlled TFA and IHC analyses. (B) Log₁₀ transformed p-values of the χ² test evaluating the association of PTEN status with the hierarchical clustering-determined groups of human tumors. Clustering results are based on PTEN-controlled TFAs (red; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031053#pone-0031053-g003" target="_blank">Figure 3</a>), prostate cancer-related TFAs (blue), TFA-based (gold) and gene expression-based (green) signatures derived from PTEN IHC data in breast tumors. When three major clusters are observed in prostate and breast cancers, the χ² tests are performed to associate different PTEN status in group 1 and groups 2 plus 3.</p

PTEN-regulated TFAs are significantly increased in murine prostate cancer models <i>in vivo</i>.

<p>(A) Heatmap showing changes of PTEN-regulated TFAs in PTEN inducible MEFs (PTEN null compared to PTEN re-expression or PTEN WT) and murine prostate cancer models (compared to WT control mice; Rapa: Rapamycin treatment). TFAs regulated by PTEN/AKT/mTOR pathway are marked in bold. TFAs exhibit discordant regulation between c-Myc and the PTEN/AKT/mTOR pathway are marked by *. The purple and green asterisks indicate Myc-activating and suppressing TFs respectively. (B) Triangle diagram summarizing the TFAs regulated by PTEN, AKT/mTOR and/or c-MYC.</p

Subsets of PTEN-controlled TFAs preferentially function in specific types of tumors.

<p>(A) t-test p-values comparing TFAs of the subgroups based on PTEN status and clustering results as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031053#pone-0031053-g005" target="_blank">Figure 5</a> in three tumor types. In the t-tests performed on of prostate and breast cancers, PTEN positive samples in group 3 were used as the PTEN positive functional status, the PTEN negative samples in group 1 as the PTEN negative functional status. Similarly, in brain tumor PTEN IHC positive samples in group 2 and PTEN IHC negative samples in group 1 were selected for representing PTEN positive and negative functional status respectively. The red line highlighted the 6 TFAs significantly (p<0.05) altered between tumor subgroups in three tumor types. (B) Venn diagram summarizing the overlap of the TFAs that contribute to the discrimination of tumor subgroups with different PTEN status in different tumor types. (C–D) Heatmap of the absolute Pearson correlation coefficients between NCA-inferred TF activity profiles across the tumor samples from prostate (C), breast (D) and brain (E) cancers, indicating groups of co-active transcription factors may function together.</p