Quantitative assessment of gene expression network module-validation methods

Li, Bing; Zhang, Yingying; Yu, Yanan; Wang, Pengqian; Wang, Yongcheng; Wang, Zhong; Wang, Yongyan

doi:10.1038/srep15258

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Published: 16 October 2015

Quantitative assessment of gene expression network module-validation methods

Bing Li^1,2,
Yingying Zhang¹,
Yanan Yu¹,
Pengqian Wang¹,
Yongcheng Wang³,
Zhong Wang¹ &
…
Yongyan Wang¹

Scientific Reports volume 5, Article number: 15258 (2015) Cite this article

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Abstract

Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.

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Introduction

Modularity is a common characteristic of omics-based biological networks^1,2,3. Module-based analyses that investigate or deconstruct omics-based biological networks have become a hot topic in recent years^4,5. Various types of algorithms have been proposed to identify modules (also known as communities, clusters and subnetworks), including network clustering^6,7, heuristic search^8,9, seed extension¹⁰, topology network^11,12 and matrix decomposition^13,14. However, in contrast to the large number of module detection methods⁴, there are few methods for module validation and evaluation. How to evaluate the accuracy and validity of modules has become a new challenge for researchers. Most previous studies used function enrichment methods to evaluate modules based on functional annotations, such as GO, MIPS and KEGG^{15,16,17,18,19,20,21,22,23,24}. However, some modules may be enriched with too many functions, whereas others may be enriched without any functions and the background annotation database itself is constantly being updated. Other studies used molecular biological experimental techniques to verify the co-expression, transcription regulation or other interaction relationships among members of a given module^{25,26,27,28,29,30}. However, this method is only suitable for small modules that consist of only a few nodes and it is nearly impossible to perform this method for a larger module. Thus, in the era of Big Data and omics revolution, an arising challenge is to explore rational strategies to validate biological network modules.

Several published studies have employed Computational Validation Approaches based on Modular Architecture (CVAMA) to evaluate modules’ authenticity, reproducibility and significance or to identify phenotype-related functional modules^{31,32,33,34,35}. These approaches are not limited by module size and supporting databases. With an increasing number of omics technologies and module analysis methods, CVAMA may become the new focus. In this paper, we summarized the available CVAMA methods, which were divided into topology-based approaches (TBA) and statistics-based approaches (SBA). One representative method of each was selected to validate modules obtained from genomic datasets and comparative analyses were performed to illuminate the feasibility and challenges in CVAMA.

Results

Topology-based approaches (TBA) for module validation

A module may have several topological features, such as modularity², connectivity³⁶, density^36,37, clustering coefficient³⁷, degree³⁸ and edge betweenness³⁹. Module detection methods may focus on one or a few topological criteria and it is also essential to determine whether the identified modules have a modular structure. Therefore, we may use a single or composite topological index to evaluate whether a module is valid (Table 1). Any single topological index used to validate a module should be independent of the methods used to identify the module, such as the network perplexity index of Entropy^40,41. The entropy increases when the data are more uniformly distributed; therefore, a good quality module is expected to have a low entropy^42,43. Topological indexes, including intra-modular connectivity⁴⁴ and NB value²⁶, have been applied to evaluate whether the intra-modular structure is different from other parts of the whole network. Other indexes, such as compactness⁴⁵ and weak community⁴⁶, can be used to select good clusters from integrated clustering results. Because a single topological index is not likely to provide a global evaluation of the modular structure, an alternative choice is to combine multiple topological indexes into an integrated measure to assess a module’s validity. Both internal and external indexes, such as density, connectivity and tabulation-based module preservation statistics, can be integrated to validate the existence of a module^35,47. Based on a global view of the modular structure, it may be advantageous to aggregate multiple module evaluation statistics into summary preservation statistics. In our study, we validated five preserved modules whose Zsummary value (an integrated index) was greater than 2.

Table 1 Topology- and statistics-based methods for module validation.

Full size table

Statistics-based approaches (SBA) for module validation

In addition to topological criteria, a module should also be statistically significant, which means that the modular architecture distribution ought to be highly unlikely to be obtained by chance in a randomized network. Moreover, exploring the relationship between modules and various phenotypes or identifying consistent modules may also require significance testing^31,33,48,49. For this reason, SBA is an important process to assess a module’s stability, phenotypic correlation or significance of consistency (Table 1). For responsive modules or module biomarker identification^50,51,52, binary or mixed integer linear programming models can be used to validate the causal or dependent relations between network modules and biological phenotypes^34,53,54. In phylogeny, resampling approaches are defined as a confidence measure for splits in a phylogenetic tree and are used to calculate consensus trees⁵⁵, which can also be used to assess the robustness of modules in network analysis^25,56. A permutation test with a p value calculated by empirically estimating the null distribution can be adopted to determine whether the module composition is higher than expected by chance or associated with the disease being investigated^57,58. Moreover, given two or more networks, comparative network analysis is often used to identify modules across networks or species and these modules are defined as consensus or conserved modules^31,59. Moreover, a module’s “reproducibility” can also be assessed, i.e., to what extent a module obtained from one network is compatible with modules in another network^{46,55,60,61,62}. In our study, by using hierarchical clustering, we identified 66 statistically significant modules based on approximately unbiased (AU) p-values.

Module identification based simply on topological criteria or statistical significance may not discover certain types of biologically meaningful modules⁶³. Because disparate results can be obtained from the same network with different algorithms, functional validation can be used to evaluate the performance of different module identification methods⁶⁴. Although it is not our focus in this study, we summarize and list functional module validation methods reported in the published literature (Supplementary Table 1). Typically, the most widely used functional validation method is functional homogeneity evaluation^65,66, with indexes such as functional enriched p value^7,45,67 and R score^7,68. Furthermore, the index of quantitative score based on function enrichment analysis may be applied to assess a module’s confidence level²¹ or disease relationship⁶⁹. Moreover, known protein complex matching can also provide functional evidence for modules and the commonly used indexes include the overlapping score (OS)^7,70 and F-measure^67,71. Other measurements, such as the positive predictive value (PPV), accuracy and separation, can also be used⁷². For small modules, the experimental techniques of molecular biology, such as real-time quantitative PCR (qPCR), western blotting and siRNA knock-down, may be applied to validate the co-expression, co-regulation or other interaction relations among the genes or proteins within a module^25,26,27,28.

Homogeneity of different models of TBA on the same dataset

Both Zsummary and medianRank are integrated topological indexes of module preservation. We applied these two models to evaluate modules identified from the same dataset (GSE24001), which was derived from 30 newly diagnosed infant acute lymphoblastic leukemia samples. Modules were identified by the Weighted Gene Co-Expression Network Analysis (WGCNA) R package⁷³, setting 3 as the minimum module gene number. Each module was detected based on a hierarchical cluster tree and was labeled by colors (Fig. 1A). The validation results are shown in Fig. 1B,C. Five preserved modules (Zsummary ≥ 2) all had a relatively low medianRank values and the most strongly preserved module (Zsummary = 13) had the lowest medianRank value. Similarly, the two modules that had the highest medianRank value (medianRank = 10) were both unpreserved.

Example modules validated by two models of TBA

Among the 5 preserved modules, the turquoise module, which had 250 nodes and 30,833 edges, was most strongly preserved (Zsummary = 13), having the lowest medianRank value (medianRank = 2) (Fig. 2A). The preserved yellow module, which was composed of 64 nodes and 1,970 edges, had a low medianRank value (medianRank = 5) (Fig. 2B). Among the 4 unpreserved modules, the red module, consisting of 23 nodes and 253 edges (Fig. 2C) and the magenta module, consisting of 5 nodes and 9 edges (Fig. 2D), had the highest medianRank values (both medianRank = 10).

Variability of TBA (Zsummary) and SBA (AU P-value) results on the same dataset

Based on the same dataset (GSE24001), we compared the validation results of TBA and SBA, choosing Zsummary and AU p-value as the representative methods for each approach. For Zsummary, 5 preserved modules were validated from 9 modules. The Zsummary value was dependent on the module size, which was consistent with previous studies³⁵ (Fig. 1D). The AU p-value used to access the modular architecture distributional probability was computed to search for significant modules (clusters). As shown in Fig. 1E, 66 modules with 3 or more genes and an AU p-value larger than 0.95 are highlighted by rectangles, which are strongly supported by the gene expression data. Different numbers of detected modules and valid modules were obtained through the two methods.

Multiple comparisons of Zsummary and medianRank on 10 datasets

For the same modules identified by WCGNA, we applied the integrated topological indexes Zsummary and medianRank to 10 datasets to further compare the results of module preservation evaluation. The average size of modules obtained from the 10 datasets is shown in Fig. 3A. Because medianRank was a relative preservation index without a cutoff value, we compared the top 10 ranked modules validated by Zsummary and medianRank (Fig. 3B). We failed to obtain a valid Zsummary value in two datasets (GSE6448, GSE29230) when the minimum module size was set at 3. For the other 8 datasets, overlapping preserved modules validated by Zsummary and medianRank were found in 7 datasets and consistent ranked modules were observed in 6 datasets, demonstrating the consistency of the two indexes. However, no overlapping modules in the top 10 preserved modules were found in one dataset (GSE4882).

Zsummary analysis was impeded by small module size

Because we failed to obtain a valid Zsummary value in two datasets (GSE6448, GSE29230) with a minimum module size of 3, we changed this cutoff value from 4 to 10. Then, valid Zsummary values were acquired in both datasets and the percent of preserved modules was stable (Fig. 3C) due to the too small density or connectivity, leading to invalid Zsummary values when the minimum module size was set at 3.

Comparison of TBA (Zsummary) and SBA (AU P-value) results for 10 datasets

As mentioned above, Zsummary is a TBA index and AU P-value is an SBA index. Based on 10 datasets, we compared the performance of these two types of index. In this application, modules with 3 or more genes were considered as valid modules. The proportions of valid (preserved or significant) modules obtained by these two indexes from 10 datasets are shown in Fig. 3D,E. For different methods and datasets, both the module number and the proportion of valid modules varied greatly. Overall, the Validation Success Ratios (VSR) of Zsummary and AU P-value were 51% and 12.3%, respectively (Fig. 3F). This indicated that Zsummary obtained a higher ratio of valid modules (invalid values from two datasets were deemed as zero). A prior study adopted Zsummary to validate CASTxB6 female liver modules with 9 other expression datasets and revealed an average VSR of 86.44%⁷⁴. However, Zsummary also had a higher Fluctuation Ratio (FR, 80.92%) than AU P-value (45.84%), indicating that the stability of the AU P-value results was superior to that of Zsummary (Fig. 3F).

Correlation between the network parameters and the ratio of valid modules

To further determine which network parameters influence the ratio of valid modules of the Zsummary and AU P-value methods, we selected 6 main network parameters of the 10 datasets (Supplementary Table 2), i.e., modularity, density, clustering coefficient, characteristic path length, network heterogeneity and network centralization. Linear regression analysis indicated that none of these 6 parameters was correlated with the valid module ratio of the Zsummary and AU P-value methods (Fig. 4). This implied that the valid module ratio of genomic networks may not be influenced by a single network parameter.

Impact of gray area variation on 8 datasets

The gray area was the region of gray genes that was not assigned into any module and labeled in gray by WGCNA. Except for the two datasets (GSE6448 and GSE29230) without valid Zsummary values, 8 datasets were simulated by changing the gray area genes’ expression levels to 0.1, 0.5, 0.9, 1.1, 1.5 and 2 times that of the original dataset. Based on each simulated dataset, WGCNA (an R package used to compute Zsummary) and pvclust (an R package used to compute AU p-value) were performed for module identification and validation. For WGCNA, 5 datasets (GSE2283, GSE12148, GSE6738, GSE12520 and GSE4882) had no changes at the 6 simulated levels compared with the original datasets (a change in Zsummary less than the cutoff value was considered as no change). The changes in the number of modules or gray genes (only for WGCNA) and Variation Ratio (VR) for the remaining 3 datasets are shown in Fig. 5A–C. For pvclust, changes were observed in all 8 datasets compared with the original datasets and the changes in the module number and VR are shown in Fig. 5D–K. No correlation was found between the VR and the simulated levels in the changed datasets.

Comparison of TBA and SBA by Gray area simulation

For the 8 datasets, the average VRs of WGCNA and pvclust at the 6 simulated levels are shown in Fig. 6A. With regard to WGCNA, only the VRs in the changed datasets were calculated. The VRs of WGCNA and pvclust in all simulated datasets can be seen in Fig. 6B. WGCNA had a higher VR (8.43%) for module identification but a lower VR (8.10%) for module validation. By contrast, pvclust had a lower VR (1.29%) for module identification and a higher VR (14.06%) for module validation. Thus, the gray area changes had different impacts on the two models in terms of module identification and validation. Moreover, the VSR and FR of TBA (Zsummary) and SBA (AU p-value) were stable at each simulated level (Fig. 6C). Zsummary had a higher VSR and FR at each simulated level (2 datasets with invalid Zsummary values were not included). When data at all 6 simulated levels were aggregated, the VSR of Zsummary and AU p-value was 63.82% and 12.30% and the FR was 55.84% and 51.42%, respectively.

Discussion

Functional enrichment and biological experiments based on module validation methods may not satisfy the rising demands of various omics networks. As an alternative choice, CVAMA can potentially provide an analytical assessment of the structure and stability of modules captured by various partitioning methods and should be considered as a crucial tool in the interpretation of network modules. The feasibility of CVAMA was demonstrated in our applications of TBA and SBA-based module validation methods in genomic network modules. As a TBA, Zsummary had a high VSR (51%) but also a high FR (80.92%) and was impeded by small module size. As an SBA, AU p-value had a low FR (45.84%) but also a low VSR (12.3%). The Gray area simulated study showed that the VSR and FR of both TBA (Zsummary) and SBA (AU p-value) remained stable at each simulated level. Meanwhile, TBA (Zsummary) had a lower VR (8.10%) for module validation at the 6 simulated levels, indicating that the gray gene changes had little impact on the topology-based models. Although different validation results were obtained by the two types of method with different gene expression datasets and module detection methods, one may choose an appropriate validation index based on the topological structure or stability of the modules. For example, if we focus on whether a module is structurally different from the rest of the network, we may use Zsummary to assess its preservation. If we focus on whether a module is stable or robust, we may choose AU P-value to assess its confidence. Taken together, the existing methods are not ideal and further improvement is justified.

Modular analysis in genomic networks is a complicated process that involves various factors. In general, there is no “golden standard” for assessing the validity and quality of modules and different algorithms for module identification with different parameters may produce disparate module partition results^4,75,76. Thus, it is difficult to determine which module is “correct” and which module partition method outperforms others. As our application demonstrated, different types of module validation indexes for the same network may generate different outcomes. Generally speaking, each type of module validation method may have its own advantages and disadvantages and some methods may require certain conditions (e.g., data type, network pattern, or module identification method), limiting their applicability and flexibility. There is no universally acceptable approach that can perform well for all types of data under all scenarios, which results in challenges to make a clear-cut prescription for genomic module validation.

As for the possible discordance between topological criteria and biological meaning in module identification⁷⁷, methods combining both function and structure have been proposed to identify functional modules⁷⁸. Similarly, function and topology are also the two aspects of module validation. CVAMA may neglect the biological meaning and directly assess the correlation of modules with known functional annotation, which may deviate from the densely connected property. It is assumed that fusion of functional and topological evaluation may lead to a high quality selection of better modules. However, most of the existing methods in published literature focus on either function or topology and researchers may only be interested in their own subject or module identification algorithm. Therefore, module validation methods that integrate both functional and topological indexes and are independent of the vagaries of module detection algorithms need to be further explored.

For the modules obtained by clustering algorithms, internal and external cluster validation methods for assessing the quality of clustering results have been discussed in previous studies^47,79. The internal validation attempts to measure how well a given partitioning corresponds to the natural cluster structure of the data and such indexes include compactness, connectedness, separation, combinations and stability⁴¹. External validation attempts to compare the recovered structure to a priori knowledge and to quantify the match between them and such indexes include unary measures and binary measures⁷⁹. In addition to cluster quality assessment, how to estimate the optimal number of clusters has also been discussed^40,80. Generally, if a module is known to be consistent with the known knowledge, it would show stronger evidence of preservation than a module without a priori evidence, such as a known pathway or co-transcriptional regulation^17,81,82,83.

Gene interactions are dynamic in regulating the functioning of cells and organisms^84,85. A number of studies have focused on dynamic module identification, as well as the dynamic behavior of modules in networks^11,86,87. As such, module validation should not be constrained to a static situation. Modular dynamics may involve time-series molecular interaction⁸⁸, environment changes⁸⁹, phenotypic changes^90,91 and ontogenetic and phylogenetic time^92,93. Dynamic evaluation of modules in certain dynamic processes may generate more comprehensive results, which cannot be obtained in a static state. The more meaningful consequence is intertwined with greater challenges in dynamic CVAMA.

Therefore, to address the challenge of omics-based module validation, computing-based methods are an easy and feasible choice. Despite these challenges, CVAMA, in addition to functional enrichment and biological experiments, offers novel insights into module network research and may become a new paradigm in modular analysis.

Materials and Methods

Datasets and samples

Gene expression datasets were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). Eleven spotted DNA/cDNA datasets from different organisms and experiment platforms were downloaded, with the sample size ranging from 28 to 592 and the gene number ranging from 448 to 3,520. Because a test dataset was needed for Zsummary and medianRank, we selected half of the samples in each dataset as reference data and the other half as test data. The raw gene-expression information is shown in Supplementary Table 2.

Network construction and network parameter calculation

For each genomic dataset, weighted gene co-expression network analysis (WGCNA)⁷³ was used to construct a network and to detect modules. The freely available WGCNA R package and R tutorials were described in⁷³. After network construction, we exported each network to Cytoscape software⁹⁴. Network analysis was conducted with the NetworkAnalyzer⁹⁵ plugin to calculate the network topological parameters. Network modularity was calculated by the CommFinder⁹⁶ plugin in Cytoscape. The network topological parameters and modularity are listed in Supplementary Table 2.

Zsummary and medianRank module preservation statistics

Zsummary is an integrated statistic implemented in functional module preservation in the WGCNA R package³⁵. It is composed of 4 statistics related to density and 3 statistics related to connectivity that can quantitatively assess whether the density and connectivity patterns of modules defined in a reference dataset are preserved in a test dataset. A Zsummary value between 2 and 10 indicates moderate module preservation, whereas a Zsummary > 10 provides strong support for module preservation³⁵. Another integrated index, medianRank³⁵, is also composed of statistics related to density and connectivity. It is a rank-based measure to compare the relative preservation among multiple modules; a module with lower medianRank tends to exhibit stronger observed preservation.

Approximately unbiased (AU) p-value

The AU p-value, computed by multi-scale bootstrap resampling⁹⁷, was selected as a representative SBA index. The AU p-value is often used to assess the uncertainty of clustering analysis⁹⁸. In our application, the AU p-value was computed using the R package pvclust⁹⁹ with 1,000 times resampling, varying the bootstrap sample size from 0.5 to 1.4-fold the real sample size of the gene expression data. We set clusters with an AU p-value larger than 0.95 as significant modules⁹⁹.

Validation success ratio (VSR) and fluctuation ratio (FR)

The VSR and FR were defined for comparing the two types of module validation approaches on multiple datasets. The VSR (Eq. 1) was defined as the average percentage of valid modules against all modules available on multiple datasets. The FR (Eq. 2) was defined as the stability or variation degree of the percentage of valid modules on multiple datasets; a lower FR indicated that the percentage of valid modules was more stable.

where R is the ratio of valid modules on one dataset and N is the number of all available datasets.

Simulated comparisons by changing the gray area gene expression level

In WGCNA, genes that were not assigned into any module were labeled in gray. We called these gray gene regions the gray area. The gray area represented genes whose profiles were simulated to be independent (i.e., without any correlation structure). To illustrate the impact of gray area changes on module identification and validation by TBA and SBA, we changed the gray area genes’ expression levels to 0.1, 0.5, 0.9, 1.1, 1.5 and 2 times that of the original datasets to obtain simulated datasets. Based on the simulated datasets, we compared the Variation Ratio (VR, Eq. 3) of TBA (WGCNA) and SBA (pvclust) for module identification and validation.

where ΔX is the changed number of modules or genes relative to the original data and n is the number of the simulated datasets.

Additional Information

How to cite this article: Li, B. et al. Quantitative assessment of gene expression network module-validation methods. Sci. Rep. 5, 15258; doi: 10.1038/srep15258 (2015).

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Acknowledgements

The authors’ work is funded by the National Natural Science Foundation of China (90209015) and the Foundation of ‘Eleventh Five’ National Key Technologies R&D Programmer (2006BAI08B04-06). The funders had no role in study design, data ollection and analysis, decision to publish, or preparation of the manuscript.

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Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing, 100700, China
Bing Li, Yingying Zhang, Yanan Yu, Pengqian Wang, Zhong Wang & Yongyan Wang
Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, 16 Nanxiaojie, Dongzhimennei, Beijing, 100700, China
Bing Li
Brightech International, LLC, 285 Davidson Ave #504, Somerset, 08873, NJ, USA
Yongcheng Wang

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Yanan Yu
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Z.W. and Y.W. designed and coordinated the study. Y.Y., Y.Z., P.W. and B.L. performed data analysis. B.L. and Y.W. wrote and modified the manuscript. All authors reviewed, edited and approved the final manuscript.

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Li, B., Zhang, Y., Yu, Y. et al. Quantitative assessment of gene expression network module-validation methods. Sci Rep 5, 15258 (2015). https://doi.org/10.1038/srep15258

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Received: 20 March 2015
Accepted: 21 September 2015
Published: 16 October 2015
DOI: https://doi.org/10.1038/srep15258

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