Comparisons of graph-structure clustering methods for gene expression data

Asthma-associated comorbidities in children with and without secondhand smoke exposure

BACKGROUND

Secondhand smoke (SHS) exposure is known to trigger asthma, but asthma disease severity and comorbidities in children exposed to SHS are not very well quantified.

OBJECTIVE

To identify comorbidities and understand health care usage in children with asthma exposed to SHS (cases) compared with children with asthma but without SHS exposure (controls).

METHODS

A retrospective nested matched case-and-control study was conducted with children 5 to 18 years old who were enrolled in the Pediatric Asthma Management Program. Pulmonary function testing (spirometry, methacholine challenges, and exhaled nitric oxide) and body mass index were reviewed. Influenza vaccination rates, oral steroid usage, emergency department visits, and hospitalizations were assessed. Network analysis of the 2 groups also was conducted to evaluate for any associations between the variables.

RESULTS

Cases had significantly higher body mass index percentiles (>75%, odds ratio [OR] 1.64, 95% confidence interval [CI] 1.22-2.2, P = .001). Cases were less likely to have had a methacholine challenge (OR 0.49, 95% CI 0.36-0.68, P < .001) and an exhaled nitric oxide (OR 0.6, 95% CI 0.37-0.97, P = .04) performed than controls. The ratio of forced expiration volume in 1 second to forced vital capacity and forced expiration volume in 1 second were lower in cases than in controls (P < .05). Cases were less likely to have received an influenza vaccination (OR 0.61, 95% CI 0.45-0.82, P = .001) than controls. Unsupervised multivariable network analysis suggested a lack of discrete and unique subgroups between cases and controls.

CONCLUSION

Children with asthma exposed to SHS are more likely to have comorbid conditions such as obesity, more severe asthma, and less health care usage than those not exposed to SHS. Smoking cessation interventions and addressing health disparities could be crucial in this vulnerable population.

Using qualitative probability in reverse-engineering gene regulatory networks

This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.

A comparison of four clustering methods for brain expression microarray data

Background

DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.

Results

k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia.

Conclusion

Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

Background

The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).

Results

We developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.

Conclusion

The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.