Short Report  Open  Published:
Comparison of threshold selection methods for microarray gene coexpression matrices
BMC Research Notesvolume 2, Article number: 240 (2009)
Abstract
Background
Network and clustering analyses of microarray coexpression correlation data often require application of a threshold to discard small correlations, thus reducing computational demands and decreasing the number of uninformative correlations. This study investigated threshold selection in the context of combinatorial network analysis of transcriptome data.
Findings
Six conceptually diverse methods  based on number of maximal cliques, correlation of control spots with expressed genes, top 1% of correlations, spectral graph clustering, Bonferroni correction of pvalues, and statistical power  were used to estimate a correlation threshold for three timeseries microarray datasets. The validity of thresholds was tested by comparison to thresholds derived from Gene Ontology information. Stability and reliability of the best methods were evaluated with block bootstrapping.
Two threshold methods, number of maximal cliques and spectral graph, used information in the correlation matrix structure and performed well in terms of stability. Comparison to Gene Ontology found thresholds from number of maximal cliques extracted from a coexpression matrix were the most biologically valid. Approaches to improve both methods were suggested.
Conclusion
Threshold selection approaches based on network structure of gene relationships gave thresholds with greater relevance to curated biological relationships than approaches based on statistical pairwise relationships.
Introduction
To extract gene networks from microarray data, correlations are often used as a measure of gene coexpression. A typical microarray with 20,000 gene probes will produce 200 million correlations. Correlations below a threshold value, closer to zero, will be less meaningful. Hard and soft threshold approaches have been applied to biological data. Hard thresholds discard gene pairs with correlation below the threshold, while soft thresholds use the correlation value to weight gene network relationships. Zhang and Horvath [1] concluded that soft thresholds based on aggregate, modular relationships between genes gave more robust results, but data reduction by a hard threshold is often essential for computational tractability of graph algorithms.
We focus on relevance networks, created by applying a hard threshold to the gene expression correlation matrix [2], then extracting gene networks. The resulting networks have been well documented in recent literature to yield sets of coexpressed genes [3–5]. Relevance networks are easily converted to graphs, with genes as vertices, only connected by an edge if their correlation is above the threshold. A clique is a subgraph in which all nodes are connected to each other [6]. A disadvantage of using cliques is the computational requirements, which grow exponentially with number of genes. Thus hard threshold selection is required when performing clique extraction on microarray data.
Current approaches to threshold selection are typically statistically based, and do not fully reflect the connectivity of the data [7]. Methods based on statistical arguments may not necessarily yield biologically significant relationships [3, 8].
Some studies used an arbitrary threshold correlation such as 0.80 [9]. Moriyama et al. [10] obtained random correlation distributions for gene pairs by permuting their expression values and defended their choice of threshold based on statistical significance. Lee et al. [11] used the top 1% of correlations (absolute value) to build a coexpression network. Voy et al. [3] used distribution of correlations of genes with buffer spots on the arrays to select a threshold correlation value of 0.875.
However, using connectivity of the data to derive thresholds has been suggested. Langston et al. [12] recommended use of ontological distance, statistical significance and various graph structural attributes to arrive at a correlation threshold. Palla et al. [13] found that a threshold based on clique size was effective at separating networks.
Here two threshold selection methods based on correlation graph structure are compared with common statistically based methods. The graph based methods used spectral properties [14] or number of cliques to select a threshold. Objectives were to compare the various hard threshold methods for validity (retention of biological information), stability, and reliability.
Methods
Datasets
Three yeast S. cerevisiae timeseries datasets were chosen for this study: 31 arrays for Anoxia state [15], 21 arrays for Reoxygenation state [15] and 18 arrays from yeast cultures synchronized using Alphafactor arrest [16]. Data are available on Gene Expression Omnibus under GSE2246, GSE2267 and GSE22. Extensive GO annotation for S. cerevisiae genes influenced the selection. Exploratory data analyses within each dataset using PCA, box plots and pairwise correlations between arrays found no outlier arrays. Quantile plots showed data were normally distributed, and distribution of correlations among gene expression profiles had the expected bellshaped curve, so all data were used.
Software
Software written by Langston and colleagues (University of Tennessee) was used, including Datagen version 1.4a for computing correlations, maximal clique enumeration code version 2.0.1 [17], spectral analysis code [14], and GO Pairwise Similarity analysis code version 1.0. Matrix calculations for spectral graph analysis were carried out in MATLAB 7.0. Pvalues were calculated in SAS version 9.1 (Cary; NC). Statistical power was calculated using PASS statistical software http://www.ncss.com/pass.html.
Threshold Estimation
Six conceptually different approaches were evaluated:

1)
Numbers of maximal cliques were calculated at each potential correlation threshold, starting at r = 0.99. The threshold was lowered, in steps of 0.01, and number of maximal cliques increased due to greater connections among genes. When clique number increased two times (Maximal Clique2) or three times (Maximal Clique3) the previous value, that correlation was chosen as the threshold.

2)
For each potential threshold correlation value, spectral graph theory [18] was used to decompose the resulting graph into eigenvalues and eigenvectors, which were used to enumerate spectral clusters [19]. As the potential threshold was incrementally lowered in steps of 0.01, a peak in the number of clusters occurs, and the threshold is chosen to maximize cluster number. Details are in [14].

3)
Correlations of control spots with all other genes on the array were calculated, creating a null distribution. The 99th percentile correlation value (absolute value) of this distribution gave the threshold.

4)
The top 1% of all correlations (absolute value) among genes was used to estimate a threshold [11]. Correlations were ranked, and the correlation at the 99th percentile was the threshold estimate. Note that the control spot method uses a different subset of correlations (only with control spots), whereas this method uses all correlations among genes.

5)
A pvalue for every correlation was computed, testing if the correlation was zero (Fisher's ztransformation). Threshold estimate was the correlation value corresponding to the critical Bonferroni pvalue, 0.05/number of correlations. This threshold will remove any correlations that are statistically equal to zero.

6)
Statistical power calculations were used to find the correlation value that gave an 80% chance of rejecting the null hypothesis, Ho: correlation = 0. Type I error rate in these calculations was Bonferroniadjusted to correct for multiple testing.
Further details on computing these threshold estimation methods are in the Additional file 1.
Performance Evaluation
Performance of the threshold estimation methods was evaluated by comparison to a biologically based Gene Ontology threshold. GO data used was gene_ontology_edit.obo.20080501.gz. The biological meaning for each correlation bin (in 0.01 increments) was the average of functional similarity scores for all gene pairs within that correlation bin. Functional similarity for a pair of genes was defined as log(n/N)/log(2/N), where n is the number of genes in the lowest GO category that contained both genes, and N is the total number of genes annotated for the organism. The formula normalizes Functional similarity to a 0 to 1 range, and a value of 1 means the GO category contained only the two genes being considered (perfect similarity). GO threshold estimate was defined as the correlation at which change in average functional similarity exceeded median change plus half its standard deviation, thus identifying where biological information begins to accumulate.
To study stability of the methods, 10,000 block bootstrap samples were created by sampling arrays with replacement from each block. Blocks were defined to be 2 or 3 adjacent time periods, such that each block contained 3 or 4 arrays. Block bootstrapping was necessary to preserve as much as possible the timecourse dependency structure of the experiments [20]. For each of the 10,000 samples, a threshold estimate was calculated by each method, and the distribution of these thresholds was used to compare threshold methods for stability.
Results
Functional similarity scores for the three datasets are displayed in Figure 1. Changes in scores across correlation values were similar for all datasets, and the lack of GO term relationship for negative correlations is striking. Because of this, the GO threshold was defined by the curve for positive correlations. Biological relationship begins to increase sharply above a correlation value of 0.80, and this produced the GO thresholds in Table 1.
Estimated thresholds obtained by each method are listed in Table 1 for the three datasets. If estimated threshold is higher than the biological threshold, false negatives will occur, because data reduction by the higher threshold will remove real relationships. Conversely, using a threshold below the biological threshold will create false positives, and relationships that are not real would be included in the network. In discoverybased settings, false positives are more acceptable, as they can be removed with further validation. Thus methods that estimate a lower threshold are preferred. Maximal Clique2 and Spectral Clustering performed better than the other methods, based on summed absolute deviations from GO threshold (Table 1). Maximal Clique2 was further from the GO threshold, but might be preferred since it never exceeded that threshold.
The estimated threshold derived for selected methods for each dataset is compared to bootstrap distributions in Table 2. The best methods from above, Maximal Clique2 and Spectral Clustering, and two other methods for comparative purposes were chosen for this analysis. The bootstrap mean was never less than the estimated threshold, and occasionally was two standard deviations above. This upward bias in correlation is expected, as each time period had a limited number of arrays, making it likely that the identical array would be resampled. However, Maximal Clique and Spectral Clustering methods showed more resistance to this bias. The bootstrap standard deviation measures ability of the methods to produce similar threshold estimates from randomized arrays. Again the networkbased methods showed the lowest standard deviations, and highest stability. All methods showed poorest performance with the Alpha dataset, possibly due to its unreplicated design. This makes it less likely that all time levels would be represented in the bootstrap samples, whereas the other datasets had glucose and galactose biological replicates.
Discussion
The two networkbased methods, Maximal Clique2 and Spectral Clustering, performed very well in terms of bootstrap stability and biological validity. Though Maximal Clique2 method gave thresholds close to the biological threshold, and always below, the method had slightly higher bootstrap standard deviations. The robustness of the Maximal Clique2 algorithm could be enhanced by exclusion of smaller cliques in the graph, for example cliques of size 3. Spectral Clustering thresholds were on average closer to biological thresholds, but too often exceeded it. However, if all thresholds for Spectral Clustering were lowered by 0.05, it would have been clearly the best method. Further finetuning of the parameters in the algorithm (size of sliding window, different tolerance levels for cluster formation) may improve the method's validity. In a recent paper, Almendral and DíazGuilera [21] documented the sensitivity of the nonzero eigenvalue to network changes. All methods had subjective settings, and further work on many more species and experiments would be needed to establish best choices.
The results from this study complement the work of Zhang and Horvath [1] which concluded that thresholds based on the scalefree topology  the formation of hubs and denselyconnected subgraphs  produced more robust results. The statisticallybased methods studied here are directly dependent on the correlation distribution and thus were unable to capture biological relationships. Although the ControlSpot method is based on logical reasoning, the high correlation of control spots with other genes on the arrays weakened the method's validity. The Top 1% Correlations method is arbitrary, and failed to capture biological relationships. Statistical considerations used for the Power and Bonferroni methods were also not able to identify biological relationships, reflecting the wellknown discrepancy between biological and statistical significance. Experiments that are small will produce thresholds that are too high, while large experiments will give excessively low thresholds, even though the biological relationships are the same.
The GO similarity measure of biological validity we have used, however, is by no means perfect and is just one way of quantifying biological information. Khatri and Draghici [22] have listed limitations of GO in detail. We also found low GO scores at high negative correlations as compared to the high GO score associated with high positive correlations for all three datasets. The drop in GO score at high negative correlations could be due to several reasons, for example experimental and analytical limitations to detect biologically negative correlations among genes, and limited gene annotations [11]. As the quantification of biological information in data gets more precise, the selection of thresholds should become easier. In fact, note that a method like the GO threshold used here would be a logical choice if GO information were complete and accurate.
References
 1.
Zhang B, Horvath S: A general framework for weighted gene coexpression network analysis. Stat Appl Genet Mol Biol. 2005, 4:
 2.
Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS: Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci USA. 2000, 97 (22): 1218212186. 10.1073/pnas.220392197.
 3.
Voy BH, Scharff JA, Perkins AD, Saxton AM, Borate B, Chesler EJ, Branstetter LK, Langston MA: Extracting gene networks for lowdose radiation using graph theoretical algorithms. PLoS Comput Biol. 2006, 2 (7): e8910.1371/journal.pcbi.0020089.
 4.
Yan X, Mehan MR, Huang Y, Waterman MS, Yu PS, Zhou XJ: A graphbased approach to systematically reconstruct human transcriptional regulatory modules. Bioinformatics. 2007, 23 (13): i577586. 10.1093/bioinformatics/btm227.
 5.
Freeman TC, Goldovsky L, Brosch M, van Dongen S, Maziere P, Grocock RJ, Freilich S, Thornton J, Enright AJ: Construction, visualisation, and clustering of transcription networks from microarray expression data. PLoS Comput Biol. 2007, 3 (10): 20322042. 10.1371/journal.pcbi.0030206.
 6.
Baldwin NE, Chesler EJ, Kirov S, Langston MA, Snoddy JR, Williams RW, Zhang B: Computational, integrative, and comparative methods for the elucidation of genetic coexpression networks. J Biomed Biotechnol. 2005, 2005 (2): 172180. 10.1155/JBB.2005.172.
 7.
Butte AJ, Kohane IS: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000, 418429.
 8.
Quackenbush J: Genomics. Microarraysguilt by association. Science. 2003, 302 (5643): 240241. 10.1126/science.1090887.
 9.
Sanoudou D, Haslett JN, Kho AT, Guo S, Gazda HT, Greenberg SA, Lidov HG, Kohane IS, Kunkel LM, Beggs AH: Expression profiling reveals altered satellite cell numbers and glycolytic enzyme transcription in nemaline myopathy muscle. Proc Natl Acad Sci USA. 2003, 100 (8): 46664671. 10.1073/pnas.0330960100.
 10.
Moriyama M, Hoshida Y, Otsuka M, Nishimura S, Kato N, Goto T, Taniguchi H, Shiratori Y, Seki N, Omata M: Relevance network between chemosensitivity and transcriptome in human hepatoma cells. Mol Cancer Ther. 2003, 2 (2): 199205.
 11.
Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P: Coexpression analysis of human genes across many microarray data sets. Genome Res. 2004, 14 (6): 10851094. 10.1101/gr.1910904.
 12.
Langston MA, Perkins AD, Saxton AM, Scharff JA, Voy BH: Innovative Computational Methods For Transcriptomic Data Analysis: A Case Study in the Use Of FPT For Practical Algorithm Design and Implementation. ACM symposium on Applied Computing: 2006; Dijon, France. 2006
 13.
Palla G, Derenyi I, Farkas I, Vicsek T: Uncovering the overlapping community structure of complex networks in nature and society. Nature. 2005, 435 (7043): 814818. 10.1038/nature03607.
 14.
Perkins AD, Langston MA: Threshold selection in gene coexpression networks using spectral graph theory techniques. BMC Bioinformatics. 2009, 10 (Suppl 11): S410.1186/1471210510S11S4.
 15.
Lai LC, Kosorukoff AL, Burke PV, Kwast KE: Metabolicstatedependent remodeling of the transcriptome in response to anoxia and subsequent reoxygenation in Saccharomyces cerevisiae. Eukaryot Cell. 2006, 5 (9): 14681489. 10.1128/EC.0010706.
 16.
Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive identification of cell cycleregulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 1998, 9 (12): 32733297.
 17.
Zhang Y, AbuKhzam FN, Baldwin NE, Chesler EJ, Langston MA, Samatova NF: Genomescale computational approaches to memoryintensive applications in systems biology. Supercomputing 2005 Proceedings of the ACM/IEEE SC Conference: 2005. 2005, 12
 18.
Chung FRK: Spectral Graph Theory. 1994, American Mathematical Society
 19.
Ding CHQ, He X, Zha H: A spectral method to separate disconnected and nearly disconnected Web graph components. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California. 2001, 275280. full_text. [http://ranger.uta.edu/~chqding/papers/kdd3a.ps]
 20.
Politis DN: The impact of bootstrap methods on time series analysis. Statistical Science. 2003, 18 (2): 219230. 10.1214/ss/1063994977.
 21.
Almendral JA, DiazGuilera A: Dynamical and spectral properties of complex networks. New Journal of Physics. 2007, 9: 18710.1088/13672630/9/6/187.
 22.
Khatri P, Draghici S: Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics. 2005, 21 (18): 35873595. 10.1093/bioinformatics/bti565.
Acknowledgements
This research has been supported in part by the National Institutes of Health under grants P01DA01502701, R01HD05247202, R01MH07446001, U01AA013512 and U01AA01364104 and by the UTORNL Science Alliance. Dr. E.J. Chesler was supported by NIAAA Integrative Neuroscience Initiative on Alcoholism under grants U01AA13499 and U24AA13513. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC0205CH11231. Additional support was provided by the University of Tennessee Genome Science and Technology program. John Eblen, Andy Perkins, Gary Rogers and Yun Zhang helped with basic issues of algorithm synthesis. Drs. Bing Zhang and Roumyana Yordanova provided valuable comments on certain aspects of this study.
Author information
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
BRB wrote code for the analyses, summarized results, and drafted the paper. All authors were involved in study design, and read and approved the final manuscript.
Authors’ original submitted files for images
Rights and permissions
About this article
Received
Accepted
Published
DOI
Keywords
 Spectral Cluster
 Maximal Clique
 Threshold Estimate
 Biological Relationship
 Spectral Graph