Hydrogen peroxide room disinfection -ready for prime time?

Benedikt D Huttner firstname.lastname@example.com
Infection Control Program and Division of Infectious Diseases
Faculty of Medicine
Geneva University Hospitals
Rue Gabrielle-Perret-Gentil 41205GenevaSwitzerland

Stephan Harbarth
Infection Control Program and Division of Infectious Diseases
Faculty of Medicine
Geneva University Hospitals
Rue Gabrielle-Perret-Gentil 41205GenevaSwitzerland

Hydrogen peroxide room disinfection -ready for prime time?
10.1186/s13054-015-0915-8C O M M E N T A R Y Open Access See related research by Blazejewski et al., http://ccforum.com/content/19/1/30 * Correspondence:
Non-manual techniques for terminal disinfection of hospital rooms have gained increasing interest in recent years as means to reduce transmission of multidrugresistant organisms (MDROs). A prospective crossover study by Blazejewski and colleagues in five ICUs of a French academic hospital with a high prevalence of MDRO carriers showed that two different hydrogen peroxide (H 2 O 2 )-based non-touch disinfection techniques reduced environmental contamination with MDROs after routine cleaning. This study provides further evidence of the 'in use' bioburden reduction offered by these techniques. Before H 2 O 2 -based non-touch disinfection can be recommended for routine clinical use outside specific outbreak situations, further studies need to show whether the environmental contamination reduction provided by these techniques is clinically relevant and results in reduced cross-infections with MDROs.Commentary

Many people associate the smell of hospitals with the smell of disinfectants, and few people would feel comfortable being hospitalized in a facility that is not free of visible dirt. Since the close environment of the patient gets contaminated with microorganisms originating from its human occupant, hospital rooms usually undergo terminal cleaning with conventional 'hands-on' disinfection techniques (by mechanically applying a disinfecting product) after the patient has left the room. These techniques are highly operator-dependent and difficult to standardize and leave room for error. In recent years, non-manual disinfection techniques (sometimes called 'non-touch disinfection') via hydrogen peroxide (H 2 O 2 ) vapor or aerosol, In a recent article in Critical Care, Blazejewski and colleagues [1] describe the results of a prospective crossover study in a French tertiary care center examining the impact of terminal disinfection with two different nonmanual H 2 O 2 disinfection techniques on environmental contamination with multidrug-resistant organisms (MDROs). The authors should be congratulated for the rigorous setup and environmental sampling in this study.

After routine terminal cleaning with a quaternary ammonium compound and a sodium hypochlorite solution, rooms were disinfected by either H 2 O 2 vapor or aerosolized H 2 O 2 combined with peracetic acid during 6 weeks with a switch to the other methodology for another 6 weeks. The primary outcome was environmental contamination of a room with any MDRO defined as methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa resistant to ceftazidime or imipenem, extended spectrum β-lactamase Enterobacteriaceae (ESBL-E), imipenem-resistant Acinetobacter baumannii, or vancomycin-resistant enterococci. Extensive environmental sampling of 24 high-touch surfaces was performed after patient discharge, after routine terminal cleaning, and after H 2 O 2 disinfection. Both H 2 O 2 techniques reduced environmental MDRO contamination from 11 of 182 rooms after terminal cleaning to 1 of 182 rooms after H 2 O 2 disinfection.

How should those findings be interpreted, and do the results support the widespread use of non-manual H 2 O 2 -based disinfection techniques? First of all, it is worth mentioning that the setting in which the study was carried out is somewhat atypical. In the study hospital, all 46 rooms in the five included ICUs were singleoccupancy rooms, hardly the standard situation in most hospitals. Apart from providing excellent conditions for infection control, this setup greatly facilitates the use of H 2 O 2 techniques that can be applied only in empty rooms because of toxicity risk. Thus, ICUs with a less advantageous architectural setup may find it hard to introduce H 2 O 2 terminal disinfection into routine practice.

A further particularity worth pointing out is the high prevalence of MDRO carriage among room occupants. A strikingly high proportion (42%) of all patients in this study was colonized with ESBL-E (24%), resistant P. aeruginosa (10%), MRSA (8%), or imipenem-resistant A. baumanii (6%). This high prevalence could be related to silent MDRO transmission clusters or a very active MDRO screening policy. Despite this high prevalence of MDRO carriers and extensive environmental screening of surfaces, only 23 (1.5%) of 1,456 sampled surfaces and 15 (8%) of 182 rooms were MDRO-positive after patient discharge, before any disinfection. Interestingly, the environmental MDRO contamination reduction provided by H 2 O 2 disinfection after routine cleaning was uniquely attributable to a reduction in ESBL-E (indeed, none of the other MDROs was identified after routine cleaning), which were isolated mostly from sinks, where other environmental infection control measures may be easily applicable [2]. With regard to ESBL-E, there are probably differences between species in the degree of environmental contamination (higher for Klebsiella spp. than for Escherichia coli), but unfortunately the ESBL-E species were not reported in this study [3].

Surprisingly, only 27 of 74 MDRO carriers were identified as such at ICU admission, suggesting that a nonnegligible number of patients may have acquired MDROs during their ICU stay despite the enhanced terminal disinfection. This confirms that other routes of transmission (for example, through the hands of health-care workers) play an important role [4].

Given the results of the present and numerous other studies, there is little doubt that MDROs contaminate the environment and that H 2 O 2 -based techniques can reduce this environmental contamination [5][6][7]. H 2 O 2based techniques thus improve 'cleanliness' , but does it matter? Environmental contamination is only a surrogate marker and reducing it cannot be a goal per se given the significant resources in time and money associated with these techniques. The real question is whether the environmental contamination reduction offered by non-manual disinfection techniques compared with conventional disinfection results in significantly reduced MDRO cross-infection and this question remains unanswered. We are still low on the 'evidentiary hierarchy' outlined by McDonald and Arduino [8]. Most studies aiming for the higher level of the 'evidentiary hierarchy' pyramid have significant methodological limitations and more research in this area is needed [8][9][10][11].

Conclusions

The study by Blazejewski and colleagues provides further evidence that non-manual disinfection techniques based on H 2 O 2 can reduce environmental contamination with MDROs compared with standard cleaning alone. However, solid evidence that these techniques ultimately reduce MDRO cross-infection is lacking. Given the considerable cost associated with these techniques and the fact that the majority of transmission events in nonepidemic settings are probably not mediated by the environment, it seems advisable for now to continue to invest most effort into traditional infection control measures such as hand hygiene and wait for further evidence before introducing these techniques into routine care. Competing interests SH has received investigator-initiated research grants funded by Pfizer (New York, NY, USA) and B. Braun (Melsungen, Germany) and is a member of the advisory boards of Destiny Pharma (Brighton, UK), bioMérieux (Marcy l'Etoile, France), Novartis (Basel, Switzerland), and DaVolterra (Paris, France). BDH declares that he has no competing interests.

Authors' contributions BDH wrote the first draft of the commentary, and SH revised the manuscript. Both authors read and approved the final manuscript.

Abbreviations ESBL-E: Extended spectrum β-lactamase Enterobacteriaceae; H 2 O 2 : Hydrogen peroxide; MDRO: Multidrug-resistant organism; MRSA: Methicillin-resistant Staphylococcus aureus.

* Correspondence: firstname.lastname@example.com Infection Control Program and Division of Infectious Diseases, Geneva University Hospitals and Faculty of Medicine, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland ultraviolet light, or antimicrobial surfaces have therefore gained interest.
Acknowledgments BDH is partially supported by the European Commission (FP7-HEALTH-2011-R-GNOSIS contract 282512).
Efficiency of hydrogen peroxide in improving disinfection of ICU rooms. C Blazejewski, F Wallet, A Rouze, Le Guern, R Ponthieux, S Salleron, J , Crit Care. 1930Blazejewski C, Wallet F, Rouze A, Le Guern R, Ponthieux S, Salleron J, et al. Efficiency of hydrogen peroxide in improving disinfection of ICU rooms. Crit Care. 2015;19:30.

The sink as a correctable source of extended-spectrum beta-lactamase contamination for patients in the intensive care unit. I Wolf, P W Bergervoet, F W Sebens, H L Van Den Oever, P H Savelkoul, W C Van Der Zwet, J Hosp Infect. 87Wolf I, Bergervoet PW, Sebens FW, van den Oever HL, Savelkoul PH, van der Zwet WC. The sink as a correctable source of extended-spectrum beta-lactamase contamination for patients in the intensive care unit. J Hosp Infect. 2014;87:126-30.

Predictors of hospital surface contamination with Extended-spectrum beta-lactamase-producing Escherichia coli and Klebsiella pneumoniae: patient and organism factors. J T Freeman, J Nimmo, E Gregory, A Tiong, De Almeida, M Mcauliffe, G N , Antimicrob Resist Infect Control. 35Freeman JT, Nimmo J, Gregory E, Tiong A, De Almeida M, McAuliffe GN, et al. Predictors of hospital surface contamination with Extended-spectrum beta-lactamase-producing Escherichia coli and Klebsiella pneumoniae: patient and organism factors. Antimicrob Resist Infect Control. 2014;3:5.

Is patient isolation the single most important measure to prevent the spread of multidrug-resistant pathogens?. C Landelle, L Pagani, S Harbarth, Virulence. 4Landelle C, Pagani L, Harbarth S. Is patient isolation the single most important measure to prevent the spread of multidrug-resistant pathogens? Virulence. 2013;4:163-71.

The role of the healthcare environment in the spread of multidrug-resistant organisms: update on current best practices for containment. R F Chemaly, S Simmons, C DaleJr, S S Ghantoji, M Rodriguez, J Gubb, Ther Adv Infect Dis. 2Chemaly RF, Simmons S, Dale C Jr, Ghantoji SS, Rodriguez M, Gubb J, et al. The role of the healthcare environment in the spread of multidrug-resistant organisms: update on current best practices for containment. Ther Adv Infect Dis. 2014;2:79-90.

The role of the surface environment in healthcare-associated infections. D J Weber, D Anderson, W A Rutala, Curr Opin Infect Dis. 26Weber DJ, Anderson D, Rutala WA. The role of the surface environment in healthcare-associated infections. Curr Opin Infect Dis. 2013;26:338-44.

Comparison of the microbiological efficacy of hydrogen peroxide vapor and ultraviolet light processes for room decontamination. N L Havill, B A Moore, J M Boyce, Infect Control Hosp Epidemiol. 33Havill NL, Moore BA, Boyce JM. Comparison of the microbiological efficacy of hydrogen peroxide vapor and ultraviolet light processes for room decontamination. Infect Control Hosp Epidemiol. 2012;33:507-12.

Editorial commentary: climbing the evidentiary hierarchy for environmental infection control. L C Mcdonald, M Arduino, Clin Infect Dis. 56McDonald LC, Arduino M. Editorial commentary: climbing the evidentiary hierarchy for environmental infection control. Clin Infect Dis. 2013;56:36-9.

An evaluation of environmental decontamination with hydrogen peroxide vapor for reducing the risk of patient acquisition of multidrug-resistant organisms. C L Passaretti, J A Otter, N G Reich, J Myers, J Shepard, T Ross, Clin Infect Dis. 56Passaretti CL, Otter JA, Reich NG, Myers J, Shepard J, Ross T, et al. An evaluation of environmental decontamination with hydrogen peroxide vapor for reducing the risk of patient acquisition of multidrug-resistant organisms. Clin Infect Dis. 2013;56:27-35.

The environment and healthcare-acquired infections: why accurate reporting and evaluation of biological plausibility are important. S Harbarth, M Maiwald, S J Dancer, Infect Control Hosp Epidemiol. 34Harbarth S, Maiwald M, Dancer SJ. The environment and healthcare-acquired infections: why accurate reporting and evaluation of biological plausibility are important. Infect Control Hosp Epidemiol. 2013;34:996-7.

Hospital-based environmental hygiene: priorities for research. S Harbarth, Healthcare Infection. 18Harbarth S. Hospital-based environmental hygiene: priorities for research. Healthcare Infection. 2013;18:49-50.


Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection

Zixing Wang
F Anthony
San Lucas
Peng Qiu
Yin Liu
Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection
R E S E A R C H A R T I C L E Open Access
Background: Many variable selection techniques have been proposed for the clustering of gene expression data. While these methods tend to filter out irrelevant genes and identify informative genes that contribute to a clustering solution, they are based on criteria that do not consider the potential interactive influence among individual genes. Motivated by ensemble clustering, there is a strong interest in leveraging the structure of gene networks for gene selection, so that the relationship information between genes can be effectively utilized, while the selected genes are expected to preserve all the possible clustering structures in the data. Results: We present a new filter method that uses the gene connectivity in the gene co-expression network as the evaluation criteria for variable selection. The gene connectivity measures the importance of the genes in term of their expression similarity with others in the co-expression network. The hard threshold and soft threshold transformations are employed to construct the gene co-expression networks. Both simulation studies and real data analysis have shown that the network based on soft thresholding is more effective in selecting relevant variables and provides better clustering results compared to the hard thresholding transformation and two other canonical filter methods for variable selection. Furthermore, a new module analysis approach is proposed to reveal the higher order organization of the gene space, where the genes of a module share significant topological similarity and are associated with a consensus partition of the sample space. We demonstrate that the identified modules can lead to biologically meaningful sample partitions that might be missed by other methods.Conclusions: By leveraging the structure of gene co-expression network, first we propose a variable selection method that selects individual genes with top connectivity. Both simulation studies and real data application have demonstrated that our method has better performance in terms of the reliability of the selected genes and sample clustering results. In addition, we propose a module recovery method that can help discover novel sample partitions that might be hidden when performing clustering analyses using all available genes. The source code of our program is available at http://nba.uth.tmc.edu/homepage/liu/netVar/.

Background

Variable selection in high-dimensional clustering analysis has drawn attention recently in a variety of fields, including statistics, machine learning, pattern recognition and bioinformatics. Generally, variable selection algorithms can be categorized as either wrappers or filters.

In the context of clustering, the wrapper approach searches for variables best suited to a specific clustering algorithm aiming to improve the clustering performance [1,2]. The wrapper approach has been shown to be effective on low dimensional data. However, one problem for these methods, when applied to large data sets, is the increase in computational complexity as the search space exponentially increases over the number of variables. Furthermore, the wrapper method lacks robustness and is biased towards the clustering algorithm used [3]. In contrast, the filter approach is more efficient in dealing with these drawbacks. Filter-based algorithms do not involve clustering algorithms for the evaluation of variable subsets. Rather, the variables are evaluated according to certain criteria (e.g., feature variance [4], entropy-based distance [5], similarity among feature [6], Laplacian score [7]). The filter approach is considered faster and more efficient than the wrapper method in high-dimensional data analysis.

DNA microarray datasets are examples of highdimensional data characterized by low sample sizes and high dimensionality of variables. Clustering microarray data can be very useful for biological and medical studies. For example, based on the gene expression profiles, interesting cluster distinctions can be found among groups of samples, which may correspond to particular phenotypes, such as different types of cancer [8]. In addition to the sample clustering, selecting the informative genes that best define the clusters of samples is also important. Therefore, many variable selection approaches have been proposed for the clustering analysis of microarray data, including the nonparametric densitybased methods [9,10] and the parametric mixture modelbased approaches [11,12]. In this context, Pan and Shen [13] employed an extra L-1 penalty term of mean vectors in the likelihood function to simultaneously perform variable selection and maximize the penalized likelihood [13]. Recently, a new sparse clustering method achieved variable selection by optimizing a weighted within cluster sum of squares (WCSS), subject to constraints on the weights, in the framework of K-Means clustering [14]. However, the results from these methods are limited and might not be able to capture the sheer complexity of gene regulation processes. While all these methods tend to identify the informative genes that contribute most to a single sample clustering solution, this clustering solution may not capture the meaningful sample partition corresponding to some phenotypes of interest. As gene expression can be influenced by many factors, such as cell type, cell differentiation, microenvironment, and external perturbation, the microarray dataset is the result of all these factors mixed together. The same set of samples can undergo different partitions according to different subsets of variables. Therefore, a good variable selection algorithm should select informative features that best preserve all the possible clustering structures in the data.

In this study, we propose a novel network-based method to achieve variable selection for microarray clustering analysis. Network analysis plays an increasingly important role in the exploration of information communication and has been used to study the information on the relationship between genes or proteins [15,16]. Here, we construct a gene co-expression network, in which nodes and edges represent genes and their expression similarity, respectively. Our proposed method is based on the assumption that each gene may induce a specific partition of the sample space in the absence of a priori information about the variable space. Therefore, given the thousands of genes in the microarray dataset, there might be thousands of distinct sample clustering solutions on the same set of samples. In this context, the imminent task is to combine these multiple partitions into a single consensus clustering, which should share as much information as possible with the given pool of sample partitions. This notion of integrating multiple clustering solutions is in line with the framework of cluster ensembles [17], which tend to reuse the existing knowledge and minimize the information loss incurred in the process of cluster assembling. Based on the premise that higher correlated gene expression profiles tend to produce more similar partition structures, we propose to assemble genes according to their expression similarity rather than their sample partitions. The objective of our work is two-fold based on the level of gene organization: first, to select a list of individual genes that shares the most amount of similarity with other genes, so that the final sample partition based on this gene list is a high-quality combination with the most consensus information among the partitions inferred by each individual gene. Intuitively, a good informative gene will have a large number of directly connected genes in the co-expression network, such that it has a strong ability of representing others. Therefore, we propose to assess genes on their connectivity in the co-expression network and to select the genes with top connectivity. The second objective of our study is to identify co-regulated subsets of genes, known as modules, which may represent different biological processes or pathways. The genes in each module are expected to be highly correlated and exhibit a coherent expression profile across samples, while others exist as background noise. Here the gene connectivity is used to further evaluate the gene topological similarity. The genes with high topological similarity with each other are identified as a gene module and should lead to a biologically meaningful sample partition. With simulation and real data analysis, we show that the gene connectivity, which measures the importance of the genes in term of their expression similarity with others in the co-expression network, is an appropriate criterion to select informative genes for sample clustering.

Methods

Gene co-expression network analysis

To define a measure of similarity s ij between the expression profiles of genes i and j, we use the absolute value of the Pearson correlation s ij = abs(cor(x i ,x j )), where x i and x j represent the gene expression profiles for genes i and j, respectively. Therefore the similarity matrix can be denoted by S = [s ij ], where the values of s ij lie between 0 and 1. However, since microarray gene expression data are typically quite noisy, directly employing the similarity matrix for gene co-expression network analysis may not be appropriate. We find it useful to employ the following transformations that map a similarity matrix into an adjacency matrix. The first transformation is the signum function which implements hard thresholding. Specifically,
a ij ¼ signum s ij; τ À Á ¼ 1 if s ij ≥ τ 0 if s ij < τ &ð1Þ
Here a ij is an element of the adjacency matrix and s ij is an element of the similarity matrix. In hard thresholding, the value of the parameter τ determines the number of genes and edges included in the resulting unweighted network. Typically, an arbitrary value of τ is chosen to exclude spurious edges, but this may lead to a loss of information. To address this issue in hard thresholding, a "soft" power transformation function has been proposed [18]:
a ij ¼ s ij βð2Þ
with a single parameter β, where β > =1. Soft thresholding results in a completely connected network with each edge being assigned a weight.

Gene connectivity and variable selection

With the n × n symmetric adjacency matrix, the connectivity (node degree) k i of gene i is define by
k i ¼ X j≠i a ijð3Þ
For the hard thresholding transformation, the connectivity of gene i simply equals to the number of genes that it is directly connected to in the unweighted network. For the soft thresholding transformation, the connectivity of gene i equals the sum of weights between gene i and all other genes in the weighted network. To select the relevant genes for microarray clustering analysis, we first rank the genes according to their connectivity. For the hard thresholding transformation, the genes with connectivity of 0 are removed from the original network. Therefore, the gene connectivity ranking is only applicable on the set of genes included in the resulting unweighted network, which is of a reduced size depending on the value of the threshold τ. In the weighted network, the ranking can be obtained for all the genes. Finally, the genes with low ranks are filtered out, while the genes with top ranks are considered to have high degree of connectivity and are selected for clustering analysis.

Module identification

Our module identification method is based on the node similarity measure of their relative interconnectedness coupled with the hierarchical clustering method. Instead of using the gene correlation coefficients directly as the similarity measure, we calculate the Jaccard similarity coefficient J ij based on the gene connectivity in the transformed network.
J ij ¼ h ij k i þ k j −h ijð4Þ
Where h ij = ∑ u a iu a uj , which equals the number of genes to which both i and j are connected in the case of hard thresholding, and the total interconnectedness of genes i and j in the soft thresholding transformation. And k i = ∑ u a iu is the node connectivity as defined in equation (3). Therefore, the similarity measure will be affected by the selection of the transformation parameters. In our implementation, we adjust the hard thresholding parameter τ or the power function parameter β to explore their effects on the results of module identification. Once the topological similarity measure matrix is obtained, we re-order it by hierarchical clustering of each row and column to put similar genes in an adjacency zone [19]. Since the similarity measure matrix is symmetric, these highly similar genes would form "hot" blocks along the diagonal and can be identified as a module by visual inspection. The genes in the resulting modules are expected to be highly co-expressed.

Evaluate the performance of variable selection

The performance of our method for variable selection is evaluated by the F-score, where F = 2*Precision*Recall/ (Precision + Recall). The precision is the proportion of selected variables that are truly relevant, and the recall is the proportion of truly relevant variables that are selected by our method, also known as the true positive rate. The F-score ranges between 0 and 1, and can be interpreted as a weighted average of the precision and recall.

Based on the selected genes, we cluster samples using the K-means algorithm with 50 iterations. The sample clustering performance is evaluated by the classification error rate (CER). The derived sample clustering (p 1 ) is compared to the true clustering (p 2 ) to assess the performance. The CER is defined as
CER p 1 ; p 2 ð Þ ¼ X i>i 0 1 p 1 i ð Þ¼¼p 1 i 0 ð Þ −1 p 2 i ð Þ¼¼p 2 i 0 ð Þ n 2ð5Þ
where n is the sample size. Note that smaller CER values reflect more accurate clustering results. A CER of zero indicates that the clustering results p 1 and p 2 agree perfectly.

Simulation data setting

We used a simulation setting similar to that in Witten and Tibshirani [14]. A simulated dataset contains 60 samples from three classes C 1 , C 2 and C 3 (20 samples from each) and each sample X i is a d -dimensional vector that follows N(μ i , ∑ d ) and is independent of other samples. Thus, the clustering structure is determined by the specification of μ i 's that are defined as
μ ij ¼ μ 1 i∈C1 -1 i∈C j1 À Á if j ≤ 10 μ 1 i∈C j1 -1 i∈C1 À Á if 10 < j ≤ 20 μ 1 i∈C2 -1 i∈C j2 À Á if 20 < j ≤ 30 μ 1 i∈C j2 -1 i∈C2 À Á 0 if 30 < j ≤ 40 otherwise 8 > > > > < > > > > :ð6Þ
Where μ is a positive constant and set to 1 in the experiment. This configuration of μ sets the first 40 genes as informative genes and the other genes as noise. We take ∑ d = diag (σ 1 , …, σ d ) where fc σ 1 , …, σ d are set such that the population variance of each variable is one. In the simulation, the first 20 genes together can be considered as a module since their expression profiles are highly correlated and this module differentiates samples in class C 1 from the others. Whereas the next 20 genes form another module that differentiates samples in class C 2 from others. Therefore, these two sets of genes exhibit different sample partitions.

Results and discussion

Variable selection performance with simulation dataset

We describe herein the performance of our method with two network inference methods on a simulated dataset. In the hard thresholding transformation, we considered the effects of two parameters on the performance of variable selection: the hard threshold τ that determines the number of genes and edges included in the unweighted coexpression network, and the percentage of genes to be selected based on their connectivity in the resulted network, determined by equation (3). With a greater value of τ, the resulting network will have a smaller number of genes and edges, but the connection strength (correlation coefficient) between paired genes will be higher. We reported the average F-scores and CER values based on 100 simulated datasets for two different dimensionalities (d=500, Figure 1a and b, and d=1000, Additional file 1: Figure S1a and b).

It is not surprising to observe that selecting all of the 500 genes in the dataset can only lead to a low F-score (0.15) and a high CER (0.29), as shown in Figure 1a and b, simply because too many noninformative genes were included without the variable selection step. When we varied the threshold τ to generate a network with reduced size but kept all the genes in the resulting network regardless of their connectivity (in the case of genes with top 100% connectivity being selected), the performance evaluated by the F-score was improved but still poor, regardless of how many genes were in the resulting network. However, both the F-scores and CER were shown to improve further with an additional step of gene filtering by the gene connectivity rank. Generally, the more stringent the gene connectivity rank filtering, the lower the number of genes selected. To compare the performance of different gene connectivity ranks with the same number of genes selected, we had to decrease the threshold τ to achieve a large network size when the gene connectivity rank was more stringent. As shown in the Figure 1a and b, the gene filtering with the top 20 percentile connectivity resulted in the highest F value and the lowest CER when 100 genes were selected. However, this was not always the case. When 40 genes were included, the top 40% percentile rank achieved best performance among all these filtering scales. This indicates that the performance of variable selection is affected by both the connection strength and the connectivity of the selected genes. We observed similar results in both simulated datasets with different dimensionalities (d= 1000, Additional file 1: Figure S1a and b).

As shown in above analysis, the network variable selection based on hard-thresholding transformation was influenced by both the network size and the gene connectivity filtering. It may be challenging to optimize both of these two factors in real data analysis. To resolve this problem, we used a soft thresholding transformation for gene selection that is only dependent on the power function parameter β. It not only takes into account the information of all the genes, but also reduces the effect of noise induced correlation by the power function, assuming that the noise correlation occurs more likely at smaller values than the correlation associated with true gene relationships.

In the soft thresholding transformation, we varied the value of β to construct a series of gene co-expression networks. Results in Figure 1c and d demonstrated that the power transformation significantly improved the performance of variable selection and led to a higher F-score peak and lower CER than the original non-transformed one (β=1). We further found the performance was not a monotonic function of β. Among the four power functions with different parameters β, the optimal value of F-score and CER were achieved when β was set to 3, which may result in the optimized state for emphasizing the correlation associated with true gene relationships by diminishing the noisy effects in this simulation setting. We observed similar results when d=1000 (Additional file 1: Figure S1c and d).

In comparison with other feature selection methods

To further demonstrate the effectiveness of our proposed network based analysis for variable selection, we compared it with two other classic filter algorithms, the Laplacian Score [7] and the Max-Variance. As a special case of a spectral feature selection algorithm, Laplacian score selects those features that can best preserve the local manifold structure (He et al. [7]). In the comparison, we set up the parameter β with the optimized value obtained in the aforementioned simulation result (β =3). The genes selected from each method were used for clustering samples with the K-means algorithm. Our proposed network algorithm consistently outperformed the other two methods, particularly at a low dimensionality (20-100), where all of these methods reached their best performance (Figure 2b). The Laplacian score method achieved an optimal CER value of 0.25, close to that of the network without a power transformation. Similar results were observed in the comparison of the F-scores (Figure 2a). This indicates the effectiveness of our variable selection method in clustering analysis.

Selecting genes that support a common clustering structure by module identification

The gene co-expression network analysis captures the relationships among the genes so that it can help identify a small number of sets of highly correlated genes, each of which tends to assemble into a functional module that can be involved in biological pathways or molecular complexes. Also, these genes together assure a specific clustering of samples, which might be different from other sets of correlated genes. The genes selected by a module usually have greater intramodular connectivity than those that do not belong to the module. Therefore, module analysis not only captures the connectivity information of individual nodes as what we did in the variable selection, but also reveals the higher order organization of gene topological similarity in the entire gene space.

We applied both the hard-thresholding and softthresholding transformation on the gene co-expression network for module identification. Note that the sensitivity of this method varies depending on the coexpression network size and the composition of variable space. In the analysis, we assigned the value of μ to 1.5 in the simulation setting to demonstrate a clear module structure. For hard thresholding transformation of network (d=500), we varied the threshold τ to retain the top 1% of correlation coefficients for calculating the Jaccard similarity coefficients between genes. This cutoff is roughly consistent with our simulation setting where there are 40 informative genes and 40*40 informative gene pairs among 500*500 total number of gene pairs (0.64%). We also tested the performance on the top 5% of correlations. Figure 3b and c showed the discovered modules in the network when the top 1% and top 5% of correlations were kept in the transformed network, respectively. Two 'hot blocks' can be clearly identified along the diagonal, each of which corresponding to the original defined module in the simulation setting with only a few missing genes. Due to varying numbers of edges included, the boundaries between blocks exhibited distinctive sharpness in Figure 3b and c, but the module structure and the genes included in each module were the same. Therefore, this simulation result can serve as a guideline for determining cutoffs. For a hard threshold of 1%, we are assuming that roughly 10% of the genes are informative and 90% are not. This assumption is of course not optimal for every dataset. Fortunately, this simulated example suggests that module identification is not very sensitive to this parameter. Therefore, in a real dataset, if we use a hard threshold, we will first set the threshold to select the top 1% of edges, and also vary the threshold slightly, while checking whether the hot block appears to be consistent with respect to small changes of the cutoff.

We also performed soft thresholding transformation and obtained similar results (Figure 3d and e), indicating the relative robustness of our method in module identification. Furthermore, each of the blocks induced a unique bipartitioning of the sample space that is equivalent to the sample partition inferred by the corresponding modules in our simulation setting (C1 vs. others and C2 vs. others). We observed similar results when d=1000 (Additional file 1: Figure S2).

Application in real datasets

Along with simulations, we applied our method to two real experimental datasets: Leukemia [8] and Colon cancer data [20].

The leukemia dataset consists of 72 patients with two subtypes of acute leukemia: acute myeloid leukemia  (AML) and acute lymphoblastic leukemia (ALL). The latter is composed of two subclasses, B-cell and T-cell types. Therefore there could be two possible biologically meaningful clustering solutions including one with twoclusters of samples (AML and ALL) and the other with three clusters (T cell ALL, B cell ALL and AML).

Following Dudoit and Fridlyand [21], three preprocessing steps were applied to the original data matrix and a final 72 X 3571 data matrix was obtained. Because the pre-processing steps included thresholding the gene expression values with a floor and a ceiling boundaries, many artificially high correlations were introduced. We filtered out these genes whose medians equal the boundary values and obtained 3033 genes totally. We first studied the module organization of the gene space and the associated sample partition in the leukaemia dataset. In the implementation, the value of τ was chosen to include only the edges corresponding to the top 1% of paired correlation coefficients in the network. As shown in Figure 4a, the topological similarity matrix exhibited a sharp separation of modules from its neighboring genes. We evaluated the sample clustering performance of modules by using the gene set included in each module for sample partitioning. We found that most of them induced a meaningful partition of the sample space. Specifically, the first module at the bottom right corner rendered a dichotomy of the samples according to the known classification, ALL/AML, with the CER value equalling 0.155, whereas the second module tends to distinguish B cell ALL patients from the rest with a CER value of 0.2, indicating the unrecognized similarity between T cell ALL samples and AML samples in the dataset. The other modules also imposed a potential novel partition of samples. These results confirmed multiple possible clustering solutions in the leukemia dataset. We also performed variable selection to select individual genes based on their connectivity in the transformed network using soft thresholding transformation. For the three-cluster solution, the 100 genes selected from the network-based analysis yielded the sample partition coinciding almost precisely with the known classification (T cell ALL, B cell ALL and AML) with CER equalling to 0.09, when the parameter β reached 40 or above (Figure 4c). In Figure 4c, we also compared the performance of our network-based method with other filter methods. Results showed that our approach achieved a comparable optimal CER value with the Laplacian and the Max-Variance methods, when more than 100 genes were selected. Out of the top 100 informative genes selected from our method, 28 and 35 genes were found by the Laplacian and the Max-Variance method, respectively (Additional file 1: Figure S4a). The genes selected from our method may also represent new sample partitions. This is supported by the observation that our method achieved better separation of B cell AML samples from the rest compared to other methods (Additional file 1: Figure S5a). We further examined the gene lists selected from soft thresholding transformation with varied β, and found that the overlap among the gene lists increased as β became larger. After β reached 80, the selected gene lists were almost unchanged. This is consistent with their similar clustering performance as shown in Figure 4c. However, recall that in our simulation studies the optimal CER was achieved when β was small (β =3), reflecting possibly different variable compositions between the simulated dataset and the leukemia data. As shown in Figure 5a and b, these implications became clear. In Figure 5a, the correlation of 40 informative genes in the simulated dataset followed a uniform distribution. Unlike the 40 informative genes in simulation, the correlation of the 100 selected informative genes from the leukemia data followed a mixture distribution with two components, one at the high end of the distribution and another close to zero (Figure 5b). This is reasonable for real datasets given the assumption that gene expression data are influenced by many active biological processes, where genes within each of the processes tend to be highly correlated with one another, but may not be well correlated with those participating in other biological processes. Therefore, the correlation values between genes corresponding to different biological processes will be small, located in the low end of the correlation distribution. Since the component in the high end was well separable from the other for the full gene space, the corresponding highly connected genes tend to be always selected as informative genes disregarding the value of β. Therefore, the selected genes should not be sensitive to β when β is above a certain threshold that aims to remove the noisy correlations. However, this was not the case for the simulated dataset, where the correlation of the informative genes followed a uniform distribution while that of the full set of genes was close to zero, so a small value of β should be able to remove the noise induced correlations.

We also analyzed the colon cancer data by Alon et al. [20], which contains two classes of samples based on disease status: 40 tumor samples and 22 normal samples. In addition, an independent study reported that there was a difference in the experimental protocols used to process the samples [22]. There are 22 samples processed by protocol 1, and the other 40 samples were processed by protocol 2. Taking the different protocols into consideration, the study has at least three different possible sample partition structures based on disease status, sample protocols, and their combination. In the analysis, we first employed the module analysis on the dataset, followed by variable selection for the clustering analysis. As we did with the leukemia dataset, the edges corresponding to the top 1% correlation were kept for module analysis. As shown in Figure 4b, dozens of modules were identifiable along the diagonal of the similarity matrix. Each module exhibited a distinctive partition of the sample space. Among the first three modules at the bottom right, module 1 had strong tendency to partition the samples according to the normal versus tumor classification with a CER value (0.35), whereas module 3 was informative for the partition based on different protocols (CER=0.27). Also the number of genes included in these two modules differed. Module 1 was the largest in terms of the number of genes included. These results indicate that the classification of tumor versus normal samples is a more dominant factor in the sample clustering compared to the different sample protocols. It was interesting to observe that the number of genes in module 2 was similar compared to module 1. However their clustering behaviours differed, suggesting that module 2 may inform a novel sample partition of the colon cancer dataset. The aforementioned module analysis reinforced the idea that the colon cancer dataset has at least three clustering solutions. Here, a soft thresholding transformation was implemented to select the feature genes. As shown in Figure 4d, in a four-cluster solution that combines the disease status and the sample protocol changes, the clustering performance of our method based on different values of β is similar given a β value of 40 or above. Here, the CER value was higher than that obtained from the leukemia dataset, possibly due to mislabeled samples [23]. Nevertheless, our approach achieved a better performance than the other methods. In Figure 4d, when β was 40 or above, our method had a CER value equal to 0.25 with 100 genes selected, whereas Laplacian and Max-Variance obtained the CER values of 0.32 and 0.29, respectively. Only 18 and 8 genes selected from our approach were also obtained from the Laplacian and the Max-Variance method, respectively (Additional file 1: Figure S4b). For the other clustering solution based on different experimental protocols, our method outperformed others as well (Additional file 1: Figure S5b). We also plotted the distribution of correlations for full gene space and the 100 selected informative genes respectively (Figure 5c). Similar to the leukemia dataset, the gene set has two wellseparated components of correlations at two ends, which may explain their saturating behaviour of clustering performance when β reaches a certain value.

Conclusions

Variable selection for clustering is never a trivial problem. This is particularly true in high dimensional data analysis, where few dozens of informative variables are often dispersed over a noisy background with thousands of noninformative variables. Traditional approaches to filtering out the irrelevant features are based on certain criteria that do not account for potential interactive influence from other individual variables. Motivated by ensemble clustering, we propose a new filter score, the gene connectivity in the co-expression network, which takes into account all of the information gained from other nodes in the network in terms of expression similarity; therefore, the selected genes are expected sustain a consensus sample partition that populates through the partition pool induced by individual genes.

To obtain the connectivity of each gene, we have applied two network inference methods based on a hard thresholding and a soft thresholding adjacency function. In the first method, we use a hard threshold parameter τ to infer the gene network, followed by filtering the nodes based on their connectivity rank. Therefore, the resulted feature gene set is affected by the resulting network size and the stringency of the connectivity rank. The marginal gene connectivity obtained from hard thresholding transformation is estimated solely based on the given gene and its neighbourhood in the network with reduced size. Therefore, to retain more information of the entire network, we employ the soft thresholding transformation to build a complete network including all genes, where each gene is connected to all the other genes in the network with weighted connection strength. Our simulation results showed that soft thresholding is more effective and provides better clustering results compared to the hard thresholding method in terms of clustering error rate and variable selection. We realize although the connectivity obtained from a soft thresholding network preserves more information of the entire network compared to the hard thresholding transformation, the gene connectivity calculation is still based on the gene and its edges to all the genes in the network, and it does not consider the edges among its neighbors. There are other network related metrics, such as the betweenness centrality of a node that requires knowledge of the entire network topology and indicates how important the node is within the context of the entire network. These network metrics will be evaluated in our future studies. In this work, considering the fact that gene connectivity is easy to calculate and the derived gene hubs represent a large number of other genes in the network well, we use gene connectivity as our evaluation criteria for variable selection.

The parameter setting in variable selection methods renders a crucial influence on the performance of the feature genes selected. However, tuning parameter selection in the unsupervised setting is always a challenging problem [24]. In real data analyses, we showed the relative robustness of our method when the parameter β was above a threshold value in the soft thresholding transformation. Further analysis of these informative genes demonstrates a mixture correlation distribution with two components in the profile. The component in the high end comes from paired gene correlations within the same biological processes, so they will be preferentially selected by the high power transformation parameter β compared to other genes. Therefore, we propose to pick the value of β for a given dataset so that the selected genes are not sensitive to β if it is above this threshold value. Alternatively, we can evaluate each set of feature genes with other criteria, such as the purity and efficiency of clustering results, or within/between class distance of induced sample partitions.

Furthermore, we developed a module identification method by measuring the node interconnectedness in the co-expression network. Our module identification is based on using a node similarity measure in conjunction with a clustering method. In this study, we chose to use the Jaccard similarity coefficient based on the gene connectivity instead of using the gene correlation coefficients directly. Jaccard similarity takes advantage of the co-expression network information assuming that two nodes having a higher degree of overlapping neighbors are more likely to be in the same functional class than nodes having a lower topological overlap. This is particularly useful when high background noise divert the real network information. We performed a comparison of module structures based on two similarity measures: the Pearson correlation coefficient and the Jaccard similarity coefficient. As shown in Additional file 1: Figure S3, there is a clear difference before and after the Jaccard similarity was computed on the gene correlation coefficients. The Jaccard similarity coefficient led to more distinct gene modules than the correlation coefficient which resulted in a highly noisy background on its module structure. Once the node similarity measure is obtained, we applied hierarchical clustering to resort the rows and columns of genes. There are alternative clustering procedures such as K-means clustering, but hierarchical clustering is more straightforward, and does not require specification of the number of modules. The genes corresponding to each module clearly form squares along the diagonal so that the modules can be easily identified by visual inspection.

The focus in our module analysis is on the high order organization of gene space, rather than their specific corresponding sample partitions. It is particularly useful for discovering unanticipated sample partition structures in data. Unlike the previous methods [22], our method does not need the partitioning and merging steps of the gene space; alternatively, we use the co-expression network, which is capable of recovering biologically meaningful modules amongst a noisy background, that putatively represent pathways or cellular processes. Such information can be used to establish causal models connecting the informative feature sets with known phenotypes such as disease symptoms, which will facilitate discovery of new and hidden patterns in datasets.

Additional file

Additional file 1: Figure S1. Variable selection performance in the simulated dataset (d= 1000). The averaged F-scores (a, c) and the CER curves (b, d) in hard thresholding and soft thresholding transformation, respectively. The horizontal line in each plot represents the performance based on all genes (1000 totally). Figure S2. Module structure in the gene co-expression network from the simulated dataset of d=1000. (a) and (b): in hard threshold transformation, with top 1% and 5% correlations were included in the network, respectively. (c) and (d): in soft transformation, power function with β=3 and β=7. Figure S3. Comparison of module structure recovery using different similarity measures. The rows and columns of genes have been reordered according to the hierarchical clustering of similarity matrix. (a). Pearson correlation coefficients of 500 genes, no transformation. (b) Jaccard similarity coefficients of 500 genes, no transformation. (c) Pearson correlation coefficients with power transformation with β=3. (d). Jaccard similarity coefficients derived from power transformation with β=3. Figure S4. Comparison of lists of genes selected from different methods. Network refers to our network-based variable selection method. (a) Leukemia dataset and (b) Colon cancer dataset. Figure S5. Clustering performance based on new partition structures identified by our module analysis. The CER curve with various power functions in co-expression network transformation for new partition structures of the Leukemia dataset (a) and Colon dataset (b).

Figure 1
1Performance of variable selection. The averaged F-scores (a, c) and the CER curves (b, d) in hard thresholding and soft thresholding transformation, respectively. The horizontal line in each plot represents the performance based on all genes (500 totally).

Figure 2
2Comparison with other variable selection methods. (a) The averaged F scores and (b) the CER curves. Our method is based on soft thresholding transformation with the power function parameter β=3.

Figure 3
3Module structure in the gene co-expression network from the simulated dataset of d = 500. (a). Two modules were identified in the co-expression network. The rest on the right are zoomed-in view of the modules highlighting the genes included in two modules respectively.(b) and (c), hard thresholding transformation, with top 1% and 5% correlations included in the network, respectively. (d) and (e), soft thresholding transformation, with the power functions parameter β=3 and β=7, respectively.

Figure 4
4Module analysis and clustering results for the leukemia and colon datasets. Top panel: Zoomed-in view of the module composition in the gene co-expression matrices of the Leukemia (a) and Colon dataset (b). Bottom panel: The CER curves based on soft thresholding transformation with various power functions for three clusters of Leukemia dataset (c) and four clusters of Colon cancer dataset (d).

Figure 5
5The correlation distribution of full variable space and informative gene set. (a) The simulated dataset with d=500, (b) the Leukemia and (c) the colon cancer dataset.
http://www.biomedcentral.com/1471-2105/15/153
AcknowledgementsThis work is supported in part by NIH grant R01 LM010022, R01 CA163481 and the seed grant from the University of Texas Health Science Center at Houston.Author detailsCompeting interestsThe authors declare that they have no competing interest.Authors' contributions ZW carried out the simulation studies and real data application and drafted the manuscript. FAS participated in the data analysis and results discussion. QP participated in the design of the study and helped to draft the manuscript. YL conceived and coordinated the study and helped to draft the manuscript. All authors read and approved the final manuscript.
Feature selection in mixture-based clustering. M H Law, M Figueiredo, A K Jain, Advances in Neural Information Processing Systems. 15Law MH, Figueiredo M, Jain AK: Feature selection in mixture-based clustering. In Advances in Neural Information Processing Systems 15: December 2002. 2002.

Feature selection for unsupervised learning. F G Dy, C E Brodley, J Mach Learn Res. 545Dy FG, Brodley CE: Feature selection for unsupervised learning. J Mach Learn Res 2004, 5:45.

Feature slection for clustering: review. S Alelyani, J Tang, H Liu, Data Clustering: Algorithms and Applications. Charu A, Chandan RCRC PressAlelyani S, Tang J, Liu H: Feature slection for clustering: review. In Data Clustering: Algorithms and Applications. Edited by Charu A, Chandan R. 2013. CRC Press.

SDED: a novel filter method for cancer-related gene selection. W Xu, M Wang, X Zhang, L Wang, H Feng, Bioinformation. 27Xu W, Wang M, Zhang X, Wang L, Feng H: SDED: a novel filter method for cancer-related gene selection. Bioinformation 2008, 2(7):301-303.

Feature Selection for Clustering -A Filter Solution. M Dash, K Choi, P Scheuermann, H Liu, Proceedings of the Second International Conference on Data Mining. the Second International Conference on Data MiningDash M, Choi K, Scheuermann P, Liu H: Feature Selection for Clustering -A Filter Solution. In Proceedings of the Second International Conference on Data Mining. 2002:115-122.

Unsupervised feature selection using feature similarity. P Mitra, C A Murthy, S Pal, EEE Transactions on Pattern Analysis and Machine Intelligence. 12Mitra P, Murthy CA, Pal S: Unsupervised feature selection using feature similarity. In EEE Transactions on Pattern Analysis and Machine Intelligence. 2002:12.

Laplacian score for feature selection. X He, D Cai, P Niyogi, Advances in Neural Information Processing Systems. 8He X, Cai D, Niyogi P: Laplacian score for feature selection. In Advances in Neural Information Processing Systems. 2006:8.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. T R Golub, D K Slonim, P Tamayo, C Huard, M Gaasenbeek, J P Mesirov, H Coller, M L Loh, J R Downing, M A Caligiuri, C D Bloomfield, E S Lander, Science. 2865439Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531-537.

Automatic subspace clustering of high dimensional data for data mining applications. R Agrawal, J Gehrke, D Gunopulos, P Raghavan, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, SIGMOD'98. the 1998 ACM SIGMOD international conference on Management of data, SIGMOD'98New York, NY: USAACM121998th editionAgrawal R, Gehrke J, Gunopulos D, Raghavan P: Automatic subspace clustering of high dimensional data for data mining applications. In IN Proceedings of the 1998 ACM SIGMOD international conference on Management of data, SIGMOD'98. 1998th edition. New York, NY: USA: ACM; 1998:12.

Using evidence of mixed populaitons to select variables for clustering very high-dimensional data. Y Chan, P Hall, J Am Stat Assoc. 10549012Chan Y, Hall P: Using evidence of mixed populaitons to select variables for clustering very high-dimensional data. J Am Stat Assoc 2010, 105(490):12.

CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. E P Xing, R M Karp, Bioinformatics. 171Xing EP, Karp RM: CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics 2001, 17(1):S306-S315.

A mixture model-based approach to the clustering of microarray expression data. G J Mclachlan, R W Bean, D Peel, Bioinformatics. 183McLachlan GJ, Bean RW, Peel D: A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 2002, 18(3):413-422.

Penalized model-based clustering with application to variable selection. W Pan, X Shen, J Mach Learn Res. 820Pan W, Shen X: Penalized model-based clustering with application to variable selection. J Mach Learn Res 2007, 8:20.

A framework for feature selection in clustering. D Witten, R Tibshirani, J Am Stat Assoc. 10549014Witten D, Tibshirani R: A framework for feature selection in clustering. J Am Stat Assoc 2010, 105(490):14.

When is hub gene selection better than standard meta-analysis?. P Langfelder, P S Mischel, S Horvath, PLoS One. 8461505Langfelder P, Mischel PS, Horvath S: When is hub gene selection better than standard meta-analysis? PLoS One 2013, 8(4):e61505.

Incorporating prior knowledge into Gene network study. Z Wang, W Xu, San Lucas, F A Liu, Y , Bioinformatics. 201320Wang Z, Xu W, San Lucas FA, Liu Y: Incorporating prior knowledge into Gene network study. Bioinformatics 2013, 29(20):2633-2640.

Cluster ensembles -a knowledge reuse framework for combining multiple partitions. A Strehl, J Ghosh, J Mach Learn Res. 335Strehl A, Ghosh J: Cluster ensembles -a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 2003, 3:35.

A general framework for weighted gene coexpression network analysis. B Zhang, S Horvath, Stat Appl Genet Mol Biol. 417Zhang B, Horvath S: A general framework for weighted gene co- expression network analysis. Stat Appl Genet Mol Biol 2005, 4: Article17.

Discovering biological progression underlying microarray samples. P Qiu, A J Gentles, S K Plevritis, PLoS Comput Biol. 741001123Qiu P, Gentles AJ, Plevritis SK: Discovering biological progression underlying microarray samples. PLoS Comput Biol 2011, 7(4):e1001123.

Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. U Alon, N Barkai, D A Notterman, K Gish, S Ybarra, D Mack, A J Levine, Proc Natl Acad Sci. 9612Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A 1999, 96(12):6745-6750.

A prediction-based resampling method for estimating the number of clusters in a dataset. S Dudoit, J Fridlyand, Genome Biol. 3736Dudoit S, Fridlyand J: A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol 2002, 3(7): RESEARCH0036.

Coupled two-way clustering analysis of gene microarray data. G Getz, E Levine, E Domany, Proc Natl Acad Sci U S A. 9722Getz G, Levine E, Domany E: Coupled two-way clustering analysis of gene microarray data. Proc Natl Acad Sci U S A 2000, 97(22):12079-12084.

Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. M L Chow, E J Moler, I S Mian, Physiol Genomics. 52Chow ML, Moler EJ, Mian IS: Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol Genomics 2001, 5(2):99-111.

Comparison of threshold selection methods for microarray gene co-expression matrices. B R Borate, E J Chesler, M A Langston, A M Saxton, B H Voy, 10.1186/1471-2105-15-153BMC Res Notes. 2240Borate BR, Chesler EJ, Langston MA, Saxton AM, Voy BH: Comparison of threshold selection methods for microarray gene co-expression matrices. BMC Res Notes 2009, 2:240. doi:10.1186/1471-2105-15-153

Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection. Wang, BMC Bioinformatics. 15153Cite this article asCite this article as: Wang et al.: Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection. BMC Bioinformatics 2014 15:153.


The use of percutaneous coronary intervention in black and white veterans with acute myocardial infarction

Charles Maynard firstname.lastname@example.com
Department of Veterans Affairs Puget Sound Health Care System
Health Services Research and Development
SeattleWashingtonUSA

Haili Sun
Department of Veterans Affairs Puget Sound Health Care System
Health Services Research and Development
SeattleWashingtonUSA

Elliott Lowy
Department of Veterans Affairs Puget Sound Health Care System
Health Services Research and Development
SeattleWashingtonUSA

Anne E Sales
Department of Veterans Affairs Puget Sound Health Care System
Health Services Research and Development
SeattleWashingtonUSA

Stephan D Fihn
Department of Veterans Affairs Puget Sound Health Care System
Health Services Research and Development
SeattleWashingtonUSA

The use of percutaneous coronary intervention in black and white veterans with acute myocardial infarction

BMC Health Services Research
10.1186/1472-6963-6-107BioMed Central Page 1 of 6 (page number not for citation purposes) Research articleHaili Sun -HailiSun@vagovElliott Lowy -ElliottLowy@vagovAnne E Sales -asales@uwashingtoneduStephan D Fihn -StephanFihn@vagov * Corresponding author
Background: It is uncertain whether black white differences in the use of percutaneous coronary intervention (PCI) persist in the era of drug eluting stents. The purpose of this study is to determine if black veterans with acute myocardial infarction (AMI) are less likely to receive PCI than their white counterparts.Methods:This study included 680 black and 3529 white veterans who were admitted to Veterans Health Administration (VHA) medical centers between July 2003 and August 2004. Information for this study was collected as part of the VHA External Peer Review Program for quality monitoring and improvement for a variety of medical conditions and procedures, including AMI. In addition, Department of Veterans Affairs workload files were used to determine PCI utilization after hospital discharge. Standard statistical methods including the Chi-square, 2 sample t-test, and logistic regression with a cluster correction for medical center were used to assess the association between race and the use of PCI ≤ 30 days from admission.Results: Black patients were younger, more often had diabetes mellitus, renal disease, or dementia and less often had lipid disorders, previous coronary artery bypass surgery, or chronic obstructive pulmonary disease than their white counterparts. Equal proportions of blacks and whites underwent cardiac catheterization ≤ 30 days after admission, but the former were less likely to undergo PCI (32% vs. 40%, p < 0.0001). This difference persisted after multivariate adjustment, although measures of the extent of coronary artery disease were not available.Conclusion:Given the equivalent use of cardiac catheterization, it is possible that less extensive or minimal coronary artery disease in black patients could account for the observed difference.

Background

In the past, black veterans hospitalized with acute myocardial infarction (AMI) were less likely to undergo cardiac revascularization procedures, including percutaneous coronary intervention (PCI), than their white counterparts [1][2][3][4][5][6][7]. These differences in the use of cardiac revasculariza-tion procedures were observed over 10 years ago in a health care system where there was and still is equal access to care. It is uncertain whether these differences in the use of PCI persist in the era of drug eluting stents. It is possible that black patients are less likely to be the beneficiaries of new technology due to receiving care at hospitals with lower procedure rates and greater disparity in racial composition [8]. On the other hand, the indications for PCI in the setting of AMI have expanded significantly due to new technology, and it is possible that this trend has affected the proportions of black patients undergoing PCI. The purpose of this study is to determine if in the era of drug eluting stents, black veterans with AMI are less likely to receive PCI than their white counterparts.

Methods

Patient population

Between July 20, 2003 and August 19, 2004, there were 6262 veterans who were admitted to Veterans Health Administration (VHA) medical centers and developed AMI that was documented by standard electrocardiographic, troponin, and/or other clinical evidence. Not included in this count were patients who were transferred into VHA hospitals from other medical facilities, because important baseline information was not available. Also excluded were patients who had AMI while hospitalized for another medical condition.

Information for this study was collected as part of the VHA External Peer Review Program for quality monitoring and improvement for a variety of medical conditions and procedures, including AMI. Patients with International Classification of Diseases 9 th Revision diagnosis codes 410.xx were identified from administrative data housed at the Austin Automation Center. Working with the peer review abstraction contractor, West Virginia Medical Institute, the VHA Office of Quality and Performance generated a list of patients that was transmitted to VHA facilities where both paper and electronic medical records were manually abstracted by trained abstractors using standard reporting forms. Abstracted data were then entered into a database maintained by the contractor.

Study variables

We used information from the peer review database and Department of Veterans Affairs workload files to describe demographics and personal characteristics, vital status and cardiac procedure use after hospital discharge [9]. Race/ethnicity was obtained from the medical inpatient datasets and outpatient care files at Austin and was classified as 1) Hispanic, 2) Black, 3) White, 4) Other, or 5) Unknown.

Medical centers were classified according to type of invasive cardiac procedures offered: 1) no cardiac catheterization, 2) cardiac catheterization only, 3) PCI, or 4) full range of procedures including coronary artery bypass graft surgery. Clinical information such as cardiac medications, time from symptom onset to hospital admission, initial heart rate, systolic and diastolic blood pressures, and initial symptoms were obtained from the medical record.

The initial electrocardiographic diagnosis of ST elevation myocardial infarction included those with ST elevation ≥ 1 millivolt in 2 or more contiguous leads and/or left bundle branch block. The remainder of patients had non-ST elevation AMI. Both the initial and highest troponin values were recorded; positive tests were determined according to criteria for the particular type of assay used. In both groups, over 99% of AMIs were confirmed by troponin elevations.

Cardiac treatments and procedures were recorded as part of chart abstraction, but workload data from the Austin Automation Center were also accessed to determine cardiac procedure use after discharge. For patients who presented to the hospital with ST segment elevation, PCI within 12 hours of admission was defined. In addition, 30-day rates of cardiac catheterization, PCI, and coronary artery bypass surgery were constructed.

Statistical methods

Comparisons between black and white patients were made with the Chi-square test for categorical variables and the 2 sample t-test for continuous measures. Logistic regression was used to further examine the association between race and the use of PCI performed ≤ 30 days after admission. We used backward stepwise logistic regression to select predictors of PCI use. After statistically significant predictors were selected from candidate variables in tables 1 and 2, the black/white variable was forced in to determine its association with PCI use. Analyses were run with and without a cluster correction for medical center.

Ethics and consent

This study was approved for expedited review by the University of Washington Institutional Review Board and was granted a waiver of informed consent.

Results

From the 6262 individuals in this study, only black (n = 680) or white (n = 3529) patients were included, resulting in 4209 individuals. Excluded were those of other racial or ethnic groups (n = 388) or those whose race was unknown (n = 1665). Race/ethnicity was unknown for over 25% of individuals. Compared to their white counterparts, black individuals, who comprised 16% of the study group, were younger, less often had lipid disorders, previous coronary artery bypass graft surgery, or chronic obstructive pulmonary disease but more often had diabetes, renal disease, or dementia (table 1). In addition, black patients lived closer to VHA medical centers and more often resided in the South region of the United States. Significantly, a higher proportion of black veterans smoked in the past year. The distribution of cardiac medications used prior to admission was similar in the 2 groups.

With respect to presenting signs and symptoms, black and white veterans were similar, except that the former group had higher systolic and diastolic blood pressures on admission (table 2). Symptoms were similar in the 2 groups, although black individuals reported less shoulder pain. ST segment elevation on the initial electrocardiogram was present in 16% and 18% (p = 0.10) of black and white patients, respectively, although the first troponin assay was more often elevated in black veterans (76% vs. 69%, p = 0.001). The highest troponin reading was elevated in 99% of both groups.

There were distinct differences concerning the type of admitting hospital as well as the kind of cardiology services received. Almost 75% of black patients were admitted to VHA medical centers with capacity to perform PCI, compared to only 54% of white veterans. Significantly, 29% of white veterans were admitted to VHA hospitals without cardiac catheterization laboratories. Consistent with this result was the finding that white individuals were more likely to be transferred for PCI than were black patients (table 3). With respect to cardiology services, over 90% of veterans received them, although 49% of black individuals had a cardiology attending as compared to 38% of white veterans (p < 0.0001).

Rates of cardiac catheterization performed ≤ 30 days after admission were similar in the 2 groups as were rates of PCI performed ≤ 12 hours from admission (table 3). However, a significantly lower proportion of black veterans received PCI ≤ 30 days after admission (odds ratio = 0.71, 95% confidence interval = 0.60-0.85). Even among those who underwent cardiac catheterization, the rate of PCI was still lower in black veterans (48% vs. 59%, p <

Discussion

For many years, race and gender differences in the use of cardiac revascularization procedures have been noted, and it appears that they have persisted through time [10]. Similar differences have been noted in VHA, although gender differences are less relevant given that relatively few women with AMI are treated in VHA medical centers [1][2][3][4][5][6][7]. In the current study, a higher proportion of black veterans were younger, had diabetes mellitus, renal disease, dementia, or elevated blood pressure, although more white patients had lipid disorders, previous coronary artery bypass surgery, or chronic obstructive pulmonary disease. Significantly, a higher proportion of black individuals smoked cigarettes in the year prior to admission.

Even though they were more likely to be admitted to medical centers with interventional capability, black veterans were less likely to undergo PCI. This was true in both unadjusted and adjusted analyses. White patients were 2 times more likely to be transferred for PCI, and this increased the numbers undergoing PCI. Equal proportions of African-Americans underwent emergent or urgent PCI, and although white veterans more often underwent bypass surgery, the absolute difference was slightly less than 4%.

In this study there were no black-white differences regarding the use of cardiac catheterization, although this was not the case in a previous study conducted in VHA medical centers [11]. The fact that there were no black-white differences with respect to use of cardiac catheterization may provide a possible explanation for the observed difference with respect to PCI. If results of cardiac catheterization indicated that black veterans had minimal coronary artery disease or had lesions that were not amenable to intervention, then the difference could be explained. Unfortunately, measures of extent of coronary disease were not collected as part of the records abstraction. Peniston et al. compared the use of cardiac revascularization procedures in African-American and white veterans who underwent cardiac catheterization, and did not find racial differences after adjusting for extent of disease [7]. Whittle et al. reported that among clinically similar black and white veterans undergoing coronary angiography, black patients were less likely to have obstructive coronary disease [12].

In addition to the absence of catheterization results, another limitation of this study was that an undetermined number of veterans may have received PCI in non VHA hospitals. With the exception of Medicare data (which were not available for this study), these procedures are not recorded in national databases, and therefore could not be identified. If a higher proportion of white patients underwent PCI in non VHA hospitals, then the racial difference reported in this study may have been underestimated.

Over 25% of veterans were of unknown race. Recently the means for acquisition of race/ethnicity have changed in VHA. Race/ethnicity is now reported by the patient rather than determined by the individual provider or administrative assistant responsible for entering data into the electronic medical record. If the question is not asked or if the patient is not present when data are entered, then race/ ethnicity is unknown.

Among those with unknown race, 43% had PCI within 30 days of admission; these individuals were on average 66 years of age and were less likely to have chronic conditions such as diabetes, congestive heart failure, lung disease, or cerebrovascular disease. Therefore patients with unknown race were relatively healthier and underwent PCI more often than their counterparts with known race. The problem of unknown race in VHA has been more apparent in the past several years. Therefore, for younger patients who did not use VHA health care in the 5 to 10 years prior to their events, race information from that time was not available to fill in missing information for 2003-2004. We were reluctant to impute race given the lack of adequate measures of characteristics highly correlated with race, such as income and education.

Conclusion

In this era of drug eluting stents and more aggressive treatment for AMI, black-white differences in the use of PCI persist even in the VHA, where access to procedures is not based on the ability to pay. However, given the equivalent use of cardiac catheterization as well as the equivalent use of PCI within 12 hours of admission, it is possible that less extensive or minimal coronary artery disease in black veterans could explain the observed difference.

Table 1 :
1Baseline demographics, medical histories, and prior cardiac medications by raceVariable

Table 2 :
2Presenting signs and symptomsVariable

Table 3 :
3Treatments, transfer status, in-hospital events, and mortalityPublish with Bio Med Central and every scientist can read your work free of charge"BioMed Central will be the most significant development for disseminating the results of biomedical researc h in our lifetime." Sir Paul Nurse, Cancer Research UK Your research papers will be: available free of charge to the entire biomedical community peer reviewed and published immediately upon acceptance cited in PubMed and archived on PubMed Central yours -you keep the copyright Submit your manuscript here: http://www.biomedcentral.com/info/publishing_adv.aspVariable

(page number not for citation purposes)
AcknowledgementsThis work was supported by a grant from the Department of Veterans Affairs Ischemic Heart Disease Quality Enhancement Research Initiative. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs.Competing interestsThe author(s) declare that they have no competing interests.Authors' contributionsCM-Conceived and designed study, performed data analysis, drafted manuscript.EL-Acquired data, constructed study database, assisted with data analysis, revised manuscript critically for important intellectual content.HS-Acquired data, constructed study database, assisted with data analysis, revised manuscript critically for important intellectual content.AES-Revised manuscript critically for important intellectual content.SDF-Revised manuscript critically for important intellectual content.All authors have read and approved the final version of the paper.Pre-publication historyThe pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/6/107/pre pub
Racial differences in the use of invasive cardiovascular procedures in the Department of Veterans Affairs medical system. J Whittle, J Conigliaro, C B Good, R P Lofgren, N Engl J Med. 329Whittle J, Conigliaro J, Good CB, Lofgren RP: Racial differences in the use of invasive cardiovascular procedures in the Depart- ment of Veterans Affairs medical system. N Engl J Med 1993, 329:621-7.

Variations in utilization of cardiac procedures in the Department of Veterans Affairs health care system: effects of race. D M Mirvis, R Burns, L Gaschen, F T Clear, M Graney, J Am Coll Cardiol. 74Mirvis DM, Burns R, Gaschen L, Clear FT, Graney M: Variations in utilization of cardiac procedures in the Department of Vet- erans Affairs health care system: effects of race. J Am Coll Car- diol 1994, 74:1297-304.

Racial variation in cardiac procedure use and survival following acute myocardial infarction in the Department of Veterans Affairs. E D Peterson, S M Wright, J Daley, G E Thibault, JAMA. 271Peterson ED, Wright SM, Daley J, Thibault GE: Racial variation in cardiac procedure use and survival following acute myocar- dial infarction in the Department of Veterans Affairs. JAMA 1994, 271:1175-80.

Acute myocardial infarction: clinical characteristics, management and outcome in a metropolitan Veterans Affairs Medical Center teaching hospital. J K Mickelson, C M Blum, J M Geraci, J Am Coll Cardiol. 29Mickelson JK, Blum CM, Geraci JM: Acute myocardial infarction: clinical characteristics, management and outcome in a met- ropolitan Veterans Affairs Medical Center teaching hospital. J Am Coll Cardiol 1997, 29:915-25.

Examination of racial differences in management of cardiovascular disease. J A Ferguson, W M Tierney, G R Westmoreland, L A Mamlin, D S Segar, G J Eckert, X H Zhou, D K Martin, M Weinberger, J Am Coll Cardiol. 30Ferguson JA, Tierney WM, Westmoreland GR, Mamlin LA, Segar DS, Eckert GJ, Zhou XH, Martin DK, Weinberger M: Examination of racial differences in management of cardiovascular disease. J Am Coll Cardiol 1997, 30:1707-13.

Racial differences in the performance of invasive cardiac procedures in a Department of Veterans Affairs Medical Center. S P Sedlis, V J Fisher, D Tice, R Esposito, L Madmon, E H Steinberg, J Clin Epidemiol. 50Sedlis SP, Fisher VJ, Tice D, Esposito R, Madmon L, Steinberg EH: Racial differences in the performance of invasive cardiac pro- cedures in a Department of Veterans Affairs Medical Center. J Clin Epidemiol 1997, 50:899-901.

Severity of coronary artery disease in black and white male veterans and the likelihood of revascularization. R L Peniston, D Y Lu, V Papademetriou, R D Fletcher, Am Heart J. 139Peniston RL, Lu DY, Papademetriou V, Fletcher RD: Severity of cor- onary artery disease in black and white male veterans and the likelihood of revascularization. Am Heart J 2000, 139:840-7.

P W Groeneveld, S B Laufer, A M Garber, Technology diffusion, hospital variation, and racial disparities among elderly Medicare beneficiaries. 43Groeneveld PW, Laufer SB, Garber AM: Technology diffusion, hospital variation, and racial disparities among elderly Medi- care beneficiaries: 1989-2000. Med Care 2005, 43:320-9.

Data resources in the Department of Veterans Affairs. C Maynard, M Chapko, Diabetes Care. 27Maynard C, Chapko M: Data resources in the Department of Veterans Affairs. Diabetes Care 2004, 27:B22-B26.

Sex and race differences in the management of acute myocardial infarction. V Vaccarino, S S Rathore, N K Wenger, P D Frederick, J L Abramson, H V Barron, A Manhapra, S Mallik, H M Krumholz, N Engl J Med. 353Vaccarino V, Rathore SS, Wenger NK, Frederick PD, Abramson JL, Barron HV, Manhapra A, Mallik S, Krumholz HM: Sex and race dif- ferences in the management of acute myocardial infarction, 1994 through 2002. N Engl J Med 2005, 353:671-682.

Racial disparities in cardiac catheterization as a function of patient perspectives. N R Kressin, B H Chang, J Whittle, E D Peterson, J A Clark, A K Rosen, M B Orner, T C Collins, L G Alley, L A Petersen, Am J Public Health. 94Kressin NR, Chang BH, Whittle J, Peterson ED, Clark JA, Rosen AK, Orner MB, Collins TC, Alley LG, Petersen LA: Racial disparities in cardiac catheterization as a function of patient perspectives. Am J Public Health 94:2091-2097.

Racial differences in the prevalence of coronary obstructions among men with positive nuclear imaging studies. J Whittle, N R Kressin, E D Peterson, M B Orner, M Glickman, M Mazzella, L A Petersen, J Am Coll Cardiol. 47Whittle J, Kressin NR, Peterson ED, Orner MB, Glickman M, Mazzella M, Petersen LA: Racial differences in the prevalence of coro- nary obstructions among men with positive nuclear imaging studies. J Am Coll Cardiol 2006, 47:2034-2041.