 Methodology article
 Open Access
 Published:
A powerful nonparametric method for detecting differentially coexpressed genes: distance correlation screening and edgecount test
BMC Systems Biology volume 12, Article number: 58 (2018)
Abstract
Background
Differential coexpression analysis, as a complement of differential expression analysis, offers significant insights into the changes in molecular mechanism of different phenotypes. A prevailing approach to detecting differentially coexpressed genes is to compare Pearson’s correlation coefficients in two phenotypes. However, due to the limitations of Pearson’s correlation measure, this approach lacks the power to detect nonlinear changes in gene coexpression which is common in gene regulatory networks.
Results
In this work, a new nonparametric procedure is proposed to search differentially coexpressed gene pairs in different phenotypes from largescale data. Our computational pipeline consisted of two main steps, a screening step and a testing step. The screening step is to reduce the search space by filtering out all the independent gene pairs using distance correlation measure. In the testing step, we compare the gene coexpression patterns in different phenotypes by a recently developed edgecount test. Both steps are distributionfree and targeting nonlinear relations. We illustrate the promise of the new approach by analyzing the Cancer Genome Atlas data and the METABRIC data for breast cancer subtypes.
Conclusions
Compared with some existing methods, the new method is more powerful in detecting nonlinear type of differential coexpressions. The distance correlation screening can greatly improve computational efficiency, facilitating its application to large data sets.
Background
The vast majority of human diseases are complex diseases, in the sense that they are not the consequence of an abnormality of a single gene, but a result of changes in many genes. Thanks to the rapid advance of highthroughput technologies, researchers nowadays can investigate the association between a disease and tens of thousands of genes simultaneously. Two types of analysis, namely differential expression (DE) analysis and differential coexpression (DCE) analysis, have been extensively applied in genetic association studies [1–4]. Differential expression analysis targets genes with differential expression levels in different phenotypes, while DCE analysis detects gene pairs or gene sets that are differentially associated or regulated in different groups. Over the past years, there have been considerable tools developed for DE analysis and other similar analyses such as differential methylation (DM) analysis. One can refer to Soneson and Delorenzi (2013) [5] for a comprehensive review and comparison of several most popular tools including edgeR, DESeq, TSPM, baySeq, EBSeq and ShrinkSeq. Despite the success of DE analysis, the progress on DCE analysis is relatively slow partially due to the combinatorial nature of the problem and the lack of powerful statistical test for comparing multidimensional patterns.
Current DCE analyses are mostly relied on Pearson’s correlation coefficient [1, 2, 6, 7], which is sensitive to outliers and only measures the strength of linear dependence. Some modified measures such as Spearman’s correlation and biweight midcorrelation [1] are more robust to outliers, but still unable to capture nonlinear changes in coexpression. In this paper, we introduce a new method to generally test for DCE gene pairs without assuming linear or monotonic relation between genes. First of all, it is important to emphasize that the objective of this work is to search for differential coexpressions of single gene pairs, which is different from objective of approaches that set out to find modules of differentially coexpressed genes. To begin with, we give the formal definitions of gene coexpression and DCE genes: “The coexpression of two genes is defined as the dependence between their expression levels. If the dependency structure in one phenotype is different from that in another, the two genes are called DCE genes” [8]. For computational simplicity, most existing methods assume that genes are jointly normally distributed, i.e., the correlations between genes are linear. Under this assumption, the DCE testing is equivalent to testing the equality of two correlation coefficients, which can be formulated as the following hypothesis testing
where ρ_{1} and ρ_{2} represent the true correlation coefficients between gene A and gene B in two phenotypes. Let r_{1} and r_{2} be the sample correlation coefficients, by Fisher’s ztransformation, we have
where n_{1} and n_{2} stand for the sample sizes of two phenotypes. A routine twosample ztest can then be directly applied to evaluate the significance:
where Z represents a standard normal random variable.
The method described above is simple as the calculation only involves productmoment correlations, and it generally works well for linearly dependent genes. However, the assumption of joint normality is not realistic as the gene expression data could strongly deviate from normality. To this end, we relax the normal assumption and reformulate the DCE search as a general statistical comparison between two joint distributions, so that the DCE genes, based on their definition, can be tested through the following hypothesis setup:
where \(\mathbf {F}^{*}_{1}\) and \(\mathbf {F}^{*}_{2}\) represent the joint distributions of genes A and B in two phenotypes after the quantile normalization. By quantile normalization, the marginal distributions match across groups, so that one can test for the difference between two dependency structures (in spirit, it is same as comparing two copula densities, \(\frac {f_{1}(x, y)}{\int f_{1}(x, y)dx \int f_{1}(x, y)dy}\) and \(\frac {f_{2}(x, y)}{\int f_{2}(x, y)dx \int f_{2}(x, y)dy}\)). A significant discrepancy between \(\mathbf {F}^{*}_{1}\) and \(\mathbf {F}^{*}_{2}\) indicates differential coexpression in two phenotypes.
It should be noted that the test proposed here does not rely on any parametric assumption but generally targets all types of DCE. One can explicitly test H_{0} with a recently developed edgecount test [9]. However, unlike the Pearson’s correlation method, the new test requires several intermediate steps including the calculation of minimum spanning trees, therefore it could be less efficient when applied to largescale data. To overcome this difficulty, we use the distance correlation measure to screen out noncoexpressed (independent) gene pairs before the edgecount test, so that the search space can be greatly reduced. The distance correlation measure has appealing theoretical properties and can generally capture nonlinear associations. On the whole, we put forward a complete framework for DCE analysis which is effective and applicable to largescale expression data.
The rest of the paper is structured as follows: Section “Methods” reviews the technical details of distance correlation screening and edgecount test. Simulation studies are performed to compare the edgecount test with two existing approaches based on Pearson’s correlation and mutual information. In Section “Results”, we apply this new approach to the Cancer Genome Atlas (TCGA) data as well as the METABRIC data for the DCE analysis between four subtypes of breast cancer. We discuss the strengths and some possible extensions of the new approach in Section“Discussion” and conclude this paper in Section“Conclusions”.
Methods
Distance correlation screening
Our screening step is based on distance correlation (DC), which is a measure of dependence between two random vectors, not necessarily of same dimension [10]. For given random vectors X and Y, if we let ϕ_{ x }(t) and ϕ_{ y }(s) be the respective characteristic functions, then the distance covariance between X and Y can be defined as follows:
where d_{ x } and d_{ y } are the dimensions of X and Y, \(c_{d_{x}}=\frac {\pi ^{(1+d_{x})/2}}{\Gamma \{(1+d_{x})/2\}}\) and \(c_{d_{y}}=\frac {\pi ^{(1+d_{y})/2}}{\Gamma \{(1+d_{y})/2\}}\). Unless otherwise specified, \(\pmb {z}_{d_{z}}\) denotes the Euclidean norm of \(\pmb {z}\in \mathbb {R}^{d_{z}}\), and \(\phi ^{2} = \phi \bar {\phi }\) for a complexvalued function ϕ and its conjugate \(\bar {\phi }\).
Similar as Pearson’s correlation coefficient, the DC between X and Y is defined as a rescaled distance covariance:
Generally, we have 0≤dCor(X,Y)≤1, which is different from Pearson’s correlation. One remarkable property of DC is that dCor(X,Y)=0 if and only if X and Y are independent [11–13], indicating that DC can also measure nonlinear associations. With random samples {(X_{ i },Y_{ i }),i=1,…,n}, a natural estimator of dCov(X,Y) can be obtained as follows:
where
if we let \(\phantom {\dot {i}\!}a_{ij}=\pmb {X}_{i}\pmb {X}_{j}_{d_{X}}\), \(\bar {a}_{i}=\frac {1}{n}\sum \limits _{k=1}^{n}\pmb {X}_{k}\pmb {X}_{i}_{d_{X}}\), \(\bar {a}_{j}=\frac {1}{n}\sum \limits _{l=1}^{n}\pmb {X}_{l}\pmb {X}_{j}_{d_{X}}\), \(\bar {a}=\frac {1}{n^{2}}\sum \limits _{k=1}^{n}\sum \limits _{l=1}^{n}\pmb {X}_{l}\pmb {X}_{k}_{d_{X}}\), \(b_{ij}=\pmb {Y}_{i}\pmb {Y}_{j}_{d_{Y}}\phantom {\dot {i}\!}\), \(\bar {b}_{i}=\frac {1}{n}\sum \limits _{k=1}^{n}\pmb {Y}_{k}\pmb {Y}_{i}_{d_{Y}}\), \(\bar {b}_{j}=\frac {1}{n}\sum \limits _{l=1}^{n}\pmb {Y}_{l}\pmb {Y}_{j}_{d_{Y}}\), \(\bar {b}=\frac {1}{n^{2}}\sum \limits _{k=1}^{n}\sum \limits _{l=1}^{n}\pmb {Y}_{l}\pmb {Y}_{k}_{d_{Y}}\). The sample estimate of DC can be obtained immediately:
One can test for significance of DC using an approximate ttest proposed by Szekely and Rizzo (2013) [13], which was implemented in R package energy [14]. Szekely and Rizzo (2013) established the following result under high dimensions
where \(\mathcal {R}^{*}_{n}(\pmb {X},\pmb {Y})\) represents a modified distance correlation between X and Y (see Szekely and Rizzo (2013), Eq 2.10, p.197) and \(v=\frac {n(n3)}{2}\). Here, it is worth noting that although the tapproximation above is derived under high dimensions, it also works well for lowdimension cases (in our problem, dimensions of X and Y both equal one for each test). To evaluate the performance of the tapproximation under dimension one, we consider two independence settings

Setting 1: X_{ i }∼N(0,1), Y_{ i }∼N(0,2), i=1,2,…,50,

Setting 2: X_{ i }∼Uniform(0,1), Y_{ i }∼Uniform(0,2), i=1,2,…,50.
For each setting, we generated 10,000 data sets and calculated the test statistic \(\mathcal {T}_{n}\) for each data set. Figure 1 compared the sample distribution of \(\mathcal {T}_{n}\) with the asymptotic t distribution (close to a standard normal distribution as the degree of freedom v−1 is generally large). Futhermore, we compared the approximate pvalue with the permutation pvalue (based on 10,000 random shuffles) in 100 replications. As shown in Fig. 2, the approximate pvalues are very close to the permutation pvalues, indicating a satisfactory performance of the tapproximation under low dimensions.
The distance correlation measure has been applied in previous genomic studies to quantify gene coexpressions [15]. Besides DC, there are several measures that can pick up nonlinear dependence between variables, although each of them has its own practical limitations. Clark (2013) [16] empirically compared six popular measures including Pearson’s correlation, Spearman’s correlation, distance correlation, mutual information (MI), maximum information coefficient (MIC) and Hoeffding’s D under a variety of different settings, and it was found that the six methods perform almost equally well in detecting the linear correlation. However, under the nonlinear dependence, the distance correlation and MIC performed notably better than the other measures. There are two considerations that lead to the choice of DC instead of MIC in our analysis. First, DC is straightforward to calculate and not an approximation while MIC relies on a userdefined number of grids for approximation. Second, as pointed out in some recent studies [17, 18], the DC exhibits more statistical power than MIC under moderate or small sample sizes.
Edgecount test
Our testing step is to compare two multivariate distributions (dimension is 2 in DCE analysis). In statistics literature, there are mainly two types of multivariate tests, namely the multidimensional KolmogorovSmirnov (KS) test [19] and edgecount test [20, 21]. These two methods, however, both have practical limitations when applied to real data. For instance, KS test is very conservative, i.e., the null hypothesis is too often not rejected. Also, by the brute force algorithm, the application of multidimensional KS test can be prohibitively computationally intensive. The edgecount test is easy to implement but it is known to be problematic under the location and scale alternatives. Recently, Chen and Friedman [9] developed a modified version of edgecount test, which works properly under different alternatives and exhibits substantial power gains over existing edgecount tests. Similar as other edgecount tests, the new test is based upon a similarity graph such as minimum spanning tree (MST, [22]) that is constructed over the pooled samples from different groups. Generally, if two groups have different distributions, samples would be preferentially closer to others from the same group than those from the other group, therefore the edges in the MST would be more likely to connect samples from the same group. The test rejects the null if the number of betweengroup edges is significantly less than expected.
To be precise, we let x_{1},x_{2},…,x_{ n } and y_{1},y_{2},…,y_{ m } be i.i.d. samples from two multivariate distributions F_{ X } and F_{ Y }, respectively. We first pooled samples from two groups and indexed them by 1,2,…,N=n+m. A MST is then constructed on the pooled samples using Kruskal’s algorithm [22]. Unless otherwise specified, G represents the MST (or other similarity graphs) as well as the set of all edges, and G denotes the total number of edges. To illustrate the technical details, we adopted the notations from Chen and Friedman’s paper. Let g_{ i }=0 if sample i is from group X and g_{ i }=1 otherwise. For the edge e connecting samples i and j, i.e., e=(i,j), we define:
and
Here R_{1} and R_{2} represent the numbers of edges connecting samples from same group, and R_{0} stands for number of edges connecting samples from different groups. The new test statistic simply quantifies the deviation of (R_{1},R_{2}) from their expected values under true H_{0}. It has the following quadratic form:
where μ_{1}=E(R_{1}), μ_{2}=E(R_{2}) and Σ=V((R_{1},R_{2})^{T}) have the following expressions (see the Appendix of Chen and Friedman’s paper for detailed proof):
where \(C=\frac {1}{2}{\sum \nolimits }_{i=1}^{N}G_{i}^{2}G\), and G_{ k } stands for the subgraph in G that includes all edges that connect to node k. It was proved that under the permutation null hypothesis, S asymptotically follows a Chisquare distribution with 2 degrees of freedom [9]. The pvalue approximation generally works well under relatively small sample size, for instance, when min(n,m)=20. In their work, Chen and Friedman also suggested that the use of kMST graphs (e.g., 3MST or 5MST) may lead to a better approximation of pvalue in practice.
It is noteworthy to mention that Chen and Friedman’s method was developed for twogroup comparison. In the case of multiple groups, a sequence of pairwise comparisons need to be conducted. Recently, we extended Chen and Friedman’s test to multiplegroup case and proposed an overall test to compare more than two groups simultaneously. In our technical report [23], it was proved that the test statistics for p groups asymptotically follows a Chisquare distribution with p degrees of freedom under mild regularity conditions. To be precise, for an edge e in graph G, we let
then the following theorem can be derived:
Theorem 1
If G=O(N), \({\sum \nolimits }_{k=1}^{N}G_{k}^{2}\frac {4G^{2}}{N}=O(N)\), A_{ e }B_{ e }=o(N^{3/2}), \({\lim }_{N\rightarrow \infty }\frac {N_{i}}{N}=\lambda _{i}\in (0, 1)\), then
where i=1,…,p is the group index.
The expected values and covariance matrix can be derived as in (7):
where \(N={\sum \nolimits }_{k=1}^{p}n_{k}\) and \(C=\frac {1}{2}{\sum \nolimits }_{i=1}^{N}G_{i}^{2}G\). The detailed proof for Theorem 1 can be found in the Appendix of Zhang et al. (2017) [23].
Simulation study: edgecount test versus two existing approaches
We performed a simulation study to empirically compare the edgecount test with two existing methods based on Pearson’s correlation and mutual information. Particularly, we considered the following linear setting and nonlinear setting, where X and Y represent the expression levels of two genes and subscripts “1” and “2” stand for two conditions:

Linear setting: \((X_{1}, Y_{1})^{T}\sim \mathrm {N}\left [\left (\begin {array}{l} 0\\ 0 \end {array}\right), \left (\begin {array}{ll} 1 & \rho \\ \rho & 1 \end {array}\right)\right ]\), \((X_{2}, Y_{2})^{T}\sim \mathrm {N}\left [\left (\begin {array}{l} 0\\ 0 \end {array}\right), \left (\begin {array}{cc} 1 & \rho +\Delta \\ \rho +\Delta & 1 \end {array}\right)\right ]\), where ρ=0.3, Δ∈{0.1,0.2,…,0.6}.

Nonlinear setting: X_{ i }∼Uniform(−2,2), \(Y_{i}=X_{i}^{2}+\epsilon _{i}\), \(\epsilon _{i}\sim \mathrm {N}\left (0, \sigma _{i}^{2}\right)\), i=1,2, σ_{1}=0.5, σ_{2}=σ_{1}+Δ, Δ∈{0.1,0.2,…,0.6}.
For each setting, we generated 1,000 data sets with sample sizes n_{1}=n_{2}=100 and three approaches were applied to test for the difference between two joint distributions. For edgecount test, we took 3MST based on Euclidean distance and computed the pvalue using Chisquare approximation. The R package infotheo [24] was used to estimate the entropies of X_{ i } and Y_{ i }, as well as the mutual information between X_{ i } and Y_{ i }, i=1,2. To evaluate the significance of the mutual information change, we performed a Fisher’s z transformation introduced in Zhang et al. (2012) [25]. To be precise, let H(X_{ i }) be the entropy of variable X_{ i }, and I(X_{ i },Y_{ i }) be the mutual information between X_{ i } and Y_{ i }, then the transformed z_{ i } given below approaches to a standard normal distribution with variance \(\frac {1}{n_{i}3}\):
where \(I^{*}(X_{i},Y_{i})=\frac {I(X_{i},Y_{i})}{H(X_{i})+H(Y_{i})}\). The pvalue can then be obtained by a twosample z test, i.e.,
For each data set, we conducted a quantile normalization to match the marginals and tested the hypothesis at α=0.05 (\(H_{0}: \pmb {F}^{*}_{1}(X_{1},Y_{1})=\pmb {F}^{*}_{2}(X_{2},Y_{2})\)) with three different methods, where \(\pmb {F}^{*}_{i}\) represented the joint distribution of (X_{ i },Y_{ i }) after the marginal matching. The accuracy (true positive rate) of each method under each setting was summarized in Fig. 3. As we can see, all the three methods achieved good accuracy in the linear setting (except in the subtle case of Δ=0.1). The Pearson’s correlation and edgecount test performed slightly better than the mutual information. For the nonlinear (quadratic) setting, the edgecount test substantially outperformed the other two methods, while the Pearson’s method completely failed to identify the difference.
Our simulation study demonstrated the capability of our edgecount test in capturing both linear and nonlinear changes. Generally, the edgecount test performs similarly well as Pearson’s correlation and mutual information under linear setting but achieves significantly better sensitivity for nonlinear setting.
Results
In this section, we applied the twostep pipeline to search DCE genes in four subtypes of breast cancer using the Cancer Genome Atlas (TCGA) data. Four gene sets, including two KEGG gene pathways and two MSigDB hallmark gene sets, were used as illustrative examples. We validated our findings by the largescale METABRIC breast cancer data.
Data preparation
In TCGA, each subject is represented by multiple molecular data types including gene expression, genotype (SNP), exon expression, MicroRNA expression, copy number variation, DNA methylation, somatic mutation, and protein expression [3, 26]. We only used the gene expression (RNAseq) data in this study. The TCGA transcriptome profiling data was downloaded through Genomic Data Commons (GDC) portal in January 2017. The expression level of each gene was quantified by the count of reads mapped to the gene. The quantifications were done by software HTSeq of version 0.9.1 [27] and the count data were logtransformed for further processing. We excluded 43 subjects from the analysis including 12 male subjects and 31 subjects with more than 1% missing values. In addition, we removed the effects due to different age groups and batches using a medianmatching and variancematching strategy [28]. For example, the batch effect can be removed in the following way:
where g_{ ijk } refers to the expression value for gene i from sample k in batch j (j=1,2,…,J;k=1,2,…,n_{ j }), M_{ ij } represents the median of \(g_{ij}=(g_{ij1},\ldots,g_{ijn_{j}})\phantom {\dot {i}\!}\), M_{ i } refers to the median of g_{ i }=(g_{i1},…,g_{ iJ }), \(\hat {\sigma }_{g_{i}}\) and \(\hat {\sigma }_{g_{ij}}\) stand for the standard deviations of g_{ i } and g_{ ij }, respectively.
The remaining 959 samples were further classified into five subtypes according to two molecular signatures, namely PAM50 [29] and SCMOD2 [30]. The two classifications were implemented separately using R package genefu [31] and we obtained 530 subjects with concordant classification by two classifiers. The resulting set contains 221 subjects in luminal A group, 119 in luminal B group, 74 in her2enriched group, 105 in basallike group and 11 in normallike group. The normallike group was excluded from the analysis due to the low sample size and only four subtype groups were considered.
Finally, we perform a quantile normalization [32] for each group separately, so that the marginal distributions of all the genes match across groups. The purpose of quantile normalization is to avoid the rejection of H_{0} due to marginal difference (differential expression) instead of different dependency patterns (differential coexpression).
Some illustrative examples
We illustrated the new method using four molecular pathways, including the cell cycle and ERBB pathways from KEGG database, as well as the JAKSTAT and TGFbeta signaling pathways from MSigDB database. All the selected pathways play critical roles in the initiation and progression of many human cancers. For instance, KEGG cell cycle pathway contains 128 genes that coregulate cell proliferation, including ATM, RB1, CCNE1 and MYC. Abnormal regulation among these genes may cause the over proliferation of cells and an accumulation of tumor cell numbers. The ERBB pathway in KEGG database consisted of 87 genes including important protooncogenes and tumor suppressors such as PIK3C, KRAS and STAT5. It is known that ERBB pathway is closely related to the development of a wide variety of types of tumor. Especially, the excessive signaling of growth factor receptors ERBB1 and ERBB2 are critical factors in the malignancy of solid tumor [3]. The JAKSTAT signaling pathway and TGFbeta signaling pathway were also known to play critical roles in tumor suppression and cancer metastasis. For instance, TGFbeta can modulate processes such as cell invasion, immune regulation, and microenvironment modification that cancer cells may exploit to their advantage [33].
For each subtype group, we first computed the distance correlation matrix and corresponding pvalue matrix for all gene pairs (see Methods section for details). A BenjaminiHochberg (BH, [34]) procedure with FDR≤0.05 was then applied to screen out uncorrelated genes. A gene pair was deemed as uncorrelated if the adjusted pvalues in four subtypes are all above 0.05. This screening resulted in a total of 487 correlated gene pairs in cell cycle pathway, 359 in ERBB pathway, 592 in JAKSTAT signaling pathway and 440 in TGFbeta signaling pathway. These four reduced sets of gene pairs were used as the search space for the testing step.
For each gene pair in the search space, we carried out hypothesis tests to compare the coexpression patterns in each pair of subtypes (totally \(\binom {4}{2}=6\) comparisons). An edgecount test with 3MST was implemented, followed by a BH procedure with FDR≤0.05 for multiplicity adjustment. Finally, we identified 120 DCE gene pairs in cell cycle pathway, 94 in ERBB pathway, 122 in JAKSTAT signaling pathway and 102 in TGFbeta signaling pathway. Figures 4, 5, 6, 7 showed the four DCE networks, where each edge indicated a DCE gene pair in four subtypes. It should be noted that the networks we presented here are different from the regular gene coexpression networks, instead, each network represents a collection of gene pairs that are differentially coexpressed under different conditions. When interpreting the clusters in the networks, one reasonable hypothesis could be that they represent groups of genes that are significantly coexpressed in some condition/conditions but not in others. For instance, we found that genes MYD88, STAT1, TYK2, PTPN11, CNTFR, IL17RA, LTE and CD44 (highly connected in Fig. 6) exhibited a much stronger coexpression in the basallike subtype than the other three subtypes, according to the distance correlation matrices in four subtypes. In practice, one may use our pipeline to infer the differentially coexpressed network, and then focus on a subnetwork (subset of genes) of interest by investigating the coexpressions in different conditions, either numerically or graphically.
Two examples of the identified DCE gene pairs were shown in Figs. 8 and 9. Figure 8 suggested that the coexpression of genes PAK3 and AKT3 in basallike group was substantially different from those in the other groups. In Fig. 9, genes SMAD4 and CDC27 exhibited a negative coexpression in the luminal B group, which was not observed in luminal A, her2enriched or basallike group.
Comparison with Pearson’s correlation method
To benchmark our new method, we compared it with the DCE search based on Pearson’s correlation, as introduced in the Background section. A twosample ztest with Fisher’s ztransformation was conducted, followed by a BH procedure with FDR≤0.05 for fair comparison. By Pearson’s correlation method, a total of 98 DCE gene pairs were identified in cell cycle pathway, 73 in ERBB pathway, 93 in JAKSTAT signaling pathway and 83 in TGFbeta signaling pathway. The agreement between the two approaches was summarized using Venn diagrams in Fig. 10. It can be seen that almost all the DCE genes by Pearson’s method were also captured by the new approach, but a significant number of gene pairs captured by the new approach were missed by the Pearson’s correlation method. Two gene pairs of such were provided as examples in Figs. 11 and 12. The different association patterns between genes RPS6KB2 and ELK1 in four groups were shown in Fig. 11, where it could be seen that in luminal B subtype, the two genes were positively associated when RPS6KB2 was underexpressed, but the expression of ELK1 became stabilized when RPS6KB2 was overexpressed. By Pearson’s correlation method, however, none of the pvalues was significant. There were two pvalues (after adjustment) that were highly significant by the edgecount test: p=9.4×10^{−4} for \(H_{0}: \pmb {F}^{*}_{luminal~A}=\pmb {F}^{*}_{luminal~B}\) and p=1.1×10^{−3} for \(H_{0}: \pmb {F}^{*}_{basallike}=\pmb {F}^{*}_{luminal~B}\). Likewise, as shown in Fig. 12, genes CDK2 and CDC14A exhibited a Vshape coexpression in her2enriched group, but not in the other groups. These examples indicated that our new method dominates the prevailing Pearson’s correlation method in searching DCE genes, therefore may reveal additional clues for understanding the changes in gene regulation mechanisms of different phenotypes.
Validation by METABRIC data
To validate the identified sets of DCE gene pairs, we repeated the twostep procedure to largescale data cohort, namely the METABRIC data [35]. The METABRIC data set contained molecular profiles for 2506 breast cancer samples and each sample has been assigned a subtype based on PAM50 signature. In our analysis, we included 700 samples in luminal A group, 475 in luminal B group, 224 in her2enriched group and 209 in basallike group. After the distance correlation screening and quantile normalization for each gene, we applied two methods, namely the Pearson’s correlation and edgecount test, to search DCE gene pairs in the four aforementioned gene sets. Same thresholds of FDR cutoff for distance correlation screening and edgecount test were used as in the TCGA analysis. With the METABRIC data, we identified four sets of DCE gene pairs for four pathways and Fig. 13 summarized the comparison between TCGA data and METABRIC data. The agreement between the two data sets ranged from 64.2 to 80.2% for four pathways, indicating a satisfactory reproducibility of our method. In addition, we compared two DCE sets of the nonlinear type that were identified by edgecount test but missed by Pearson’s method. As can be seen from Fig. 14, these two data sets also achieved a good agreement on the nonlinear DCE pairs. For instance, out of 23 nonlinear DCE pairs using TCGA data, 18 were confirmed by the METABRIC data.
Discussion
In this article, we developed a nonparametric method to effectively identify variability in gene coexpression pattern among multiple phenotypes. Our work presents novelty in two aspects. Firstly, we dropped the assumption of joint normality between genes and directly test if a gene pair follow the same joint distribution over different phenotypes. By a graphbased approach, the comparison between multivariate distributions was transformed to an edgecount test which is easy to implement. The statistical test used in this study is fully nonparametric and it rejects null hypothesis under different types of differential coexpressions including linear and nonlinear types. By a real life application, we demonstrated how the proposed test is better able to capture the DCE genes as compared to the Pearson’s correlation method.
Second, to make the test applicable to largescale data, we employed a distance correlation measure to filter out all the noncoexpressed gene pairs prior to the testing step. One shortcoming of the edgecount test is that it requires the calculation of a similarity graph that connects all the samples. For example, in our analysis of the breast cancer data, a 3MST (union of three nonoverlapping MSTs) was used as the similarity graph. Under large number of genes, this step can be computationally expensive. As a well accepted fact in biology, most gene coexpression networks are overall sparse, although they might be locally dense, hence the coexpression screening step should considerately reduce the search space. In the example of KEGG cell cycle pathway, the search space was reduced from more than 8000 gene pairs to less than 500.
Throughout this paper, we have focused on the study of coexpression between two genes. Nevertheless, it is noteworthy that the proposed test can be readily applied to multiplegene cases. In fact, Chen and Friedman’s test, as well as the multigroup extension, is merely built upon a similarity graph connecting all the samples, and the construction of graph depends only on the interpoint distances regardless of the dimension [9]. In practice, one can simply use Euclidean norm as the interpoint distance and construct the similarity graph such as MST or kMST. Additionally, because of the flexibility of our approach, one can also explicitly test for the difference in a higherorder interaction such as threeway gene coexpression, by properly controlling all the marginals and lowerorder interactions.
Conclusions
Differential coexpression analysis is critical for the identification of diseaserelated factors. Motivated by the fact that nonlinear coexpressions generally exist in cellular regulations, we develop a new nonparametric method for DCE analysis, which measures and compares gene coexpressions in linear and nonlinear aspects. Our method does not rely on any assumption regarding the probability distributions of the genes being studied, but it generally tests the equality of two or multiple coexpression patterns through a powerful graphbased test. For practical consideration, we suggest a screening step based on distance correlation to tackle the computational burden for largescale data. The proposed computational procedure can also be applied to other similar bioinformatics problems such as the differential comethylation analysis [36, 37] and differential gene set analysis [38, 39].
Abbreviations
 BH:

BenjaminiHochberg
 DC:

Distance correlation
 DCE:

Differential coexpression or differentially coexpressed
 DE:

Differential expression or differentially expressed
 MST:

Minimum spanning tree
 TCGA:

The cancer genome atlas
References
 1
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2011; 9(559).
 2
Yu H, Liu BH, Ye ZQ, Li C, Li YX, et al.Linkbased quantitative methods to identify differentially coexpressed genes and gene pairs. BMC Bioinformatics. 2011; 12(316).
 3
The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012; 490:61–70.
 4
Wang L, Mo Q, Wang J. MIrExpress: A databased for gene expression correlation in immune cells based on mutual information and Pearson correlation. J Immunol. 2015; 2015(140819).
 5
Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNAseq data. BMC Bioinformatics. 2013; 14(91).
 6
Li J, Li YX, Li YY. Differential Regulatory Analysis Based on Coexpression Network in Cancer Research. BioMed Res Int. 2016; 2016(4241293):1–8.
 7
McKenzie AT, Katsyv I, Song W, Wang M. DGCA: A comprehensive R package for Differential Gene Correlation Analysis. BMC Syst Biol. 2016; 10(106), Zhang, B.
 8
Santo S, Galatro T, Watanabe R, ObaShinjo S, Nagahashi M, et al.GoGA: An R package to identify differentially coexpressed gene sets by analyzing the graph spectra. PLoS One. 2015; 10(8).
 9
Chen H, Friedman JH. A new graphbased twosample test for multivariate and object data. J Am Stat Assoc. 2017; 112:397–409.
 10
Li R, Zhong W, Zhu L. Feature screening via distance correlation learning. J Am Stat Assoc. 2012; 107:1129–39.
 11
Szekely GJ, Rizzo ML, Bakirov NK. Measuring and testing dependence by correlation distances. Ann Stat. 2007; 35:2769–94.
 12
Szekely GJ, Rizzo ML. Brownian distance covariance. Ann Appl Stat. 2009; 3:1233–303.
 13
Szekely GJ, Rizzo ML. The distance correlation ttest of independence in high dimension. J Multivar Anal. 2013; 117:193–213.
 14
Rizzo ML, Szekely GJ. 2017. R package energy: https://cran.rproject.org/web/packages/energy/energy.pdf.
 15
Albukhaiti H, Luo J. Using differential nonlinear gene coexpression network analysis for identification gastric cancer related genes. Biomed Res. 2017; 28(18):8014–7.
 16
Clark M. A comparison of correlation measures. Technical report of University of Notre Dame. 2013.
 17
Kinney J, Atwal G. Equitability, mutual information, and the maximal information coefficient. Proc Natl Acad Sci U S A. 2014; 111(9):3354–9.
 18
Simon N, Tibshirani S. Comment on “Detecting novel associations in large data sets”. 2013. http://statweb.stanford.edu/tibs/reshef/comment.pdf.
 19
Lopes RHC, Hobson PR, Reid ID. Computationally efficient algorithms for the twodimensional KolmogorovSmirnov test. J Phys Conf Ser. 2008; 19(4).
 20
Friedman JH, Rafsky LC. Multivariate generalizations of the WaldWolfowitz and Smirnov twosample tests. Ann Stat. 1979; 7(4):697–717.
 21
Rosenbaum PR. An exact distributionfree test comparing two multivariate distributions based on adjacency. J R Stat Soc Ser B. 2005; 67(4):515–30.
 22
Cheriton D, Tarjan R. Finding minimum spanning trees. SIAM J Comput. 2006; 5(4):724–42.
 23
Zhang Q, Mahdi G, Chen H. A graphbased multisample test for identifying pathways associated with cancer progression. Technical Report. 2017.
 24
Meyer PE. 2014. R package infotheo: https://cran.rproject.org/web/packages/infotheo/infotheo.pdf.
 25
Zhang X, Zhao X, He K, Lu L, Cao Y, et al.Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics. 2012; 28(1):98–104.
 26
Zhang Q, Burdette JE, Wang JP. Integrative network analysis of TCGA data for ovarian cancer. BMC Syst Biol. 2014; 8(1338):1–18.
 27
Anders S, Pyl PT, Huber W. HTSeq  a Python framework to work with highthroughput sequencing data. Bioinformatics. 2015; 31:166–9.
 28
Hsu F, Serpedin E, Hsiao T, Bishop A, Dougherty E, et al.Reducing confounding and suppression effects in tcga data: an integrated analysis of chemotherapy response in ovarian cancer. BMC Genomics. 2012; 13(S13).
 29
Liu MC, Pitcher BN, Mardis ER, Davies SR, Friedman PN, et al.PAM50 gene signatures and breast cancer prognosis with adjuvant anthracycline and taxanebased chemotherapy: correlative analysis of C9741. Breast Cancer. 2016; 2(15023).
 30
HaibeKains B, Desmedt C, Loi S, Culhane AC, Bontempi G, et al.A threegene model to robustly identify breast cancer molecular subtypes. J Natl Cancer Inst. 2012; 104(4):311–25.
 31
Gendoo DM, Ratanasirigulchai N, Schroder MS, Pare L, Parker JS, et al.Genefu: an R/Bioconductor package for computation of gene expressionbased signatures in breast cancer. Bioinformatics. 2016; 32(7):1097–9.
 32
Qiu X, Wu H, Hu R. The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis. BMC Bioinformatics. 2013; 14(124).
 33
Massague J. TGFbeta in cancer. Cell. 2008; 134(2):215–30.
 34
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995; 57(1):289–300.
 35
Curtis C, Shah S, Chin S, Turashvili G, Rueda O, et al.The genomic and transcriptomic architecture of 2000 breast tumours reveals novel subgroups. Nature. 2012; 486:346–52.
 36
Zhang J, Huang K. Pancancer analysis of frequent DNA comethylation patterns reveals consistent epigenetic landscape changes in multiple cancers. BMC Genomics. 2017; 18(1045).
 37
Wang F, Xu H, Zhao H, Gelernter J, Zhang H. DNA comethylation modules in postmortem prefrontal cortex tissues of European Australians with alcohol use disorders. Sci Rep. 2016; 6(19430).
 38
de Leeuw CA, Neale BM, Heskes T, Posthuma D. The statistical properties of geneset analysis. Nat Rev Genet. 2016; 17(6):353–64.
 39
Mishra PP, Medlar A, Holm L, Toronen P. Robust multigroup gene set analysis with few replicates. BMC Bioinformatics. 2016; 17(526).
Funding
Support has been provided in part by the Arkansas Biosciences Institute, the major research component of the Arkansas Tobacco Settlement Proceeds Act of 2000.
Availability of data and materials
The TCGA data for breast cancer can be downloaded from Genomic Data Commons (https://gdc.cancer.gov). The METABRIC data (normalized gene expression data) can be downloaded from http://www.cbioportal.org/study?id=brca_metabric#summary. The KEGG pathways and MSigDB hallmark gene sets can be found at Gene Set Enrichment Analysis (http://software.broadinstitute.org/gsea/index.jsp).
Author information
Affiliations
Contributions
The author read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable
Competing interests
The author has declared that no competing interests exist.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Zhang, Q. A powerful nonparametric method for detecting differentially coexpressed genes: distance correlation screening and edgecount test. BMC Syst Biol 12, 58 (2018). https://doi.org/10.1186/s129180180582x
Received:
Accepted:
Published:
Keywords
 Distance correlation
 Edgecount test
 Differential coexpression
 Breast cancer subtypes
 Pathway analysis
 The cancer genome atlas