C-element: A New Clustering Algorithm to Find High Quality Functional Modules in PPI Networks

Abstract

Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithm's result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.

Introduction

Graph clustering of PPI networks is one of the most common techniques for inferring functional modules [1]–[5]. Although there is no widely accepted formal definition of a functional module, it is commonly conceived as a group of proteins that work together to carry out a cellular process while binding to each other in different times and places [6].Various graph clustering approaches have been developed in order to discover sets of densely connected vertices within a graph. Graph clustering approaches can be categorized into several categories such as graph partitioning, hierarchical clustering, partitional clustering, spectral clustering and modularity optimization based clustering [7]. Most clustering algorithms with the purpose of finding functional modules in bioinformatics, try to find either highly dense sub graphs based on finding cliques or in a greedy manner grow clusters starting from vertices with high degrees as seeds. A clique is a complete graph in which all the vertices are directly connected to each other. Since PPI networks are typically sparse [8], clique based methods only can find small number of clusters that only cover scant number of proteins and omit lots of proteins in PPI network. For example in our analysis CFinder [9] clusters only 596 proteins out of 2305 in Yeast PPI network, 826 proteins out of 3726 in Human PPI network and 28 proteins out of 305 in C.elegans PPI network [10], [11]. In this paper, we present a new algorithm in order to find functional modules in PPI networks utilizing the advantages of these both categories. The algorithm consists of two main parts. The main goal of the first part is to determine the best seeds by finding and removing the special proteins that might belong to the several clusters and consequently to find the best clusters. The main idea of the second part is to take the profits of clique-based clustering algorithms. To demonstrate the ability of the new algorithm to find the functional modules, we set up our experiments using GO analyses on the high throughput PPI networks such as Saccharomyces Cerevisiae, Homo sapiens and C.elegans and also on tissue specific PPI networks. We use GO Biological Process (BP) and GO Cellular Component (CC) derived functional modules to evaluate the power of the new algorithm and some other clustering algorithms in finding functional modules in PPI networks. The GO analyses are based on the framework introduced by Song and Singh [12]. We also analyze the effect of using tissue specific networks on the quality of different algorithms' results.

Background and related work

There are lots of graph clustering algorithms proposed up to now. Here, we briefly explain those methods whose focuses are on finding functional modules in PPI networks. Some of the algorithms such as CFinder [9] try to find clique like sub networks. These algorithms can only cluster a small number of vertices in the PPI networks because PPI networks are completely sparse [8]. In 2006, Adamcsek et al introduced CFinder [9] as a tool to find highly inter connected area in graphs based on Clique Percolation Method (CPM) [13]. CFinder finds all k-cliques that are defined as complete sub-graphs with k vertices (k≥3), and then it merges k-cliques if they share exactly k-1 vertices. The other algorithms such as SPICi [14] and clusterONE [15] optimistically suppose that the clusters in PPI networks are located around the vertices with high degrees. These algorithms make clusters starting from these vertices as seeds. Jiang et al.in 2010 presented SPICi, a runtime and memory efficient clustering algorithm. The spice is a greedy, runtime and memory efficient clustering algorithm that makes cluster, starting from vertices with high degrees (hubs) as seeds and grows them locally in order to optimize cluster's density. ClusterONE is a clustering algorithm that generates overlapping clusters by growing clusters with high cohesiveness from selected seed vertices. This algorithm is another seed-based clustering algorithm that was presented by Nepusz et al. in 2012. MCODE is a popular clustering algorithm to detect densely connected regions in large PPI networks [1]. MCODE was introduced by Bader et al. in 2003 and it entails three main steps, 1) vertex weighting: MCODE weights all vertices based on their local network density, 2) complex prediction: starts from a vertex with the highest weight as a seed and grows clusters by iteratively adding seed's neighbors whose weight is above a given threshold. When no more vertex can be added to current cluster, MCODE repeatedly starts from the next highest unseen weighted vertex in the network, and 3) post processing to filter or add proteins in the resulting complexes. Step Three is optional. Corban et al. in 2010 introduced NeMo as a new clustering algorithm [16]. NeMo computes a log-odds score [17], [18] of shared neighbors for all pairs of vertices and then uses hierarchical agglomerative clustering using either single-linkage or complete-linkage clustering to make final clusters. In 2011, Rhrissorrakrai et al. presented MINE [19] whose strategy is similar to that of MCODE. In [19] Authors argued that MCODE gives a good result on the Yeast PPI network, but it does not produce a good clustering result on C. elegans PPI network. The primary difference between MINE and MCODE is their weighting functions. MINE also uses modularity optimization strategy. For each vertex v, MINE assigns weight that is the product of v's clustering coefficient [20] and the number of edges (k) of the most highly connected node in the local neighborhood of v. This weighting function assigns higher weight to those vertices that are connected to a vertex with high degree (hubs). Enright et al introduced MCL in 2002 [21]. MCL is based on the idea that if a random walker starts from a vertex and randomly chooses an edge to continue its traveling; it is more probable that it stays within a cluster than travels to another one. This happens simply because there will be more edges within a cluster than edges between clusters and then most likely an inside edge will be chosen. The focus of these briefly described algorithms is on finding functional modules in PPI networks. There are also some other graph clustering algorithms. Fortunato et al. [22] argued that algorithms based on optimizing modularity suffer from a resolution limit. Modularity is a quantitative measure that was originally defined by Newman and Girvan [23], to assess the quality of graph clustering results. Based on this measure, a clustering algorithm gets high modularity if it finds clusters with dense connections between the each cluster's inside and gets low modularity scpre with sparse connections. Resolution limit means small scale clusters or clusters with high density may be merged into a single larger community. To solve this problem Ruan el al. in 2008, introduced two new algorithms QCUT and HQCUT [24]. QCUT is a heuristic algorithm based on optimizing modularity. QCUT consist of two steps. First it uses spectral graph partitioning to divide the input graph and next attempts to merge or change the result of previous step in order to optimize modularity. To solve the resolution limit problem Ruan el al. suggested recursively applying QCUT on subnetworks that are found with QCUT while ignoring all the inter clusters edges. They called this procedure HQCUT. FAG_EC [25] is a Hierarchical agglomerative algorithm that was introduced by Li et al. in 2008. FAG_EC identifies modules based on edge weighting function, (called) edge clustering coefficients. It sorts all edges based on their edge clustering coefficients descending. Repeatedly choose an edge with the highest weight and decides whether or not the two edge's endpoints are in the same cluster or not.

In the rest of this paper, we present a new algorithm called C_element and the extended version of this algorithm (C_element_extended) to find functional modules in PPI networks. The main idea of this algorithm is to improve the quality of obtained clusters utilizing the benefits of both clique-based and seed-based algorithms.

Materials and Methods

In this section, we first briefly explain some basic concepts and primary definitions related to the proposed algorithm and then we explain the algorithm in more details.

Preliminaries

A graph is a representation of a set of objects where some pair of these objects is connected via a link. Therefore, a graph G (V, E) consists of two sets V and E where V is the vertex set and E is the edge set [26]. PPI networks can be modeled as a graph. Given a PPI network, the goal of the proposed algorithm is to find internally high connected sub graphs (clusters) in order to extract functional modules in PPI networks. In this paper, networks are considered as an un-weighted and undirected graph.

The proposed algorithm

The proposed algorithm consists of two main parts. The main goal of the first part is to find the best seeds by finding and removing the special proteins that might belong to the several clusters and consequently to find the best clusters. We expect that the result of this part outperforms the results of the seed-based algorithms such as SPICi and clusterOne. The main idea of the second part is to take also the profits of clique based clustering algorithms as a complementary solution. In the rest of this section, we explain each part in more detail. Figure 1 shows an overview of the proposed algorithm.

Figure 1. An overview of the proposed algorithm; the algorithm consists of two main parts.

The First part of the algorithm: C_element

In biology a protein segment is called an active site if it plays a key role in the catalytic action of the enzyme function and substrates bind and undergo a chemical reaction. Csermely proposed that the concept of active sites can be extended to networks other than protein structures [27]. He mentioned that the active sites of networks (for example PPI networks ) at higher levels than proteins are not only central elements with a specific set of properties and with ability to monitor all the communications of the entire network, but they are also randomly scattered over the whole network while connecting distant modules. Active sites have weak connections to important vertices (often known as hubs) in the network. Csermely proposed the term creative elements for the vertices that are located in active sites. A sample network with two creative elements is shown in Figure 2. This network is clipped from Csermely's original paper [27].

Figure 2. Two red vertices A and A' are creative elements and green vertices are hubs. The original network can be found in [27].

Our main idea is that by discovering and removing creative elements (vertices) from the network, the modules in that network would appear clearly.

Using Centrality measures is an idea to find Creative elements. Centrality measures are functions that assign numerical value to each vertex in the network in order to find more important vertices. Based on these values, the vertices in the network can be ranked. Based on these measures, the more influential vertices are those get higher scores. In order to assay the power of Centrality measures to find Creative elements we simply applied some of these measures on the sample network represented in Figure 2 and also on Zachary's well-known “karate club” network [28]. The karate club network is a standard benchmark in community detection algorithms [7]. The karate club network is visualized in Figure 3. Our main reason to choose this social network rather than any other biological networks is its popularity and being well studied in researches on graph clustering. Unfortunately, there is not any standard biological network benchmark on graph clustering. The karate club network consists of 34 vertices and 78 edges. The vertices in that network are labeled from 1 to 34. Two main clusters in karate club are shown in different colors in Figure 3. Table 1 briefly shows the Centrality measures that we used to analyze the both mentioned networks.

Figure 3. Zachary's karate club network; two main clusters indicates by different colors.

Table 1. The Centrality Measures that used to find Creative elements.

Measure	Equation/Definition	Reference
Degree centrality		Koschützk et al. [29]
Leverage centrality		Joyce et al. [30]
Local leader		Blondel et al. [31]
Strict leader		Blondel et al. [31]
Closeness		Sabidussi[32]
Eccentricity		Hage et al. [33]
Radiality		Valente el al. [34]
Shortest-path betweenness		Freeman [35]
PageRank		Page et al. [36]
Eigenvector		Bonacich[37]
Power		Bonacich[37]
Cluatering coefficient		Watts et al. [20]
K-step markov		White et al. [38]

We applied all the measures in Table 1 on the both networks. Table 2 briefly shows the obtained results. We simply show some top-ranked vertices based on each measure.

Table 2. The top-ranked vertices in two networks based on different centrality measures.

Measure	The top-ranked vertices in the network from Figure 2 (A)	The top-ranked vertices in karate club
Degree centrality	b1, b3, b4	34, 1, 33, 3, 2, 32, 4, 14, 24, 9
Leverage centrality	b2, b3, 9	7, 30, 33, 32, 3, 1, 28, 6
Local leader	b1, b2, b3, b4	34, 1
Strict leader	b1, b2, b3, b4	34, 1
Closeness	b1, b3, r1	1, 3, 34, 32, 33, 14, 9, 20
Eccentricity	b1, b3, r1	1, 2, 3, 4, 9, 14, 20, 39
Radiality	b1, b3, r1	1, 3, 34, 32, 9, 14, 33, 20
Shortest-path betweenness	b1, b3, r1	1, 34, 33, 3, 32, 9, 2, 14
PageRank	b1, b3, b4	34, 1, 3, 33, 2, 9, 14, 4
Eigenvector	b1, r1, 5	34, 1, 3, 33, 2, 9, 14, 4
Power	b1, b4, b2	34, 1, 25, 26, 17, 33, 2, 12
Clustering coefficient	15, 16, 22, 23	19, 17, 18, 15, 16, 13, 21, 22, 23, 27, 8
K-step markov	b1, r1, 14	1, 34, 33, 3, 2, 32, 4, 14, 9

After analyzing Table 2, we realized that all those retrieved vertices have somehow important role in corresponding networks. For example, the main hubs of both networks are given the highest scores by measures Local leader, Strict leader and Degree centrality as well. The results of each measure on mentioned networks are available as supplementary data attached to this paper (File S1 and File S2) or following link: http://lbb.ut.ac.ir/Download/LBBsoft/C-elemnts-Clustering/.

Although, these measures marked some vertices in the network as important vertices that might have a key role in the network, none of them could fulfill our goal to find the creative elements vertices ( and ). In order to find these creative elements we propose equation (1).

(1)

Where belongs to a set of vertices whose neighbor set contains at least one hub. Here hubs refer to vertices with high degree. We consider 20% top vertices based on their degrees as hub. If a vertex has several hubs in its neighborhood and its degree is low, it will get a high score. The higher score each vertex gets, the more creative it is. Equation (1) can be considered as a new Centrality measure.

We applied equation (1) on the network in Figure 2 in order to check its ability to find Creative elements. Figure 4 (A) is visualized the results. The three vertices with the highest Centrality scores are colored in green. As it can be realized clearly, two creative elements mentioned in Figure 2 are among these three green vertices. We also applied equation (1) on karate club. Green vertices in Figure 4 (B) are more creative vertices based on equation (1). We realized that many of these vertices such as 3, 14, 20, 31 and 9 are located on the border between two main clusters; that means these vertices have connections to other vertices in two main clusters. This fact is used in the first part of the new algorithm in order to find a good seed set and consequently to find good clusters in PPI networks. The pseudo code of the first part of the algorithm is presented in Figure 5.

• At the beginning of the algorithm, there is a queue of big clusters (big_clusters_queue) waiting to analyze and also there is an empty set of founded clusters (cluster_set). Initially, all the vertices in the network in hand make the cluster at the top level of hierarchy. This means algorithm starts with big_clusters_queue with one member, the whole input network.

• One of the sub-networks in big_clusters_queue is chosen and is removed from the queue. Creative elements in this sub-network will be found and will be removed in order to separate existed clusters in that sub-network. These vertices are supposed to be located on the borders between clusters. After removing the Creative elements and all the Interactions between these elements and any other vertices in the sub-network, the remaining vertices and interactions in the sub-network might make an unconnected network. In this step all the unconnected components of the leftover sub-network is obtained. These components are a collection of some singleton vertices or groups of connected vertices that are made C_elements beginning seeds. Then the algorithm expands these seeds to make the clusters using the Expand function. There is not an assumption about the number of clusters that must be found in each step. ­After removing the Creative elements, if the leftover sub-network remains connected, it is not clusterable and it is one of the clusters at the lower level of the hierarchy. This means this sub-network do not have any sub-clusters. In this situation C_element adds this sub-network to cluster_set and continues processing another subnetwork in the big_clusters_queue.

• The Expand function assigns the removed creative elements to one of the obtained seeds in a greedy manner. This seed is one that maximizes the equation (2).

  (2)

  Where is one of the creative elements and is a founded seed of the previous step. For each removed creative element , the Expand function search for the seed with maximum value of among all the obtained seeds that are found in the previous step. Based on this equation, a vertex assign to a growing cluster in which its most neighbors located. All the resulting clusters are added to big_clusters_queue for further analyses.

• This process continues until there is not any other subnetwork in the big_clusters_queue. In that case the final clusters are in cluster_set.

Figure 4. Green vertices are the most creative elements realized by equation (1) in a network which is illustrated Figure 2 (A) and in Zachary's karate club network (B).

Figure 5. The pseudocode of the first part of the proposed algorithm.

The second part of the algorithm

In the second part of the algorithm the advantages of clique based clustering algorithms are utilized. The pseudocode of the second part of the algorithm is presented in Figure 6. In the second part, the algorithm finds all maximal cliques with size≥3 in the original input PPI network. A clique is called a maximal clique if it is not part of a bigger clique [6].The algorithm merges each C_element's already obtained cluster with n vertices and a maximal clique into one new cluster if they have n-1vertices in common. If a cluster () and a maximal clique are merged, they make a new larger cluster. Then is replaced by that larger cluster. By using this post-processing step, some small C_element's output clusters that have low quality are merged with a maximal clique and the overall quality of the obtained clusters is outperformed. Using this post-processing step, the number of C_element's final clusters might decrease. We refer to the resulting final clusters at the end of this part as C_element_extended.

Figure 6. The pseudo code of the second part of the proposed algorithm.

Our new algorithm, C_element, belongs to Hierarchical Divisive algorithm category. Hierarchical clustering approach can be classified in two categories: 1) Agglomerative algorithms: this is a bottom-up approach; at the first step starts with singleton clusters which each vertex makes a cluster with only one vertex belong to and iteratively clusters at the higher level of hierarchy are merged if their similarity is high enough; 2) Divisive algorithms: this is a top-down approach; at the first step all vertices belong to one big cluster, and recursively clusters at the higher level of hierarchy are split and make the next level. One of the advantages of Hierarchical clustering approach is that it does not require a preliminary knowledge of the number and size of the clusters [7].

Datasets

We applied the new algorithm to high throughput PPI networks from Sacchromyces cerevisiae (Yeast), Homo sapiens (Human) and C.elegans (Worm) that are obtained by Patil and Nakamura [10], [11]. They categorized the interactions into three categories: High confidence small-scale binary interactions, High confidence interactions based on reliability score and Low confidence interactions based on reliability score. All these interactions are available online on the project's website [39]. In this paper, we use high confidence small-scale binary interactions for each mentioned species as their corresponding PPI networks.

Sacchromyces cerevisiae PPI network consists of 2324 proteins connected via 4376 interactions, Homo sapiens PPI network consists of 3989 proteins connected via 7465 interactions and C.elegans PPI network consists of 379 proteins connected via 385 interactions.

We also applied our algorithm to tissue specific human PPI networks [40]. These tissue specific networks consist of 60 PPI networks for 60 specific tissues in the human body. In this paper we show the results of applying different algorithms on B cells, Bone marrow CD34 and Monocytes PPI networks. The B cells PPI network consists of 942 proteins connected via 2026 interactions, Bone marrow CD34 PPI network consists of 1669 proteins connected via 4552 interactions and Monocytes PPI network consists of 1549 proteins connected via 4025 interactions.

GO analysis

In order to evaluate the power of our clustering algorithm in finding functional modules in PPI networks, we utilize the framework which is described by Song and Singh [12]. This framework quantifies how well computationally derived clusters in physical interactomes correspond to functional modules derived via the Gene Ontology (GO) [41]. Each GO term generates a functional module that means all the proteins that are annotated with a same GO term correspond to same functional modules.

Song and Singh [12] utilized the following three formulas to quantify the overlap between computationally derived clusters and GO derived functional modules:

1) Jaccard measure:

For each computationally derived cluster jaccard similarity coefficient is defined as the maximum value over all the functional modules that are extracted by considering different GO terms A. between cluster and module is defined as the size of the intersection over the size of the union. Equation (3) shows this measure.

(3)

2) Precision–Recall measure:

For each cluster , its Precision–Recall (PR) value with a GO derived functional module is computed using equation (4). The final PR measure for , is the maximum PR value over all s.

(4)

3) Semantic density measure:

This measure measures the average semantic similarity between each pair of annotated proteins within a cluster and can be calculated using equation (5).

(5)

Where S is a cluster, and are two proteins belong to S, A is a set of all GO derived functional modules and is a weight function that assigns a weight to a pair of proteins and in the range of 0–1. is calculated using equation (6).

(6)

Where shows how specific the annotation is and is the fraction of the total number of proteins in the considered network that have annotation .

Each of these three measures varies from 0 to 1. The higher values for each clustering result mean the better agreement of the clusters on GO derived functional modules. Further information on GO analyses can be found on [12].

Results and Discussion

Go analysis

We used the GO biological process (BP) and cellular component (CC) terms to extract the functional modules. Molecular function GO terms (MF) were not used because proteins annotated with the same GO MF terms do not necessarily interact with each other as a functional unit [12].We considered the GO BP and GO CC ontologies separately. For each cluster, we separately calculated the three measures introduced in the previous section for both ontologies. For Genes in singleton clusters, we assigned Jaccard, PR and semantic similarity values of 0. Finally, each cluster was weighted by its size, and the final values for each of the six measures (three BP and three CC), was computed using the weighted average over all clusters in the computational clustering result. We compared our algorithm with 5 other popular clustering algorithms. Some clustering algorithms like MCODE [1] finds small number of very good clusters and omits from consideration lots of vertices. For example in our analyses, in Yeast PPI network, all number of vertices was 2305 and MCODE only clustered 244 proteins out of 2324 proteins and left 2061 proteins without assigning them to any clusters. Also in our experiments quality of some clustering algorithms such as FAG-EC [25], QCUT and HQCUT [24] was related to the input network. For example in our analyses QCUT and HQCUT found good resulting clusters on Homo sapiens, but they could not find any clusters on C.elegans and Yeast PPI networks. Therefore, in our analyses we focused on clusterONE (2012) [15], SPICi (2010) [14], NEMO (2010) [16]; CFinder (2006) [9] and MINE (2011) [19] that showed stable behavior on different networks. Implementations of each of these algorithms are available either on Cytoscape website [42] or on each related project's website.

Jaccard measure analyses

Figure 7 and Figure 8 show the results of Jaccard measure analyses on the results of different algorithms when GO CC derived functional modules and GO BP derived functional modules were considered as the reference set of known modules respectively.

Figure 7. The results of Jaccard measure analyses on the results of different algorithms on different network datasets when GO CC derived functional modules were considered.

Figure 8. Jaccard measure analyses results on the different network datasets and different algorithms when GO BP derived functional modules were considered.

As it can be inferred from the corresponding diagrams in Figure 7 when GO CC derived functional modules were considered and based on Jaccard measure analyses, the first part of the proposed algorithm (C_element) outperformed other algorithms in all three high throughput PPI networks and found that the GO CC derived functional modules were better than the other algorithms. Especially the result of the new algorithm on C.elegans PPI network was considerable. C_elemnt also acted well on tissue specific networks. Bone marrow was the only network on which clusterOne got higher Jaccard scores than C_element. Generally, the clusterONE clustering algorithm also found good clusters and surpassed the other four algorithms in most cases. The only case in which the SPICi clustering algorithm got better results than ClusterONE was on C.elegans PPI network, and in all the other cases our algorithm and ClusterONE seemed to have foundbetter GO CC derived functional modules than the other algorithms. After merging the clustering results of C_element with maximal cliques, the quality of the clusters was surprisingly improved and C_element_extended all the other algorithms.

Precision–Recall analyses

Figure 9 and Figure 10 show Precision–Recall measure analyses results on the results of different algorithms when GO CC derived functional modules and GO BP derived functional modules were considered as the reference set of known modules respectively. Our Precision–Recall measure analyses verified the obtained results by Jaccard measure. As it can be realized clearly from the corresponding diagrams, the success of our algorithms (both C_element and C_element_extended) to find GO derived functional modules in C.elegans PPI network and also on B cells PPI network was undeniable in comparison to other algorithms. In Yeast and Human PPI networks by considering GO BP derived functional modules there was a close competition between the first part of the proposed algorithm and clusterONE; that means on some input networks such as Bone Marrow CD34 clusterONE outperformed C_elementand on some input network datasets such as C.eleganse, B Cells and Monocytes C_element outperformed clusterONE. In all cases C_element_extended outperformed other algorithms. In summary, Precision–Recall measure analysis on B cells, Bone marrow CD34 and Monocytes tissue specific PPI networks confirmed previous Jaccard analyses. MINE and SPICi showed good results in finding BP derived functional modules and outperformed clusterONE and C-element, but not C_element_extended.

Figure 9. The results of Precision–Recall measure analyses on the results of different algorithms on different network datasets when GO CC derived functional modules were considered.

Figure 10. The Precision–Recall measure analyses results on the results of different algorithms on the six different network datasets when GO CC derived functional modules were considered.

Semantic density analyses

In the last part of our GO analyses, we compared different algorithm based on semantic density measure. Figure 11 and Figure 12 show the resulting diagrams based on this measure.

Figure 11. The results of semantic density measure analyses on the results of different algorithms on the six network datasets when GO CC derived functional modules were considered.

Figure 12. Semantic density measure analyses results on the six different networks and different algorithm considering GO BP derived functional modules.

This analysis confirmed our previous two analyses. In C.eleganse PPI network C_element's and its extended version's results were appreciable, and on two other networks (Yeast and Human) C_element found GO CC derived functional modules better than clusterONE and by considering GO BP generated functional modules clusterONE outperformed C_element. In all cases C_element_extended outperformed other algorithms. Based on our semantic density analyses, C_element_extended also surpassed other algorithms in finding both GO BP and GO CC derived functional modules on B cells, Bone marrow CD34 and Monocytes tissue specific PPI networks.

Analyze the effect of using tissue specific networks

The bars illustrated in Figure 7 through Figure 12 also can be used to compare the ability of different algorithms on the tissue specific network datasets to find GO CC derived functional modules and GO BP derived functional modules respectively. Our main goal to use the tissue specific networks was to improve clustering results on Homo sapiens PPI network. As it can be inferred from the corresponding bars in 7 through Figure 12, almost all algorithms had a low quality clustering results on Homo sapiens PPI network. Using the tissue specific networks, generally all the results of the different algorithms got improved. For example the improvement for MINE final results was significant. As it can be inferred from corresponding diagrams in Figure 7 through Figure 12, MINE had low quality results on Homo sapiens, but the algorithm's results on the other three tissue specific networks were much better. The improvements on the results of NeMo and SPICi clustering algorithms are also considerable. We can conclude tissue specific networks of human are more accurate and more reliable than the Homo sapiens PPI network. The proposed algorithm seemed to have a stable behavior on all six networks. The algorithm found good GO CC and GO BP derived functional modules in all cases and did not poorly act on any input network.

Conclusions

In this paper, we focused on finding the high quality functional modules in PPI networks. To fulfill this goal, we have proposed a new clustering algorithm. The new algorithm consists of two main parts. In order to evaluate these two parts, we set up our experiments on the clustering results of each part separately. The GO analysis results indicated that the quality of the obtained clusters at the end of the first part of the algorithm (C_element) was always much better than SPICi and also there was a close competition between the results of the first part of the algorithm and those of clusterONE. Based on these analyses we concluded that the obtained seeds were much better than choosing the hubs of the network as the seeds. The first part of the algorithm performed better than all the other algorithms when finding GO CC derived functional modules were considered. The analyses on the final clusters at the end of the second part of the new algorithm (C_element_extended) showed that the quality of the obtained clusters was significantly better than the quality of the results of the other algorithms on the input datasets. Our analyses also indicated that, generally all the algorithms could find better clusters using tissue specific networks. For example, some algorithms such as MINE, NeMo and SPICi that poorly acted on Yeast, C.elegans and Homo sapiens PPI networks found considerably better clusters on the tissue specific networks. Generally the new algorithms had a stable behavior on all the input network datasets and did not poorly act on any of them. Therefore, we can conclude that this better performance is not only the result of using tissue specific networks but also the new algorithm plays a part in this performance.

Supporting Information

File S1

Contains the sample network from Csermely's original paper and the results of each measure on this network as separate TXT files.

(RAR)

File S2

Contains “karate club” network and the results of each measure on this network as separate TXT files.

(RAR)

Funding Statement

The authors have no support or funding to report.