The guilt-by-association principle

The guilt-by-association (GBA) principle states that genes that interact are more likely to share biological function. For example, two genes seen to interact in a synthetic genetic array (SGA) are more likely to encode products involved in the same pathway or complex than two genes not seen to interact. Knet uses this principle to measure the strength of association between a network and a phenotype. By measuring the clustering of the gene set across biological networks it becomes possible to identify the network that best explains the mechanisms and processes that underlie the set. This principle is also used by Knode to prioritise genes for follow up studies. If a set of genes has been identified as being associated with a particular cellular function, then the GBA principle states that genes seen interacting with this set are also likely to be involved in the function.

The Knet function

The Knet function measures the strength of association between a network and a phenotype by quantifying the clustering of high-weight vertices on the network. High-weight vertices represent those genes that are part of a gene set or are most strongly linked with the phenotype. The GBA principle implies that the stronger the clustering, the better the network is at explaining the mechanisms that produce and maintain the phenotype.

The function is based upon Ripley’s K-statistic, a tool from spatial statistics that measures the clustering of points on a plane. Ripley’s K-statistic works by counting the number of points contained within circles of radius \(s\) positioned around each point. The value of \(s\) is increased and the area under the resulting curve (AUK) is measured. If points cluster, then they will be engulfed within the circles quicker, the function will increase faster and the AUK will be larger. By swapping the Euclidean distance across the plane with a measure of distance across the network, this statistic can be adapted for networks. We call this function the Knet function.

Unlike Ripley’s K-statistic, the Knet function can measure the clustering of a set of vertices or a continuous distribution of vertex weights. If biological networks are being used, then a high weight would indicate that a gene is strongly linked with the phenotype being studied. In some scenarios, weights may not be available for all vertices. Vertices with missing weights can be included within the network. However, they should be given a weight of `NA} to ensure that their distribution does not affect the significance analysis.

The significance of clustering is quantified by permuting the vertex weights across the networks. Vertices with missing weights are excluded. The area under the Knet function for the observed set is then compared to the area under the Knet function for the permuted sets and a z-test is used to compute a p-value. The p-values produced represents the probability that a clustering of vertex weights at least as strong as that observed occurs given that the vertex weights are distributed randomly across the network.

The Knet function is:

\[ K^{net}(s) = \frac{2}{(\bar{p} n)^{2}}\sum_{i}p_{i}\sum_{j}(p_{j} - \bar{p})~\textbf{I}(d^{g}(i,j)\leq s) \]

where \(p_i\) is the phenotype observed at vertex \(i\), \(\textbf{I}(d^{g}(i,j)\leq s)\) is an identity function that equals \(1\) if the distance between vertex \(i\) and \(j\) is less than \(s\) and 0 otherwise, and \(\bar{p} = \frac{1}{n}\sum_{i=1}^n p_i\).

The Knode function

Taking the inner sum of the Knet equation makes it possible to rank vertices by their distance from high-weight vertices. We call this function the Knode function. This function provides a natural way to prioritise genes for follow up studies.

The Knode function is:

\[ K_{i}^{node}(s) = \frac{2}{p}\sum_{j}(p_{j} - \bar{p})~\textbf{I}(d^{g}(i,j)\leq s) \]

Measuring the distance between pairs of vertices

SANTA contains three methods for measuring the distance between pairs of vertices. Each method incorporates different aspects of the network.

• Shortest paths. The distance along the shortest path connecting two vertices. In a graph without weighted edges, the length of the shortest path is equal to the number of edges in the path. In a graph with weighted edges, the length of the shortest path is equal to the sum of the edge weights in the path. This is the simplest measure of distance and is used by default by the SANTA package.
• Diffusion kernel-based method. The diffusion kernel-based distance measure can be considered as the physical process of diffusion though the graph. Therefore, unlike the shortest paths method, the diffusion kernel-based method incorporates not only the distance along the shortest path connecting two vertices, but the distances along multiple paths. It therefore incorporates more of the network structure.
• Mean first-passage time-based (MFPT) method. The MFPT distance measure computes the distance between vertex \(i\) and \(j\) (\(m_{i,j}\)) by computing the expected amount of time that a random walk emanating from node \(i\) takes to reach node \(j\) for the first time. Much like the diffusion kernel-based method, the MFPT method incorporates the number of paths connecting a pair of vertices.

i) Knet successfully identifies clustering on simulated networks

In this section, we will use simulated networks to demonstrate that the Knet-function successfully quantifies the clustering of hits on a network.

In order to demonstrate how the Knet function can be used to measure the clustering of hits on a network, we will simulate a network, apply clusters of hits of different clustering strengths to the network, and use the Knet function to quantify the strength of clustering.

We will use the Barabasi-Albert model of preferential attachment to simulate the networks, as it has been observed that this methods produced networks with attributes similar to many real-world networks. A network of 500 vertices is both large enough to contain clusters of hits of different strengths and small enough to quickly run a large number of trials upon. In the Barabasi-Albert model, vertices are added stepwise and connected to previously-added vertices. This is done under a probability distribution based upon the degree of each vertex. If \(power=1\), then the relationship between the probability distribution and vertex degree is linear. We will use this value, as it creates a network with multiple high-degree hubs, much like many biological networks. Each new vertex is connected to \(m\) previously-added vertices. In order to create a moderately-dense network, we will set \(m=1\).

# generate the simulated network
require(SANTA, quietly=TRUE)

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
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require(igraph, quietly=TRUE)
set.seed(1) # for reproducibility
g <- barabasi.game(n=500, power=1, m=1, directed=FALSE) 

A cluster of hits is simulated by first randomly choosing a seed vertex. All other vertices within the network are then ranked according to their distance to the from the seed vertex. The distance can be measured using any of the previously described distance metrics (shortest paths, diffusion kernel and MFPT). In this example, we will use the shortest paths metric, as it is the quickest to run. However, running the analysis with the other methods will produce the same results.

# measure the distance between pairs of vertices in g
dist.method <- "shortest.paths"
D <- DistGraph(g, dist.method=dist.method, verbose=FALSE) 

The Knet function computes the distances between each pair of vertices in \(g\). However, to reduce the time required to run the function, these distances can be computed outside the function. The Knet function cannot be run on the raw continuous distances. Therefore, it is necessary to place the pairs of vertices into discreet bins. This is done using the `BinGraph} function.

# place the distances into discreet bins
B <- BinGraph(D, verbose=FALSE)

Next, we will generate clusters of hits and measure the clustering strength using the Knet function. The \(s\) vertices positioned closest to the seed vertex form the sample set. From this set, 5 vertices are randomly selected to form the hit set. The size of the hit set needs to be small compared to the number of vertices in the network. Increasing the value of \(s\) decreases the strength of the clustering. We will test 5 different values of \(s\) in order to simulate different clustering strengths. A value of \(s\) equal to the total number of vertices in the network produces a hit set containing vertices randomly selected from the entire network. For each value of \(s\), we will conduct 10 trials. In the associated paper, 1000 trials were run. Here we will also use 100 permutations, rather than 1000, in order to reduce the time required.

cluster.size <- 5
s.to.use <- c(10, 20, 50, 100, 500)
n.trials <- 10
pvalues <- array(0, dim=c(n.trials, length(s.to.use)), 
    dimnames=list(NULL, as.character(s.to.use)))

# run the trials for each value of s
for (s in s.to.use) {       
    for (i in 1:n.trials) {
        # generate the hit set
        seed.vertex <- sample(vcount(g), 1) # identify seed
        sample.set <- order(D[seed.vertex, ])[1:s]
        hit.set <- sample(sample.set, cluster.size)
        
        # measure the stength of association
        g <- set.vertex.attribute(g, name="hits", 
            value=as.numeric(1:vcount(g) %in% hit.set))
        pvalues[i, as.character(s)] <- Knet(g, nperm=100, 
            dist.method=dist.method, vertex.attr="hits", 
            B=B, verbose=FALSE)$pval
    }
}

Figure 1 contains the results of the simulation study. The Knet function correctly identifies sets of hits created using smaller values of \(s\) as clustering more significantly.

ii) Comparing Knet to Compactness

Nexy we will demonstrate the effectiveness of the Knet function by comparing it to a simpler metric.

Glaab et al. define the compactness score as the mean distance between pairs of hits in a network. By permuting the vertices belonging to the hit set and comparing the observed compactness score to compactness score of the permuted sets, an empirical p-value can be produced. This is much like Knet. However, unlike Knet~, the adapted compactness score cannot be applied to continuous distributions of vertex weights and does not incorporate vertex degree when measuring the significance of association. This is demonstrated in this toy example.

First, we create the network. The vertices in this network have a range of degrees, allowing us to demonstrate the differences between Knet and Compactness. We will apply 2 sets of vertex weights, each of which will contain two interacting hits.

# create the network
n.nodes <- 12
edges <- c(1,2, 1,3, 1,4, 2,3, 2,4, 3,4, 1,5, 5,6, 
           2,7, 7,8, 4,9, 9,10, 3,11, 11,12)
weights1 <- weights2 <- rep(0, n.nodes)
weights1[c(1,2)] <- 1
weights2[c(5,6)] <- 1 

g <- graph.empty(n.nodes, directed=FALSE)
g <- add.edges(g, edges)
g <- set.vertex.attribute(g, "weights1", value=weights1)
g <- set.vertex.attribute(g, "weights2", value=weights2)

Figure 2 shows the location of each set of hits on the network. The distance between the hits in set 1 and set 2 are the same. However, intuitively we expect that the clustering of the hits in set 2 to be more significant, as the hits have fewer interacting partners and are therefore less likely to be observed interacting by chance.

We will now apply Knet and Compactness to each set of hits in order to measure the clustering significance. We will run the function with 100 permutations. Increasing the number of permutations increases the accuracy of the functions. However, it also increases the run time. 1000 permutations were used to create the results shown in the paper. The shortest paths distance measure will be used. However, other methods will produce the same results.

# set 1
Knet(g, nperm=100, vertex.attr="weights1", verbose=FALSE)$pval

## [1] 0.4490129

Compactness(g, nperm=100, vertex.attr="weights1", verbose=FALSE)$pval

## [1] 0.0999465

# set 2
Knet(g, nperm=100, vertex.attr="weights2", verbose=FALSE)$pval

## [1] 0.0004296916

Compactness(g, nperm=100, vertex.attr="weights2", verbose=FALSE)$pval

## [1] 0.09922024

The Compactness function does not incorporate vertex degree when measuring the significance of hit clustering. For this reason, it outputs a similar p-value for each hit set. Knet incorporates the global distribution of vertices and therefore identifies set 2 as clustering more significantly than set 1. It is therefore preferable to use Knet instead of Compactness when the degree of vertices varies across a network.

iii) Using Knet to demonstrate that correlation in GI profile produce functionally more informative networks

In this section we will demonstrate how the Knet function can be used to identify the most effective way of building an interaction network.

There is evidence that protein function is more closely related to global similarity between genetic interaction (GI) profile than to individual interactions. In order to test this, we will use the Knet function to quantify the strength of association between GO terms and two S. cerevisiae networks: a network of raw interactions and a network created from correlations in interaction profile. Both of these will be created using the data produced by Costanzo et al. If the GO terms associate more strongly with a network, then this is indication that the network is more functionally informative.

First, we will load two pre-processed networks, in the form of igraph objects from the SANTA package. SANTA requires all networks to be in the form of igraph objects. The data used to create these networks is available from http://drygin.ccbr.utoronto.ca/~costanzo2009/ and the method used is described in the associated SANTA paper.

Knet is also able to incorporate different edge distances when quantifying the clustering of high-weight vertices. Edge distances can represent different biological properties, such as the strength of a physical interaction between 2 gene products, or in the case of our 2 networks, the strength of the genetic interaction. The smaller the edge distance, the stronger or more significant the interaction. When creating the network, we converted the correlation coefficients into edge distances by taking the \(-log10\) of the scores. These distances have been stored under the edge attribute distance.

# load igraph objects
data(g.costanzo.raw)
data(g.costanzo.cor)
networks <- list(raw=g.costanzo.raw, cor=g.costanzo.cor)
network.names <- names(networks)
network.genes <- V(networks$raw)$name 
# genes identical across networks

Associations between genes and GO terms will be obtained from the Gene Ontology (GO) project using the org.Sc.sgd.db package. In order to reduce the time required to run this analysis, we will use only 5 GO terms. However, it is easy to run this analysis with many more GO terms, as done in the associated paper.

# obtain the GO term associations from org.Sc.sgd.db package
library(org.Sc.sgd.db, quietly=TRUE)

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xx <- as.list(org.Sc.sgdGO2ALLORFS)
go.terms <- c("GO:0000082", "GO:0003682", "GO:0007265", 
              "GO:0040008", "GO:0090329")
# apply the GO terms to the networks
for (name in network.names) {
    for (go.term in go.terms) {
        networks[[name]] <- set.vertex.attribute(
            networks[[name]], name=go.term, 
            value=as.numeric(network.genes %in% xx[[go.term]]))
    }
} 

We will now apply the Knet function to each GO terms on each network in order to quantify the strength of association. If a gene is associated with the GO term it will given a weight of 1. If it is not associated, it will be given a weight of 0. We will now store these scores in the 2 graphs under a vertex attribute called rdds. Due to the time required to compute the distance matrices, the number of permutations has been reduced to 10. Running at least 1000 permutations is recommended.

results <- list()
for (name in network.names) {
    results[[name]] <- Knet(networks[[name]], nperm=10, 
        vertex.attr=go.terms, edge.attr="distance", verbose=FALSE)
    results[[name]] <- sapply(results[[name]], 
        function(res) res$pval)
}

iv) Using Knet to investigate the functional rewiring of the yeast interaction network in response to MMS-treatment

In this section we will demonstrate how the Knet-function can be used to investigate the functional rewiring of a cell by comparing the strength of association of gene sets between 2 networks.

Bandyopadhyay et al. mapped genetic interaction (GI) networks in yeast under normal laboratory conditions and in yeast exposed to the DNA-damaging agent methyl methanesulfonate (MMS). In order to investigate the differences between these functional networks, we will use the Knet-function to quantify the strength of association of a set of genes involved in responding to DNA damage. While we cannot learn much from this example, sets of genes involved in other processes can also be tested in order to identify other processes involved in the response to DNA damage.

We will now load the two interactions networks. These networks were created using correlations in GI profile, as described in the previous section. This is because, as previously demonstrated, using correlations in GI profile as edges results in a functionally more-informative network.

# load igraph objects
data(g.bandyopadhyay.treated)
data(g.bandyopadhyay.untreated)
networks <- list(
    treated=g.bandyopadhyay.treated, 
    untreated=g.bandyopadhyay.untreated
)
network.names <- names(networks)

We will the Gene Ontology (GO) project to identify those genes involved in responding to DNA damage. By changing the code below, it is possible to measure the strength of association of gene sets involved in different functions. In the associated paper, we measured the strength of association of 194 GO terms with each network.

# obtain GO term associations
library(org.Sc.sgd.db, quietly=TRUE)
xx <- as.list(org.Sc.sgdGO2ALLORFS)
# change to use alternative GO terms
associated.genes <- xx[["GO:0006974"]] 
associations <- sapply(networks, function(g) 
    as.numeric(V(g)$name %in% associated.genes), 
    simplify=FALSE)
networks <- sapply(network.names, function(name) 
    set.vertex.attribute(networks[[name]], "rdds", 
    value=associations[[name]]), simplify=FALSE)

We will now apply the Knet function to the GO term on each network in order to measure the strength of association. The greater the number of permutations run, the greater the accuracy of the p-value.

We will also run Knet using the shortest paths method to compute the distance between vertex pairs. However, as shown in the associated paper, the diffusion and mfpt distance measures produce the same results. This can be demonstrated by replacing the dist.method argument below with one of the alternative distance measures. By running this analysis with additional GO terms, the robustness of the Knet across different distance measures can be seen. Due to the time required to compute the distance matrices, the number of permutations has been reduced to 10. Running with least 1000 permutations is recommended.

results <- sapply(networks, function(g) Knet(g, nperm=10, 
    dist.method="shortest.paths", vertex.attr="rdds", 
    edge.attr="distance"), simplify=FALSE)

## computing graph distance matrix... done
## computing graph distance bins... done
## computing the clustering of the 'rdds' weights using 10 permutations...  done
## computing graph distance matrix... done
## computing graph distance bins... done
## computing the clustering of the 'rdds' weights using 10 permutations...  done

p.values <- sapply(results, function(res) res$pval)
p.values

##      treated    untreated 
## 8.551402e-33 5.909855e-17

Since the GO term tested was response to DNA damage stimulus, it is not surprising that the gene set associated more significantly with the treated network (as shown by the lower p-value). The yeast exposed to the DNA-damaging agent have activated or upregulated pathways involved in responding to the agent, thereby increasing the strength of the genetic interaction between DNA-damage response-related genes.

v) Using Knet to investigate the functional rewiring of the yeast interaction network in response to UV damage

In this section, we will conduct similar analysis to the previous section. However, in this section, we will use a network created from GIs mapped in S. cerevisiae exposed to a high dosage of UV and GIs mapped from untreated S. cerevisiae. Due to the reduced number of interactions mapped in this study, it is not possible to build networks from correlations in GI. Therefore, the networks are built from the raw interactions.

# laod igraph object
data(g.srivas.high)
data(g.srivas.untreated)
networks <- list(
    high=g.srivas.high, 
    untreated=g.srivas.untreated
)
network.names <- names(networks)

We will again test only a single GO term (GO:0000725, recombinational repair). In the associated paper, a greater number of GO terms are tested.

# obtain GO term associations
library(org.Sc.sgd.db, quietly=TRUE)
xx <- as.list(org.Sc.sgdGO2ALLORFS)
associated.genes <- xx[["GO:0000725"]] 
associations <- sapply(networks, function(g) 
    as.numeric(V(g)$name %in% associated.genes), 
    simplify=FALSE)
networks <- sapply(network.names, function(name)
    set.vertex.attribute(networks[[name]], "dsbr", 
    value=associations[[name]]), simplify=FALSE)

The Knet function will again be used to compare the strength of association of the GO term with the 2 networks. Due to the time required to compute the distance matrices, the number of permutations has been reduced to 10. Running with least 1000 permutations is recommended.

p.values <- sapply(networks, function(g) 
       Knet(g, nperm=10, dist.method="shortest.paths", 
       vertex.attr="dsbr", edge.attr="distance")$pval)

## computing graph distance matrix... done
## computing graph distance bins... done
## computing the clustering of the 'dsbr' weights using 10 permutations...  done
## computing graph distance matrix... done
## computing graph distance bins... done
## computing the clustering of the 'dsbr' weights using 10 permutations...  done

p.values

##         high    untreated 
## 2.660426e-15 8.863750e-02

As expected, the GO term associates more strongly with the UV treated network. This is likely due to the functional rewiring that occurs within the yeast when exposed to DNA-damaging UV radiation.

vi) Using Knet to identify the network most informative about cancer cell lines

In this section, we will apply a continuous distribution of vertex weights, describing the strength of evidence that a gene is involved in the maintenance of cancer cell lines, to two networks in order to identify the network that best explains the mechanisms that underlie cell line maintenance.

Biological networks can be built using a wide range of data sources, including co-expression data, data on physical interactions or genetic interaction data. Networks can also be created by combining data sources. The HumanNet network was created by combining 21 data sources. The IntAct network is created using curated data on physical interactions.

In the next section we will apply pre-processed RNAi data from cancer cell lines to both networks as vertex weights. The GENE-E program has been used to convert the essentiality scores for each shRNA into gene-wise p-values. This p-values are contained within the loaded dataset. We will also convert these p-values into vertex weights that can be applied to the networks. By taking the \(-log_{10}\) of the p-values, we ensure that the most essential genes are given the highest vertex weights.

# import and convert RNAi data
data(rnai.cheung)
rnai.cheung <- -log10(rnai.cheung)
rnai.cheung[!is.finite(rnai.cheung)] <- max(rnai.cheung[is.finite(rnai.cheung)])
rnai.cheung[rnai.cheung < 0] <- 0

Next, we will load the interaction data for the two network and create an igraph object for each. No edge weights are given for the IntAct network. Each of the interactions in the HumanNet network is associated with a log likelihood score (LLS), describing the probability the functional interaction occurring given the data used. In order to reduce the density of the network, interactions with an \(LLS < 2\) have been removed. Distances for the remaining edges have been produced by dividing the weights by the maximum weight and taking the \(-log_{10}\) of this score. We have removed all genes not part of the largest cluster from each network.

# import and create IntAct network
data(edgelist.intact)
g.intact <- graph.edgelist(as.matrix(edgelist.intact), 
    directed=FALSE)

# import data and create HumanNet network
data(edgelist.humannet)
g.humannet <- graph.edgelist(as.matrix(edgelist.humannet)[,1:2], 
    directed=FALSE)
g.humannet <- set.edge.attribute(g.humannet, "distance", 
    value=edgelist.humannet$distance)
networks <- list(intact=g.intact, humannet=g.humannet)

Next, we will apply the vertex weights to the networks. Not all networks genes have vertex weights available.

network.names <- names(networks)
network.genes <- sapply(networks, get.vertex.attribute, 
    name="name", simplify=FALSE)
rnai.cheung.genes <- rownames(rnai.cheung)
cancers <- colnames(rnai.cheung)

for (cancer in cancers) {
    for (name in network.names) {
        vertex.weights <-rep(NA, vcount(networks[[name]]))
        names(vertex.weights) <- network.genes[[name]]
        common.genes <- rnai.cheung.genes[rnai.cheung.genes
            %in% network.genes[[name]]] 
        vertex.weights[common.genes] <- rnai.cheung[common.genes, cancer]
        networks[[name]] <- set.vertex.attribute(networks[[name]], 
            cancer, value=vertex.weights)
    }
}

We will now apply the Knet-function to these scores. Due to the time required to compute the distance matrices, the number of permutations has been reduced to 10. Running with least 1000 permutations is recommended.

knet.res <- sapply(networks, Knet, nperm=10, 
   dist.method="shortest.paths", vertex.attr=cancers, 
   edge.attr="distance", simplify=FALSE)

## no edge attribute with name 'distance' found so all edge distances set to 1

## computing graph distance matrix... done
## computing graph distance bins... done
## computing the clustering of the 'colon' weights (1/6) using 10 permutations...  done
## computing the clustering of the 'esophagealsq' weights (2/6) using 10 permutations...  done
## computing the clustering of the 'gbm' weights (3/6) using 10 permutations...  done
## computing the clustering of the 'nsclc' weights (4/6) using 10 permutations...  done
## computing the clustering of the 'ovarian' weights (5/6) using 10 permutations...  done
## computing the clustering of the 'pancreas' weights (6/6) using 10 permutations...  done
## computing graph distance matrix... done
## computing graph distance bins... done
## computing the clustering of the 'colon' weights (1/6) using 10 permutations...  done
## computing the clustering of the 'esophagealsq' weights (2/6) using 10 permutations...  done
## computing the clustering of the 'gbm' weights (3/6) using 10 permutations...  done
## computing the clustering of the 'nsclc' weights (4/6) using 10 permutations...  done
## computing the clustering of the 'ovarian' weights (5/6) using 10 permutations...  done
## computing the clustering of the 'pancreas' weights (6/6) using 10 permutations...  done

p.values <- sapply(knet.res, function(i) sapply(i, 
   function(j) j$pval))
p.values

##                    intact     humannet
## colon        6.527441e-06 3.688332e-08
## esophagealsq 9.244178e-01 9.350144e-01
## gbm          9.971605e-01 9.989558e-01
## nsclc        3.298220e-04 9.511057e-02
## ovarian      3.905352e-10 8.537960e-11
## pancreas     8.254727e-01 3.750676e-07

The RNAi data from the colon and ovarian cancer cell lines associate strongly with both networks. However, they associate associate more strongly with the HumanNet network, indicating that the HumanNet network better explains the cellular mechanisms involved in maintaining these cell lines.