NetSAM: Network Seriation and Modularization

In bingzhang16/NetSAM: Network Seriation And Modularization

Network Seriation and Modularization

Description

The NetSAM function uses random walk distance-based hierarchical clustering to identify the hierarchical modules of a weighted or unweighted network and then uses the optimal leaf ordering (OLO) method to optimize the one-dimensional ordering of the genes in each module by minimizing the sum of the pair-wise random walk distance of adjacent genes in the ordering.

Usage

NetSAM(inputNetwork, outputFileName, outputFormat="nsm", edgeType="unweighted", map_to_genesymbol=FALSE, organism="hsapiens", idType="auto",minModule=0.003, stepIte=FALSE, maxStep=4, moduleSigMethod="cutoff", modularityThr=0.2, ZRanNum=10, PerRanNum=100, ranSig=0.05, edgeThr=(-1), nodeThr=(-1), nThreads=3)

Arguments

inputNetwork	The network under analysis. inputNetwork can be the directory of the input network file including the file name with "net" extension. If edgeType is "unweighted", each row represents an edge with two node names separated by a tab or space. If edgeType is "weighted", each row represents an edge with two node names and edge weight separated by a tab or space. inputNetwork can also be a data object in R (data object must be igraph, graphNEL, matrix or data.frame class).
edgeType	The type of the input network: "weighted" or "unweighted".
outputFileName	The name of the output file.
outputFormat	The format of the output file. "nsm" format can be used as an input in NetGestalt; "gmt" format can be used to do other network analysis (e.g. as an input in GSEA (Gene Set Enrichment Analysis) to do module enrichment analysis); "multiple" represents the NetSAM function will output three files: ruler file containing gene order information, hmi file containing module information and net file containing network information; and "none" represents the function will not output any file.
map_to_genesymbol	Because pathway enrichment analysis in NetGestalt is based on gene symbol, setting map_to_genesymbol as TRUE can transform other ids in the network into gene symbols and thus allow users to do functional analysis based on the identified modules. If the input network is not a biology network or users do not plan to do enrichment analysis in the NetGestalt, users can set map_to_genesymbol as FALSE. The default is FALSE.
organism	The organism of the input network. Currently, the package supports the following nine organisms: hsapiens, mmusculus, rnorvegicus, drerio, celegans, scerevisiae, cfamiliaris, dmelanogaster and athaliana. The default is "hsapiens".
idType	The id type of the ids in the input network. MatSAM will use BiomaRt package to transform the input ids to gene symbols based on idType. The users can also set idType as "auto" that means MatSAM will automatically search the id type of the input data. However, this may take 10 minutes based on the users' internet speed. The default is "auto".
minModule	The minimum percentage of nodes in a module. The minimum size of a module is calculated by multiplying minModule by the number of nodes in the whole network. If the size of a module identified by the function is less than the minimum size, the module will not be further partitioned into sub-modules. The default is 0.003 which means the minimum module size is 30 if there are 10,000 nodes in the whole network. If the minimum module size is less than 5, the minimum module size will be set as 5. The minModule should be less than 0.2.
stepIte	Because NetSAM uses random walk distance-based hierarchical clustering to reveal the hierarchical organization of an input network, it requires a specified length of the random walks. If stepIte is TRUE, the function will test a range of lengths ranging from 2 to maxStep to get the optimal length. Otherwise, the function will directly use maxStep as the length of the random walks. The default maxStep is 4. Because optimizing the length of the random walks will take a long time, if the network is too big (e.g. the number of edges is over 200,000), we suggest to set stepIte as FALSE.
maxStep	The length or max length of the random walks.
moduleSigMethod	To test whether a network under consideration has a non-random internal modular organization, the function provides three options: "cutoff", "zscore" and "permutation". "cutoff" means if the modularity score of the network is above a specified cutoff value, the network will be considered to have internal organization and will be further partitioned. For "zscore" and "permutation", the function will first generate a set of random modularity scores. Based on a unweighted network, the function uses the edge switching method to generate a given number of random networks with the same number of nodes and an identical degree sequence and calculates the modularity scores for these random networks. Based on a weighted network, the function shuffles the weights of all edges and calculate the modularity scores for network with random weights. Then, "zscore" method will transform the real modularity score to a z score based on the random modularity scores and then transform the z score to a p value assuming a standard normal distribution. The "permutation" method will compare the real modularity score with the random ones to calculate a p value. Finally, under a specified significance level, the function determines whether the network can be further partitioned. The default is "cutoff".
modularityThr	Threshold of modularity score for the "cutoff" method. The default is 0.2
ZRanNum	The number of random networks that will be generated for the "zscore" calculation. The default is 10.
PerRanNum	The number of random networks that will be generated for the "permutation" p value calculation. The default is 100.
ranSig	The significance level for determining whether a network has non-random internal modular organization for the "zscore" or "permutation" methods.
edgeThr	If the network is too big, it will take a long time to identify the modules. Thus, the function provides the option to set the threshold of number of edges and nodes as edgeThr and nodeThr. If the size of network is over the threshold, the function will stop and the users should change the parameters and re-run the function. We suggest to set the threshold for node as 12,000 and the threshold for edge as 300,000. The default is -1, which means there is no limitation for the input network.
nodeThr	see edgeThr.
nThreads	NetSAM function supports parallel computing based on multiple cores. The default is 3.

Value

If output format is "nsm", the function will output not only a "nsm" file but also a list object containing module information, gene order information and network information. If output format is "gmt", the function will output the "gmt" file and a matrix object containing the module and annotation information.

Note

Because the seriation step requires pair-wise distance between all nodes, NetSAM is memory consuming. We recommend to use the 64 bit version of R to run the NetSAM. For networks with less than 10,000 nodes, we recommend to use a computer with 8GB memory. For networks with more than 10,000 nodes, a computer with at least 16GB memory is recommended.

Author(s)

Jing Wang

Examples

	inputNetworkDir <- system.file("extdata","exampleNetwork.net",package="NetSAM")
	outputFileName <- paste(getwd(),"/NetSAM",sep="")
	result <- NetSAM(inputNetwork=inputNetworkDir, outputFileName=outputFileName, outputFormat="nsm", edgeType="unweighted", map_to_genesymbol=FALSE, organism="hsapiens", idType="auto",minModule=0.003, stepIte=FALSE, maxStep=4, moduleSigMethod="cutoff", modularityThr=0.2, ZRanNum=10, PerRanNum=100, ranSig=0.05, edgeThr=(-1), nodeThr=(-1), nThreads=3)

bingzhang16/NetSAM documentation built on April 3, 2024, 3:35 a.m.