CePa (version 0.8.1)

CePa-package: Centrality-based pathway enrichment

Description

Centrality-based pathway enrichment

Author

Zuguang Gu <[e-mail]>

Details

Gene set enrichment analysis is broadly used in microarray data analysis aimed to find which biological functions are affected by a group of related genes behind the massive information. A lot of methods have been developed under the framework of over-represented analysis (ORA) such as GOstats and GSEABase. For a specific form of gene sets, biological pathways are collections of correlated genes/proteins, RNAs and compounds that work together to regulate specific biological processes. Instead of just being a list of genes, a pathway contains the most important information that is how the member genes interact with each other. Thus network structure information is necessary for the intepretation of the importance of the pathways.

In this package, the original pathway enrichment method (ORA) is extended by introducing network centralities as the weight of nodes which have been mapped from differentially expressed genes in pathways. There are two advantages compared to former work. First, for the diversity of genes' characters and the difficulties of covering the importance of genes from all aspects, we do not design a fixed measurement for each gene but set it as an optional parameter in the model. Researchers can select from candidate choices where different measurement reflects different aspect of the importance of genes. In our model, network centralities are used to measure the importance of genes in pathways. Different centrality measurements assign the importance to nodes from different aspects. For example, degree centrality measures the amount of neighbours that a node directly connects to, and betweenness centrality measures how many information streams must pass through a certain node. Generally speaking, nodes having large centrality values are central nodes in the network. It's observed that nodes represented as metabolites, proteins or genes with high centralities are essential to keep the steady state of biological networks. Moreover, different centrality measurements may relate to different biological functions. The selection of centralities for researchers depends on what kind of genes they think important. Second, we use nodes as the basic units of pathways instead of genes. We observe that nodes in the pathways include different types of molecules, such as single gene, complex and protein families. Assuming a complex or family contains ten differentially expressed member genes, in traditional ORA, these ten genes behave as the same position as other genes represented as single nodes, and thus they have effect of ten. It is not proper because these ten genes stay in a same node in the pathway and make functions with the effect of one node. Also, a same gene may locate in different complexes in a pathway and if taking the gene with effect of one, it would greatly decrease the importance of the gene. Therefore a mapping procedure from genes to pathway nodes is applied in our model. What's more, the nodes in pathways also include none-gene nodes such as microRNAs and compounds. These nodes also contribute to the topology of the pathway. So, when analyzing pathways, all types of nodes are retained.

The core function of the package is cepa.all. There is also a parallel version cepa.all.parallel. User can refer to the vignette to find how to use it (vignette("CePa")).

References

Gu Z, Liu J, Cao K, Zhang J, Wang J. Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes. BMC Syst Biol. 2012 Jun 6;6(1):56.

Examples

if (FALSE) {

# load the pathway database
data(PID.db)

# if you only have a differential gene list or other genes of interest
# in form of a list, you can apply the centrality-extended ORA method
res = cepa.all(dif = dif, bk = bk, pc = PID.db$NCI)
# in the above code, dif is your differential gene list, bk is your background
# gene list which always be whole set of genes on a certain microarray. If you 
# do not have a background gene list, do not set it and the function would use
# the whole human genome genes as default. pc is the pathway catalogue which in
# this example is the NCI catalogue gathered from PID database.

# after about 20 min, you can obtain a detailed report of the analysis
report(res)

# if you have a complete gene expression data, you can apply the centrality-extended
# GSA methods
res = cepa.all(mat = mat, label = label, pc = PID.db$NCI)
# mat is your expression value matrix, label is the design of the microarray experiment.
# By default, we use absolute value of t-value as the statistic for each gene and
# use the mean value of genes' statistics as the pathway statistic.

# after about 50 min, you can obtain a detailed report of the analysis
report(res)

}