R/overview.R

In CePa: Centrality-Based Pathway Enrichment

# == title (package:CePa)
# Centrality-based pathway enrichment
#
# == 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.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == examples
# \dontrun{
#
# # 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)
#
# }