R: Function to use clipper method on microarray or RNA-Seq data
clipper
R Documentation
Function to use clipper method on microarray or RNA-Seq data
Description
clipper is a method for topological gene set analysis. It implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.
An ExpressionSet object or a gene expression data matrix or count matrix, rows refer to genes, columns to samples
group
Name or number of the phenoData column or a character vector or factor that contains required class assigments
pathways
A list of pathways in a form from graphite package or created by preparePathways()
type
Type of the input data, "MA" for microarray and "RNASeq" for RNA-Seq
preparePaths
Logical, by default the pathways are transformed with preparePathways(). Use FALSE, if you have done this transformation separately
norm.method
Character, the method to normalize RNAseq data. If NULL then TMM-normalization is performed. Possible values are: "TMM", "DESeq2", "rLog", "none"
test.method
Character, the method for differentiall expression analysis of RNAseq data. If NULL then "voomlimma" is used. Possible values are: "DESeq2", "voomlimma", "vstlimma", "edgeR". This analysis is needed only for the visualization.
method
Character, "mean" or "var", the kind of test to perform on the cliques
testCliques
Logical, if TRUE then the test is applied also on the cliques of the each pathway. It is a very time consuming calculation, especially for many or big pathways
nperm
Number of permutations
alphaV
Numeric, the threshold for variance test. The calculation of mean test depends on the result of variance test.
b
number of permutations for mean analysis
permute
always performs permutations in the concentration matrix test. If FALSE, the test is made using the asymptotic distribution of the log-likelihood ratio. This option should be use only if samples size is >=40 per class
A list. First slot is a data frame containing p-values and q-values of mean and variance tests on pathways. The second slot is a list containing data.frames of the most affected paths in each pathway. The columns of the data frames contain: 1 - Index of the starting clique 2 - Index of the ending clique 3 - Index of the clique where the maximum value is reached 4 - length of the path 5 - maximum score of the path 6 - average score along the path 7 - percentage of path activation 8 - impact of the path on the entire pathway 9 - clique involved and significant 10 - clique forming the path 11 - genes forming the significant cliques 12 - genes forming the path
topo.sig
if testCliques=TRUE, a list where each slot contains the pvalues and a list of cliques in one pathway. NULL otherwise
degtest
A data.frame of gene-level differential expression statistics
Note
If there are NA's only in columns 3 to 7, then a junction tree could not be formed.
Author(s)
Ivana Ihnatova
References
Martini P, Sales G, Massa MS, Chiogna M, Romualdi C. Along signal paths: an empirical gene set approach exploiting pathway topology. Nucleic Acids Res. 2013 Jan 7;41(1):e19. doi: 10.1093/nar/gks866. Epub 2012 Sep 21. PubMed PMID: 23002139; PubMed Central PMCID: PMC3592432.
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> library(ToPASeq)
Loading required package: graphite
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Loading required package: gRbase
Loading required package: graph
Loading required package: locfit
locfit 1.5-9.1 2013-03-22
Loading required package: Rgraphviz
Loading required package: grid
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ToPASeq/Clipper.Rd_%03d_medium.png", width=480, height=480)
> ### Name: clipper
> ### Title: Function to use clipper method on microarray or RNA-Seq data
> ### Aliases: clipper
> ### Keywords: htest
>
> ### ** Examples
>
>
>
> if (require(DEGraph)) {
+ data("Loi2008_DEGraphVignette")
+ pathways<-pathways("hsapiens","kegg")[1]
+ clipper( exprLoi2008, classLoi2008, pathways,type="MA", convertTo="none")
+ }
Loading required package: DEGraph
Warning message:
In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
there is no package called 'DEGraph'
>
> ## Not run:
> ##D if (require(gageData)) {
> ##D
> ##D data(hnrnp.cnts)
> ##D hnrnp.cnts<-hnrnp.cnts[rowSums(hnrnp.cnts)>0,]
> ##D group<-c(rep("sample",4), rep("control",4))
> ##D pathways<-pathways("hsapiens","kegg")[1:3]
> ##D clipper(hnrnp.cnts, group,pathways, type="RNASeq", norm.method="TMM", convertTo="none")
> ##D }
> ## End(Not run)
>
>
>
>
>
>
> dev.off()
null device
1
>