Description Usage Arguments Details Value References See Also Examples

Basing on either variance or mean clique test, this function identifies the paths that are mostly related with the phenotype under study.

1 2 3 | ```
clipperAllRoots(expr, classes, graph, method=c("variance","mean",
"both", "paired"), nperm=100, alphaV=0.05, b=100, trZero=0.001, signThr=0.05,
maxGap=1, permute=TRUE, alwaysShrink=FALSE)
``` |

`expr` |
an expression matrix or ExpressionSet with colnames for samples and row name for genes. |

`classes` |
vector of 1,2 indicating the classes of samples (columns). |

`graph` |
a |

`method` |
the kind of test to perform on the cliques. It could be mean, variance, mixed (the best between variance and mean) or paired mean. |

`nperm` |
number of permutations. Default = 100. |

`alphaV` |
pvalue threshold for variance test to be used during mean test. Default = 0.05. |

`b` |
number of permutations for mean analysis. Default = 100. |

`trZero` |
lowest pvalue detectable. This threshold avoids that -log(p) goes infinite. |

`signThr` |
significance threshold for clique pvalues. |

`maxGap` |
allow up to maxGap gaps in the best path computation. Default = 1. |

`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. |

`alwaysShrink` |
always perform the shrinkage estimates of variance. |

The both method combines the results obtained from the mean and variance test. In particular it assign to the cliques the minimum of mean and variance p-values.

A matrix with a row for each paths. Rownames have the form:

roots-paths.

Columns are organized as follows:

Index of the starting clique

Index of the ending clique

Index of the clique where the maximum value is reached

Length of the path

Maximum score of the path

Average score along the path

Percentage of path activation

Impact of the path on the entire pathway

Cliques involved and significant

Cliques forming the path

Genes forming the significant cliques

Genes forming the path

Martini P, Sales G, Massa MS, Chiogna M, Romualdi C. Along signal paths: an empirical gene set approach exploiting pathway topology. NAR. 2012 Sep.

Massa MS, Chiogna M, Romualdi C. Gene set analysis exploiting the topology of a pathway. BMC System Biol. 2010 Sep 1;4:121.

`cliqueVarianceTest`

, `cliqueMeanTest`

,
`getJunctionTreePaths`

1 2 3 4 5 6 7 8 9 10 11 12 | ```
if (require(graphite) & require(ALL)){
kegg <- pathways("hsapiens", "kegg")
graph <- pathwayGraph(convertIdentifiers(kegg$'Chronic myeloid leukemia', "entrez"))
genes <- nodes(graph)
data(ALL)
all <- ALL[1:length(genes),1:20]
classes <- c(rep(1,10), rep(2,10))
featureNames(all@assayData)<- genes
graph <- subGraph(genes, graph)
clipped <- clipperAllRoots(all, classes, graph, "var", trZero=0.01, permute=FALSE)
clipped[,1:5]
}
``` |

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