Description Usage Arguments Value References See Also Examples

Easy clip function allows the full exploitation of Clipper Package features in a unique and easy to use function. Starting from an expression matrix and a pathway, these function extact the most transcriptionally altered portions of the graph.

1 2 3 |

`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 either mean or variance. |

`pathThr` |
The significance threshold of the whole pathway test. Deafault = 0.05 |

`pruneLevel` |
a dissimilarity threshold. NULL means no pruning. |

`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. |

`root` |
nodes by which rip ordering is performed (as far as possible) on the variables using the maximum cardinality search algorithm. |

`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. |

a matrix with row as the different paths. Columns are organized as follwes: 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)

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 | ```
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),1:24]
classes <- c(rep(1,12), rep(2,12))
featureNames(all@assayData)<- genes
graph <- subGraph(genes, graph)
easyClip(all, classes, graph, nperm=10)
}
``` |

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