Description Usage Arguments Value Examples

View source: R/loading_files.R

Differential network analysis method

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`method` |
a function that receives two adjacency matrices and returns a list containing a statistic theta that measures the difference between them, and a p-value for the test H0: theta = 0 against H1: theta > 0. |

`options` |
a list contaning parameters used by 'method'. Used only in degreeDistributionTest, spectralEntropyTest and spectralDistributionTest functions. It can be set to either |

`varFile` |
a numeric matrix contaning variables values data. |

`labels` |
a vector of -1s, 0s, and 1s associating each sample with a phenotype. The value 0 corresponds to the first phenotype class of interest, 1 to the second phenotype class of interest, and -1 to the other classes, if there are more than two classes in the gene expression data. |

`varSets` |
a list of gene sets. Each element of the list is a character vector v, where v[1] contains the gene set name, v[2] descriptions about the set, v[3..length(v)] the genes that belong to the set. |

`adjacencyMatrix` |
a function that receives a numeric matrix containing gene expression data and returns the adjacency matrix of the inferred co-expression graph. |

`numPermutations` |
the number of permutations for the permutation test. |

`print` |
a logical. If true, it prints execution messages on the screen. resultsFile: path to a file where the partial results of the analysis will be saved. If NULL, then no partial results are saved. |

`resultsFile` |
a ".RData" file name to be saved in tha work directory. |

`seed` |
the seed for the random number generators. If it is not null then the sample permutations are the same for all the gene sets. |

`min.vert` |
lower number of nodes (variables) that has to be to compare the networks. |

`BPPARAM` |
An optional BiocParallelParam instance determining the parallel back-end to be used during evaluation, or a list of BiocParallelParam instances, to be applied in sequence for nested calls to BiocParallel functions. #MulticoreParam() |

`na.rm` |
remove the NA values by excluding the rows ("row") or the columns ("col") that contaings it. If NULL (default) the NA values are not removed. |

a data frame containing the name, size, test statistic, nominal p-value and adjusted p-value (q-value) associated with each gene set.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# Glioma data
data("varFile")
gliomaData <- system.file("extdata", "variablesValue_BioNetStat_tutorial_data.csv", package = "BioNetStat")
labels<-doLabels(gliomaData)
adjacencyMatrix1 <- adjacencyMatrix(method="spearman", association="pvalue", threshold="fdr",
thr.value=0.05, weighted=FALSE)
diffNetAnalysis(method=degreeCentralityTest, varFile=varFile, labels=labels, varSets=NULL,
adjacencyMatrix=adjacencyMatrix1, numPermutations=1, print=TRUE, resultsFile=NULL,
seed=NULL, min.vert=5, option=NULL)
# The numPermutations number is 1 to do a faster example, but we advise to use unless 1000 permutations in real analysis
# Random data
set.seed(1)
varFile <- as.data.frame(matrix(rnorm(120),40,30))
labels<-data.frame(code=rep(0:3,10),names=rep(c("A","B","C","D"),10))
adjacencyMatrix1 <- adjacencyMatrix(method="spearman", association="pvalue", threshold="fdr",
thr.value=0.05, weighted=FALSE)
diffNetAnalysis(method=degreeCentralityTest, varFile=varFile, labels=labels, varSets=NULL,
adjacencyMatrix=adjacencyMatrix1, numPermutations=10, print=TRUE, resultsFile=NULL,
seed=NULL, min.vert=5, option=NULL)
``` |

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