Description Usage Arguments Value Author(s) Examples

View source: R/wTO.Complete2.R

Compute the wTO and also the bootstraps. Proposed at: arXiv:1711.04702

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`k` |
Number of threads to be used for computing the weight Topological Overlap. Default is set to 1. |

`n` |
Number of resamplings, used to compute the empirical distribuitions of the links. Default is set to 100. |

`Data` |
data.frame containing the count / expression data for the correlation. |

`Overlap` |
Set of nodes of interest, where the Overlapping weights will be computed. |

`method` |
Type of the correlation that should be used. "s" / "spearman" will compute the rank spearman correlation, "p" / "pearson" will compute the linear correlation. If no value is given, the default is to use "p". |

`method_resampling` |
method of the resampling. Bootstrap, BlockBootstrap or Reshuffle. Bootstrap null hypothesis is that the wTO is random, and Reshuffle tests if the wTO is equal to zero. |

`pvalmethod` |
method to compute the multiple test correction for the pvalue. for more information check the function |

`savecor` |
T/F if need to save the correlation. |

`expected.diff` |
Difference expected between the real wTO and resampled wTO By default, it is set to 0.2. |

`lag` |
time dependency, lag, if you are using the BlockedBootstrap. |

`ID` |
ID of the samples for the blocked bootstrap (for repeated measures). |

`normalize` |
T/F Should the data be normalized? |

`plot` |
T/F Should the diagnosis plot be plotted? |

a list with results.

wTO is a data.frame containig the Nodes, the wTO computed using the signed correlations, the pvalue and the adj.pvalue.

abs.wTO is a data.frame containig the Nodes, the wTO computed using the absolute correlations, the pvalue and the adj.pvalue.

Correlation is a data.frame containing the correlation between all the nodes.

Empirical.Quantile quantile values for the empirical distribution.

Quantile quantile values for the sample distribution.

Deisy Morselli Gysi <deisy at bioinf.uni-leipzig.de>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Not run:
# Using spearman rank correlation and bonferroni correction for the pvalues.
wTO.Complete( k =8, n = 1000, Data = Microarray_Expression1,
Overlap = ExampleGRF$x, method = "s", pvalmethod = "bonferroni")
# Changing the resampling method to Reshuffle.
wTO.Complete( k =1, n = 1000, Data = Microarray_Expression1,
Overlap = ExampleGRF$x, method_resampling = "Reshuffle")
# Changing the resampling method to BlockBootstrap, with a lag of 2.
row.names(metagenomics_abundance) = metagenomics_abundance$OTU
metagenomics_abundance = metagenomics_abundance[,-1]
wTO.Complete( k =1, n = 1000, Data = metagenomics_abundance, method = "s",
Overlap = row.names(metagenomics_abundance), method_resampling = "BlockBootstrap", lag = 2)
wTO.Complete( k =2, n = 1000, Data = Microarray_Expression1, method = "s",
Overlap = ExampleGRF$x, method_resampling = "BlockBootstrap", ID = rep(1:9,each = 2))
X = wTO.Complete( k =1, n = 1000, Data = Microarray_Expression1,
Overlap = ExampleGRF$x, method = "p", plot = FALSE)
## End(Not run)
``` |

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