wTO (version 2.1)

wTO.Complete: wTO.Complete

Description

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

Usage

wTO.Complete(
  k = 1,
  n = 100,
  Data,
  Overlap = row.names(Data),
  method = "p",
  method_resampling = "Bootstrap",
  pvalmethod = "BH",
  savecor = F,
  expected.diff = 0.2,
  lag = NULL,
  ID = NULL,
  normalize = F,
  plot = T
)

Value

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.

Arguments

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 p.adjust.

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?

Author

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

Examples

if (FALSE) {
# 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)
 }