meNet_cor.RdDifferent options are available to determine which correlations are significant, such as a constant threshold for correlation, standard deviation of bootstrapping or p-value of the permutation test. More than one method can be used simultaneously. Significant correlations determine the edged of the network. For better explanation of the significant criteria, read 'Details'.
meNet_cor( cor_matrix = NULL, data = NULL, cg_ids = NULL, cor_threshold = 0.2, neg_cor_threshold = NULL, cor_stDev = cor_threshold/3, cor_alpha = NULL, n_repetitions = 100, alternative = "two_sided", normalization_fun = NULL )
| cor_matrix | Correlation matrix of CpG sites. |
|---|---|
| data | Data frame with CpGs in columns. Variables in rows are used to calculate `cor_matrix`. |
| cg_ids | List of CpGs for which we reconstruct the network. If names of CpGs are given as `cor_matrix` or `data` column names, `cg_ids` defines a subset of CpGs which should be used in network. If omitted, all CpGs are used. If CpG names are not given, `cg_ids` is a mandatory parameter which gives the names of CpGs. |
| cor_threshold | Correlation threshold. Defaults to `0.2`. |
| neg_cor_threshold | Negative correlation threshold. This parameter is ignored if `cor_threshold` is not given. Defaults to `NULL`. |
| cor_stDev | Threshold for the standard deviation of correlation. Default to `cor_threshold/3`. |
| cor_alpha | Significance level of the correlation permutation test. Defaults to `NULL`. |
| n_repetitions | Number of repetitions for resampling and/or for the correlation permutation test. Defaults to `100`. |
| alternative | Alternative hypothesis for the correlation permutation test. Default to `"two_sided"`. |
| normalization_function | Normalization function applied to the weights of the edges. By default, no normalization is applied and weights correspond to correlation. |
Weighted network as `igraph` object with CpGs as nodes and edges representing significantly correlated pairs of CpGs. The weights of edges represent (possibly normalized) correlation. Isolated nodes are kept in the network.
A method for a significance of correlations is used whenever its parameters are correctly specified. At the same time, more than one method can be used in which case only correlations which are significant according to all methods are kept.
For a threshold method to be used, `cor_threshold` has to have a numeric value. Additionally, `neg_cor_threshold` can be specified. If only `cor_threshold` is given, correlations with absolute value larger than `cor_threshold` are considered significant. If also `neg_cor_threshold` is given, correlations smaller than `neg_cor_threshold` or larger than `cor_threshold` are considered significant. This allows different penalization of negative correlation values.
For a method based on standard deviation of bootstrapping to be used, `cor_stDev` has to have a numeric value, `data` has to be provided and `n_repetitions` has to be correctly specified. Function `cor_resamplingStats` is called for `data` with subsample size equal to the number of rows in `data` and with replacement. Correlations for which the calculated standard deviation of resampling is smaller than `cor_stDev` are considered significant. This method tests variability of correlation.
For a method based on the permutation test to be used, `cor_alpha` has to have a numeric value, `data` has to be provided and parameters `n_repetitions` and `alternative` have to be correctly specified. Parameter `alternative` has to have one of the values `"less"`, `"greater"`, `"two_sided"` or `"two_sided_signed"`. Function `cor_permutationTest` is called for `data`. Correlations for which the p-value of the permutation test are smaller than `cor_alpha` are considered significant.