qfeat_statistical: Create a list of correlation matrices from QFeatures input
In MetClassNet/MetClassNetR: Create and analize networks built from metabolomics Data

qfeat_statistical

R Documentation

Create a list of correlation matrices from QFeatures input

Description

The function qfeat_statistical uses the input from QFeatures and creates an adjacency matrix based on statistical methods using the MetNet package. The function includes functionality to calculate adjacency matrices based on LASSO (L1 norm)-regression, random forests, context likelihood of relatedness (CLR), the algorithm for the reconstruction of accurate cellular networks (ARACNE), Pearson correlation (also partial and semipartial), Spearman correlation (also partial and semipartial) and score-based structure learning (Bayes). The function returns a list of adjacency matrices that are defined by model. Additionally, for pearson and/or spearman correlation also the negative correlation values and the corresponding p-Value is calculated and listed, when p is set to TRUE.

Usage

qfeat_statistical(x, assay_name = "features", na.omit = FALSE, ...)

Arguments

x

QFeatures file

@param assay_name Character, define which assay needs to be extracted from QFeature input e.g. "pos".

@param ... Insert here parameter from statistical function from MetNetpackage. (https://github.com/MetClassNet/MetNet) model is a character vector containing the methods that will be used ("lasso", "randomForest", "clr", "aracne", "pearson", "pearson_partial", "pearson_semipartial", "spearman", "spearman_partial", "spearman_semipartial", "bayes") data.frame, containing the columns "group". pis logical, by default is set to FALSE. '

Details

qfeat_statistical extracts required information from a QFeatures input and builds data.frames containing intensity informations of all samples. Then the function statistical from the MetNet package is applied to calculate adjacency matrices based on LASSO (L1 norm)-regression, random forests, context likelihood of relatedness (CLR), the algorithm for the reconstruction of accurate cellular networks (ARACNE), Pearson correlation (also partial and semipartial), Spearman correlation (also partial and semipartial) and Constraint-based structure learning (Bayes). The default of p is FALSE. Then all types can be selected in model. The positive correlation value will be displayed in the correlation matrix. If p is set to TRUE, only "pearson" and/or "spearman" correlation may be selected. As output positive and negative correlation values will be displayed. Morover their corresponding p-values will be added to the list.

@return list containing the respective adjacency matrices specified by model. It p⁠is TRUE, also the corresponding p-values of Spearman and/or Pearson Correlation will be added to the⁠list'.