Description Usage Arguments Details Value Author(s) References
An implementation of WGCNA to correlate coexpression modules to disease
1 2 3 4 5 6 | wgcna(MODifieR_input, group_of_interest, minModuleSize = 30,
deepSplit = 2, pamRespectsDendro = F, mergeCutHeight = 0.1,
numericLabels = T, pval_cutoff = 0.05, corType = "bicor",
maxBlockSize = 5000, TOMType = "signed", saveTOMs = T,
maxPOutliers = 0.1,
dataset_name = deparse(substitute(MODifieR_input)))
|
MODifieR_input |
A MODifieR input object produced by one of the |
group_of_interest |
Numerical value denoting which group contains the condition of interest (1 or 2) |
minModuleSize |
minimum module size for module detection. See
|
deepSplit |
integer value between 0 and 4. Provides a simplified control over how sensitive
module detection should be to module splitting, with 0 least and 4 most sensitive. See
|
pamRespectsDendro |
Logical, only used when |
mergeCutHeight |
dendrogram cut height for module merging. |
numericLabels |
logical: should the returned modules be labeled by colors ( |
pval_cutoff |
The p-value cutoff to be used for significant co-expression modules (colors) |
corType |
character string specifying the correlation to be used. Allowed values are (unique
abbreviations of) |
maxBlockSize |
integer giving maximum block size for module detection. Ignored if |
TOMType |
one of |
saveTOMs |
logical: should the consensus topological overlap matrices for each block be saved and returned? |
maxPOutliers |
only used for |
dataset_name |
Optional name for the input object that will be stored in the settings object. Default is the variable name of the input object |
wgcna is an implementation of WGCNA that
associates co-expression modules (denoted by color) to a trait. Co-expression modules with an
adjusted p-value < pval_cutoff
will make up the final disease module.
The algorithm infers co-expression modules from combined expression dataset from both group1
and group2
.
Co-expression modules are then correlated to trait (group 1 ~ group 2).
After analysis there are some post-processing functions available:
wgcna_get_all_module_genes
Get a list with all genes sorted by module color
wgcna_get_module_genes_by_sign
Get a module with either only postively correlated
genes or negatively correlated genes
wgcna_adjust_significance
Adjust p-value cutoff
wgcna_split_module_by_color
Get a list where each color is a separate module
wgcna_set_module_size
Get a module close to a specific size
wgcna returns an object of class "MODifieR_module" with subclass "WGCNA". This object is a named list containing the following components:
module_genes |
A character vector containing the genes in the final module |
info_table |
A data.frame containing all genes and their assigned colors |
correlation_to_trait_table |
A data.frame containing all module colors and their p- and adjusted p-value |
softthreshold_value |
A numeric, the soft threshold power that is used. See: |
module_colors |
A character vector containing the colors that make up the final disease module |
settings |
A named list containing the parameters used in generating the object |
Dirk de Weerd
Langfelder P and Horvath S, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 doi:10.1186/1471-2105-9-559
Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering. J ournal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/
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