wgcna: An implementation of WGCNA to correlate coexpression modules...
In ddeweerd/MODifieRDev: MODifieRDev

Description Usage Arguments Details Value Author(s) References

An implementation of WGCNA to correlate coexpression modules to disease

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 `create_input` functions
`group_of_interest`	Numerical value denoting which group contains the condition of interest (1 or 2)
`minModuleSize`	minimum module size for module detection. See `cutreeDynamic` for more details.
`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 `cutreeDynamic` for more details.
`pamRespectsDendro`	Logical, only used when `pamStage` is `TRUE`. If `TRUE`, the PAM stage will respect the dendrogram in the sense an object can be PAM-assigned only to clusters that lie below it on the branch that the object is merged into. See `cutreeDynamic` for more details.
`mergeCutHeight`	dendrogram cut height for module merging.
`numericLabels`	logical: should the returned modules be labeled by colors (`FALSE`), or by numbers (`TRUE`)?
`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) `"pearson"` and `"bicor"`, corresponding to Pearson and bidweight midcorrelation, respectively. Missing values are handled using the `pairwise.complete.obs` option.
`maxBlockSize`	integer giving maximum block size for module detection. Ignored if `blocks` above is non-NULL. Otherwise, if the number of genes in `datExpr` exceeds `maxBlockSize`, genes will be pre-clustered into blocks whose size should not exceed `maxBlockSize`.
`TOMType`	one of `"none"`, `"unsigned"`, `"signed"`. If `"none"`, adjacency will be used for clustering. If `"unsigned"`, the standard TOM will be used (more generally, TOM function will receive the adjacency as input). If `"signed"`, TOM will keep track of the sign of correlations between neighbors.
`saveTOMs`	logical: should the consensus topological overlap matrices for each block be saved and returned?
`maxPOutliers`	only used for `corType=="bicor"`. Specifies the maximum percentile of data that can be considered outliers on either side of the median separately. For each side of the median, if higher percentile than `maxPOutliers` is considered an outlier by the weight function based on `9*mad(x)`, the width of the weight function is increased such that the percentile of outliers on that side of the median equals `maxPOutliers`. Using `maxPOutliers=1` will effectively disable all weight function broadening; using `maxPOutliers=0` will give results that are quite similar (but not equal to) Pearson correlation.
`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: `pickSoftThreshold`
`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/

ddeweerd/MODifieRDev documentation built on Nov. 12, 2019, 7:50 a.m.