diffcoex: DiffCoEx
In ddeweerd/MODifieRDev: MODifieRDev

Description Usage Arguments Details Value References

An implementation of the DiffCoEx co-expression based algorithm

diffcoex(MODifieR_input, beta = NULL, cor_method = "spearman",
  cluster_method = "average", cuttree_method = "hybrid",
  cut_height = 0.996, deepSplit = 0, pamRespectsDendro = F,
  minClusterSize = 20, cutHeight = 0.2, pval_cutoff = 0.05,
  dataset_name = NULL)

`MODifieR_input`	A MODifieR input object produced by one of the `create_input` functions
`beta`	User-defined soft thresholding power For method=="tree" it defaults to 0.99. For method=="hybrid" it defaults to 99 maximum of the joining heights on the dendrogram.
`cor_method`	a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
`cluster_method`	the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid". This applies to hierachical clustering.
`cuttree_method`	Chooses the method to use. Recognized values are "hybrid" and "tree".
`cut_height`	Maximum joining heights that will be considered.
`deepSplit`	For method "hybrid", can be either logical or integer in the range 0 to 4. For method "tree", must be logical. In both cases, provides a rough control over sensitivity to cluster splitting. The higher the value (or if `TRUE`), the more and smaller clusters will be produced. For the "hybrid" method, a finer control can be achieved via `maxCoreScatter` and `minGap` below.
`pamRespectsDendro`	Logical, only used for method "hybrid". If `TRUE`, the PAM stage will respect the dendrogram in the sense that objects and small clusters will only be assigned to clusters that belong to the same branch that the objects or small clusters being assigned belong to.
`minClusterSize`	Minimum cluster size.
`cutHeight`	Maximum joining heights that will be considered. For `method=="tree"` it defaults to 0.99. For `method=="hybrid"` it defaults to 99% of the range between the 5th percentile and the maximum of the joining heights on the dendrogram.
`pval_cutoff`	The p-value cutoff to be used for significant co-expression modules (colors)
`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

DiffCoEx is a method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis.

diffcoex returns an object of class "MODifieR_module" with subclass "DiffCoEx". This object is a named list containing the following components:

`module_genes`	A character vector containing the genes in the final module
`module_colors`	A character vector containing the colors that make up the final disease module
`color_vector`	A named character vector containing the genes as values and the color as name
`settings`	A named list containing the parameters used in generating the object

Tesson, B. M., Breitling, R., & Jansen, R. C. (2010). DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics, 11, 497. https://doi.org/10.1186/1471-2105-11-497

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