SpaCC_Methy: Performs Spatial Convex Clustering for methylation data

In SpaCCr: Spatial Convex Clustering

Description

Performs Spatial Convex Clustering for methylation data

Usage

SpaCC_Methy(X, Coordinates, gamma.seq, dist.cutoff = 20000, sig = 1/5000,
  weights = NULL, center = TRUE, scale = FALSE, nfolds = 5, nu = NULL,
  tol.base = 1e-04, tol.miss = 1e-04, max.iter.base = 5000,
  max.iter.miss = 500, frac = 0.1, parallel = FALSE, gam.rule = 2,
  thresh.mult = 1, thresh.value = NULL)

Arguments

X	A subject (n) by variable (p) matrix; the data
Coordinates	a vector listing genomic coordinates
gamma.seq	a vector of regularization parameters
dist.cutoff	maximum distance at which probes should be regularized
sig	positive scalar controling spatial weight decay
weights	a vector of spatial weights
center	should data be centered
scale	should data be scaled
nfolds	number of folds for cross validation
nu	parameter for augmented lagrangian
tol.base	tolerance level for base function
tol.miss	tolerance for missing function
max.iter.base	maximum number of iterations for base function
max.iter.miss	maximum number of iterations for missing function
frac	fration of fold to use for cross validation
parallel	should algorithm be run in parallel
gam.rule	cross validation rule
thresh.mult	multiplier for threshold value
thresh.value	value of threshold

Value

Labels a vector of cluster labels

Examples

data("methy")
methy <- methy[1:20,1:10]
library(dplyr)
library(tidyr)
Coordinates <- methy$Genomic_Coordinate
methy %>%
 tbl_df() %>%
 select(-Chromosome,-Genomic_Coordinate) %>%
 gather(Subject,Value,-ProbeID) %>%
 spread(ProbeID,Value) -> X
SubjectLabels <- X$Subject
X <- X[,-1] %>% as.matrix()
verbose=TRUE
tol.base = 1e-4
tol.miss = 1e-4
max.iter.base=5000
max.iter.miss=500
ngam = 20
gamma.seq <- exp(seq(log(1e-1),log(1e1),length.out=ngam))
ClusterLabels <- SpaCC_Methy(X = X,Coordinates = Coordinates,gamma.seq = gamma.seq)

SpaCCr documentation built on May 2, 2019, 11:02 a.m.