nullmodel_GoM: Null models for Grade of Membership (GoM) cluster validation

In CountClust: Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models

Description

Use null models (popular in ecology) to generate randomized matrix of counts given the observed data matrix, fit the GoM model to these null matrices and compare the fit on null model data with that on the observed data. Used for validating the GoM clusters

Usage

nullmodel_GoM(counts, K, tol = 0.1, null.model = c("frequency", "richness",
  "independentswap", "trialswap"), iter_fill = 1000, iter_randomized = 100,
  plot = TRUE)

Arguments

counts	The counts matrix (N x G): N- the number of samples, G- number of features
K	The number of clusters to fit
tol	The tolerance of the GoM model fitted
null.model	The type of nullmodel used (similar to the randomizeMatrix() function argument in picante package)
iter_fill	The number of swaps/fills in each randomized matrix build
iter_randomized	The number of randomization matrices generated
plot	If TRUE, plots density of log Bayes factor

Value

Returns a list with

GoMBF.obs	log BF for the observed counts with K=2 against the null with no clusters
GoMBF.rand	a vector of log BF for each randomized count matrix with K=2 against the null with no clusters
pval	the p-value of the observed log Bayes factor against the ones from randomized matrices

Examples

data("ex.counts")
nullmodel_GoM(ex.counts,
              K=2,
              tol=500,
              null.model="frequency",
              iter_randomized=3,
              plot=FALSE)

CountClust documentation built on Nov. 8, 2020, 5:01 p.m.