coseqFullResults {coseq} | R Documentation |
The counts slot holds the count data as a matrix of non-negative integer count values, one row for each observational unit (gene or the like), and one column for each sample.
coseqFullResults(object, ...) clusters(object, ...) likelihood(object, ...) nbCluster(object, ...) proba(object, ...) ICL(object, ...) profiles(object, ...) tcounts(object, ...) transformationType(object, ...) model(object, ...) DDSEextract(object, ...) Djumpextract(object, ...) ## S4 method for signature 'coseqResults' clusters(object, K) ## S4 method for signature 'RangedSummarizedExperiment' clusters(object, ...) ## S4 method for signature 'matrix' clusters(object, ...) ## S4 method for signature 'data.frame' clusters(object, ...) ## S4 method for signature 'MixmodCluster' likelihood(object) ## S4 method for signature 'RangedSummarizedExperiment' likelihood(object) ## S4 method for signature 'coseqResults' likelihood(object) ## S4 method for signature ''NULL'' likelihood(object) ## S4 method for signature 'MixmodCluster' nbCluster(object) ## S4 method for signature 'RangedSummarizedExperiment' nbCluster(object) ## S4 method for signature 'coseqResults' nbCluster(object) ## S4 method for signature ''NULL'' nbCluster(object) ## S4 method for signature 'RangedSummarizedExperiment' ICL(object) ## S4 method for signature 'MixmodCluster' ICL(object) ## S4 method for signature 'coseqResults' ICL(object) ## S4 method for signature ''NULL'' ICL(object) ## S4 method for signature 'coseqResults' profiles(object) ## S4 method for signature 'coseqResults' tcounts(object) ## S4 method for signature 'coseqResults' transformationType(object) ## S4 method for signature 'coseqResults' model(object) ## S4 method for signature 'coseqResults' coseqFullResults(object) ## S4 method for signature 'coseqResults' show(object) ## S4 method for signature 'MixmodCluster' proba(object) ## S4 method for signature 'Capushe' DDSEextract(object) ## S4 method for signature 'Capushe' Djumpextract(object)
object |
a |
... |
Additional optional parameters |
K |
numeric indicating the model to be used (if NULL of missing, the model chosen by ICL is used by default) |
Output varies depending on the method. clusters
returns a vector of cluster
labels for each gene for the desired model.
Andrea Rau
## Simulate toy data, n = 300 observations set.seed(12345) countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4) countmat <- countmat[which(rowSums(countmat) > 0),] conds <- rep(c("A","B","C","D"), each=2) ## Run the Normal mixture model for K = 2,3,4 run_arcsin <- coseq(object=countmat, K=2:4, iter=5, transformation="arcsin", model="Normal", seed=12345) run_arcsin ## Plot and summarize results plot(run_arcsin) summary(run_arcsin) ## Compare ARI values for all models (no plot generated here) ARI <- compareARI(run_arcsin, plot=FALSE) ## Compare ICL values for models with arcsin and logit transformations run_logit <- coseq(object=countmat, K=2:4, iter=5, transformation="logit", model="Normal") compareICL(list(run_arcsin, run_logit)) ## Use accessor functions to explore results clusters(run_arcsin) likelihood(run_arcsin) nbCluster(run_arcsin) ICL(run_arcsin) ## Examine transformed counts and profiles used for graphing tcounts(run_arcsin) profiles(run_arcsin) ## Run the K-means algorithm for logclr profiles for K = 2,..., 20 run_kmeans <- coseq(object=countmat, K=2:20, transformation="logclr", model="kmeans") run_kmeans