groupSamples {phemd} | R Documentation |
Takes sample-sample distance matrix as input and returns group assignments for each sample
groupSamples( distmat, distfun = "hclust", ncluster = NULL, method = "complete", ... )
distmat |
A distance matrix of dimension num_samples x num_samples representing pairwise dissimilarity between samples |
distfun |
Method of partitioning network of samples (currently either 'hclust' or 'pam') |
ncluster |
Optional parameter specifying total number of sample groups |
method |
Optional parameter for hierarchical clustering (see "hclust" documentation) |
... |
Optional additional parameters to be passed to diffusionKmeans method |
By default, uses 'kgs' (Kelley-Gardner-Sutcliffe) method for determining optimal number of groups. Alternatively, can take user-specified number of groups). Requires 'cluster' and 'maptree' packages.
Vector containing group assignments for each sample (same order as row-order of distmat) based on user-specified partitioning method (e.g. hierarchical clustering)
my_phemdObj <- createDataObj(all_expn_data, all_genes, as.character(snames_data)) my_phemdObj_lg <- removeTinySamples(my_phemdObj, 10) my_phemdObj_lg <- aggregateSamples(my_phemdObj_lg, max_cells=1000) my_phemdObj_monocle <- embedCells(my_phemdObj_lg, cell_model = 'monocle2', data_model = 'gaussianff', sigma=0.02, maxIter=2) my_phemdObj_monocle <- orderCellsMonocle(my_phemdObj_monocle) my_phemdObj_final <- clusterIndividualSamples(my_phemdObj_monocle) my_phemdObj_final <- generateGDM(my_phemdObj_final) my_EMD_mat <- compareSamples(my_phemdObj_final) cluster_assignments <- groupSamples(my_EMD_mat, distfun = 'hclust', ncluster=4)