groupSamples {phemd} | R Documentation

## Performs community detection on sample-sample distance matrix to identify groups of similar samples

### Description

Takes sample-sample distance matrix as input and returns group assignments for each sample

### Usage

    groupSamples(
      distmat,
      distfun = "hclust",
      ncluster = NULL,
      method = "complete",
      ...
    )

### Arguments

`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

### Details

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.

### Value

Vector containing group assignments for each sample (same order as row-order of distmat) based on user-specified partitioning method (e.g. hierarchical clustering)

### Examples

    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)

[Package phemd version 1.8.0 Index]