plotGroupedSamplesDmap {phemd} | R Documentation

## Plot diffusion map embedding of samples based on distance matrix

### Description

Visualizes diffusion map for network of samples based on square distance matrix (sample-sample pairwise dissimilarity)

### Usage

    plotGroupedSamplesDmap(
      my_distmat,
      cluster_assignments = NULL,
      pt_sz = 1,
      n_dim = 3,
      pt_label = NULL,
      cmap = NULL,
      w = 8,
      h = 5,
      scale.y = 1,
      angle = 40,
      autosave = FALSE,
      ...
    )

### Arguments

`my_distmat` | phemdObj object containing sample names in @snames slot
`cluster_assignments` | Vector containing group assignments for each sample
`pt_sz` | Size of points representing samples in plot (scaling factor)
`n_dim` | Number of dimensions for embedding (either 2 or 3)
`pt_label` | Vector of sample names corresponding to each point (same order as samples in `my_distmat` and `cluster_assignments`)
`cmap` | Vector containing colors by which points should be colored (corresponding to cluster_assignments)
`w` | Width of plot in inches
`h` | Height of plot in inches
`scale.y` | Scaling factor for diffusion map y-axis
`angle` | Rotation factor for diffusion map plot
`autosave` | Boolean denoting whether or not to save output diffusion map
`...` | Additional parameters to be passed to `DiffusionMap` function

### Details

Requires 'destiny' package

### Value

DiffusionMap object containing biological sample embedding and associated metadata

### 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, 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)
    printClusterAssignments(cluster_assignments, my_phemdObj_final, '.', overwrite=TRUE)
    dm <- plotGroupedSamplesDmap(my_EMD_mat, cluster_assignments, pt_sz=2)

[Package phemd version 1.10.0 Index]