plotGroupedSamplesDmap: Plot diffusion map embedding of samples based on distance...

In wschen/phemd: Phenotypic EMD for comparison of single-cell samples

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)

wschen/phemd documentation built on April 8, 2023, 6:27 a.m.