plotGroupedSamplesDmap {phemd} | R Documentation |
Visualizes diffusion map for network of samples based on square distance matrix (sample-sample pairwise dissimilarity)
plotGroupedSamplesDmap(my_distmat, cluster_assignments, pt_sz = 1, n_dim = 3, pt_label = NULL, cmap = NULL, w = 8, h = 5, scale.y = 1, angle = 40, autosave = FALSE, ...)
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 |
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 |
Requires 'destiny' package
DiffusionMap object containing biological sample embedding and associated metadata
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)