1. Clustering Simulated Data (Figure 1)

suppressPackageStartupMessages({
library(splatter)
library(Seurat)
library(SeuratObject)

library(patchwork)
library(grid)

library(callback)

library(callbackreproducibility)
})

Simulated Data With One True Group

First, we simulate data using splatter and convert the data format to a SeuratObject. Setting group.prob = c(1.0) results in a single group.

sim.groups <- splatter::splatSimulate(group.prob = c(1.0), method = "groups",
                                      verbose = FALSE,
                                      nGenes = 1000,
                                      batchCells = 1000,
                                      dropout.type = "experiment", 
                                      de.prob = 0.05)

seurat_obj <- Seurat::as.Seurat(sim.groups, counts = "counts", data = NULL)
seurat_obj <- SeuratObject::RenameAssays(object = seurat_obj, originalexp = 'RNA')

seurat_obj@meta.data$Group = 1

Then, we run a typical analysis using Seurat and also cluster using callback.

seurat_obj <- callback::seurat_workflow(seurat_obj, num_variable_features = 1000, resolution_param = 0.8)

seurat_obj <- callback::FindClustersCallback(seurat_obj, cores=6)

Finally, we plot a UMAP of the results including the true labels (only one in this case), the clusters found by Seurat with default parameters, and the clusters found by callback.

simulation_one_group_scatter <- callbackreproducibility::custom_scatter(seurat_obj, reduction = "umap", group_by = "Group", x_title = "UMAP 1", y_title = "UMAP 2", pt.size = 6) + Seurat::NoLegend()
simulation_one_group_seurat_scatter <- callbackreproducibility::custom_scatter(seurat_obj, reduction = "umap", group_by = "seurat_clusters", x_title = "UMAP 1", y_title = "UMAP 2", pt.size = 6) + Seurat::NoLegend() 
simulation_one_group_callback_scatter <- callbackreproducibility::custom_scatter(seurat_obj, reduction = "umap", group_by = "callback_clusters", x_title = "UMAP 1", y_title = "UMAP 2", pt.size = 6) + Seurat::NoLegend()

ggplot2::ggsave("one_group_scatter.png", simulation_one_group_scatter, width = 1.2 * 2^12, height = 2^12, units = "px")
ggplot2::ggsave("one_group_seurat_scatter.png", simulation_one_group_seurat_scatter, width = 1.2 * 2^12, height = 2^12, units = "px")
ggplot2::ggsave("one_group_callback_scatter.png", simulation_one_group_callback_scatter, width = 1.2 * 2^12, height = 2^12, units = "px")

True Groups	Seurat Clusters	callback Clusters

Simulated Data With Three True Groups

First, we simulate data using splatter and convert the data format to a SeuratObject. Setting group.prob = c(0.6, 0.2, 0.2) results in three groups in proportions of 60%, 20%, and 20%.

sim.groups <- splatter::splatSimulate(group.prob = c(0.6, 0.2, 0.2), method = "groups",
                            verbose = FALSE,
                            nGenes = 1000,
                            batchCells = 4000,
                            dropout.type = "experiment", 
                            de.prob = 0.05)

seurat_obj <- Seurat::as.Seurat(sim.groups, counts = "counts", data = NULL)
seurat_obj <- SeuratObject::RenameAssays(object = seurat_obj, originalexp = 'RNA')

Then, we run a typical analysis using Seurat and also cluster using callback.

seurat_obj <- callback::seurat_workflow(seurat_obj, num_variable_features = 1000, resolution_param = 0.8)

seurat_obj <- callback::FindClustersCallback(seurat_obj, cores=6)

We plot a UMAP of the results including the true labels, the clusters found by Seurat with default parameters, and the clusters found by callback.

simulation_three_groups_group_scatter <- callbackreproducibility::custom_scatter(seurat_obj, reduction = "umap", group_by = "Group", x_title = "UMAP 1", y_title = "UMAP 2", pt.size = 2) + Seurat::NoLegend()
simulation_three_groups_seurat_scatter <- callbackreproducibility::custom_scatter(seurat_obj, reduction = "umap", group_by = "seurat_clusters", x_title = "UMAP 1", y_title = "UMAP 2", pt.size = 2) + Seurat::NoLegend() 
simulation_three_groups_callback_scatter <- callbackreproducibility::custom_scatter(seurat_obj, reduction = "umap", group_by = "callback_clusters", x_title = "UMAP 1", y_title = "UMAP 2", pt.size = 2) + Seurat::NoLegend()



ggplot2::ggsave("three_group_scatter.png", simulation_three_groups_group_scatter, width = 1.2 * 2^12, height = 2^12, units = "px")
ggplot2::ggsave("three_group_seurat_scatter.png", simulation_three_groups_seurat_scatter, width = 1.2 * 2^12, height = 2^12, units = "px")
ggplot2::ggsave("three_group_callback_scatter.png", simulation_three_groups_callback_scatter, width = 1.2 * 2^12, height = 2^12, units = "px")

True Groups	Seurat Clusters	callback Clusters

Finally, we plot the grid of UMAPs.

column_label_1 <- wrap_elements(panel = textGrob('True Labels', gp = gpar(fontsize = 64)))
column_label_2 <- wrap_elements(panel = textGrob('Seurat Default', gp = gpar(fontsize = 64)))
column_label_3 <- wrap_elements(panel = textGrob('callback', gp = gpar(fontsize = 64, fontfamily = "Courier")))


umap_grid <- column_label_1 + column_label_2 + column_label_3 +
  simulation_one_group_scatter + simulation_one_group_seurat_scatter + simulation_one_group_callback_scatter +
  column_label_1 + column_label_2 + column_label_3 +
  simulation_three_groups_group_scatter + simulation_three_groups_seurat_scatter + simulation_three_groups_callback_scatter +
  plot_layout(widths = c(5, 5, 5),
              heights = c(1,3,1,3))

ggplot2::ggsave("umap_grid.png", umap_grid, width = 2 * 1.5 * 2^12, height = 2 * 2^12, units = "px")