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scStability is a user friendly package to analyse the stability of dimension reduction and creating cell clusters on your own dataset. Handles UMAP or t-SNE for dimension reduction given an input PCA. Handles Louvain or Leiden algorithm for cell cluster assignment.
You can install the development version of scStability like so:
# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?
A basic workflow should follow a simillar structure to the following:
library(scStability)
<- readRDS(filterednormcounts.csv)
filtered_norm_counts
<- CreateSeuratObject(filtered_norm_counts)
seurat_obj
<- RunPCA(seurat_obj)
seurat_obj
# Run the wrapper function to compute the entire scStability pipeline
<- scStability(seurat_obj, n_runs = 100, dr_method = 'umap', clust_method = 'louvain', n_cores = 2)
stability_results
# Print the mean embedding plot with the mean cluster assignments
print(stability_results$plot)
# Look at embedding and cluster stability statistics
print(stability_results$embedding_stats)
print(stability_results$cluster_stats)
A more detailed example workflow which can be ran in sections:
library(scStability)
<- readRDS(filterednormcounts.csv)
filtered_norm_counts
<- CreateSeuratObject(filtered_norm_counts)
seurat_obj
<- RunPCA(seurat_obj)
seurat_obj
# Create a list of n_runs embeddings (recommonded < 300)
<- Seurat::Embeddings(seurat_obj, reduction = "pca")
pca_emb <- createEmb(dr_input = pca_emb, n_runs = 200, method = 'umap', n_cores = 2)
emb_list
# Compare the set of embeddings and look at printed statistics and density plot
<- compareEmb(emb_list, n_cores = 2)
emb_stats
# Manually check the Kendall's tau of each embedding
print(emb_stats$mean_per_embedding)
# Create and compare the cluster assignments on the previously made PCA. Look at printed statistics
<- clustStable(seurat_obj)
clust_stats
# Manually check the mean NMI of each cluster assignment
$per_index_means
clust_stats
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.