In bioinfocz/scdrake: A pipeline for droplet-based single-cell RNA-seq data secondary analysis implemented in the drake Make-like toolkit for R language

SC3 {.tabset}

Single-Cell Consensus Clustering (SC3) is a tool for unsupervised clustering of scRNA-seq data. SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach (it calculates clusters for selected numbers of target clusters).

Cluster stability index shows how stable each cluster is across the selected range of ks. The stability index varies between 0 and 1, where 1 means that the same cluster appears in every solution for different k.

r scdrake::create_a_link(cluster_sc3_cluster_stability_plots_file, "**PDF with cluster stability plots**", href_rel_start = fs::path_dir(report_html_file))

The relationships in cluster abundances under different ks are visualized in the clustree plot below. Stable clusters across different ks can be quickly find as straight or little branched vertical lines.

r scdrake::create_a_link(cluster_sc3_clustree_file, "**PDF with clustree**", href_rel_start = fs::path_dir(report_html_file))

r scdrake::format_used_functions(c("SC3::sc3()", "SC3::sc3_plot_cluster_stability()"))

res <- scdrake::generate_dimred_plots_clustering_section(
  dimred_plots_clustering_files, dimred_plots_clustering_united_files, "sc3", "sc3", fs::path_dir(report_html_file), 3
)

bioinfocz/scdrake documentation built on Sept. 19, 2024, 4:43 p.m.