StatQuest Sc Tl Dendrogram
scanpy.pl.dendrogram — scanpy sc.tl.dendrogram no longer(?) works in backed mode · Issue #3199 Scanpy.tl.rank_genes_groups, layer= does not appear to be working Visualizing marker genes — Scanpy documentation sc.tl.dendrogram(adata, groupby='consensus_clusters', use_rep="X_scVI") sc.tl.rank_genes_groups(adata, 'consensus_clusters', method The main ideas behind PCA are actually super simple and that means it's easy to interpret a PCA plot: Samples that are correlated Examples. >>> import scanpy as sc >>> adata = sc.datasets.pbmc68k_reduced() >>> sc.tl.dendrogram(adata, groupby="bulk_labels") >>> sc.pl.dendrogram Running `sc.tl.dendrogram` with default parameters. For fine tuning it is recommended to run `sc.tl.dendrogram` independently. using 'X_pca' with n_pcs = 50 ...