Package index • scooter

All functions

add_gene_class_percent(): Add mitochondrial, ribosomal, and hemoglobin percentages to a Seurat object.
add_seurat_assay(): Add assay to Seurat object.
as_data_frame_seurat(): Function to extract data from Seurat object.
calculate_cluster_expression(): Per-cluster average expression (non-log space).
calculate_cluster_markers(): Calculate cluster markers (versus all other clusters, or pairwise) and plot them.
calculate_cluster_stats(): Per-cluster cell counts and a joined metadata+embeddings table.
calculate_clusters(): Identify clusters of cells by graph-based clustering.
check_identity_column(): Check identity of the Seurat object.
cluster_seurat_object(): Reduce dimensions, cluster, and plot the result.
create_color_vect(): Function to create a color vector.
create_seurat_object(): Create a usable Seurat object from a sample.
differential_expression_per_cluster(): Calculate differentially expressed genes within each subpopulation/cluster
dr_plot_width(): Width for a reduction plot, widened so many legend labels stay legible.
filter_cells(): Filter cells based on the number of genes, counts, and mitochondrial reads.
geneset_score(): Get geneset scores.
get_color_scheme(): Determine the color scheme.
get_dr_point_size(): Determine the point size for reduced dimensions scatter plots (smaller for larger datasets).
get_test_counts_matrix(): Get an example counts matrix.
import_mtx(): Read in 10x Genomics Cell Ranger Matrix Market format data.
initialize_seurat_object(): Create a new Seurat object from a matrix.
integrate_layers(): Integrate the layers of a Seurat object.
integrate_seurat_object(): Integrate a merged Seurat object across batches.
merge_metadata(): Function to merge two metadata tables together.
merge_seurat_objects(): Merge multiple Seurat objects into one.
normalize_counts(): Normalize the counts, select variable features, and scale.
plot_cluster_markers_heatmap(): Heatmap of the top cluster markers, at several top-N cutoffs.
plot_cluster_markers_top(): Plot a gene list in multiple ways: UMAP, dot plot, violin, and per-cluster bar plot.
plot_clusters(): Plot a cluster resolution on tSNE and UMAP.
plot_distribution(): Plot the distribution of specified features/variables.
plot_dr_feature(): Plot a single feature overlaid on a dimensionality reduction.
plot_dr_group(): Scatter plot of a reduction, colored by a grouping variable.
plot_dr_umap_clusters(): Plot a UMAP colored by one or more cluster/grouping variables.
plot_metrics_correlations(): Scatter plots of the per-cell QC metrics against each other.
plot_metrics_distribution(): Violin plots of the per-cell QC metrics.
plot_var_genes_euler(): Euler diagram of the variable genes shared between batches.
plot_var_genes_upset(): UpSet plot of the variable genes shared between batches.
profile_object_size(): Report the size of an object's slots, largest first.
read_counts_file(): Read in Gene Expression and Antibody Capture data from a 10x Genomics Cell Ranger sparse matrix or from a text file.
resolve_seurat_object(): Resolve a Seurat object, given either the object itself or a path to one on disk.
run_dr(): Run dimensionality reduction: pca, tsne, or umap
run_pca(): Run PCA
run_tsne(): Run TSNE
run_umap(): Run UMAP
save_counts(): Save the counts matrix as a single table.
save_dr_plot(): Save a reduction plot, widened to fit its own legend.
save_metadata(): Save the cell metadata and the reduction embeddings as a single table.
set_identity(): Set identity of the Seurat object.
split_layers_by_batch(): Split a Seurat object into per-batch layers and prepare it for integration.
transfer_labels(): Transfer labels from a reference Seurat object
variable_features_by_batch(): Variable features of each batch of a layer-split object.
write_message(): Small function to write to message and to log file.