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Basic Functions

Core workflow functions located in R/basic.R

read_sc_data()
Read single-cell data into a Seurat object
seurat_standard_normalize_and_scale()
Standard seurat normalize_and_scale
seurat_basic_qc()
Seurat basic QC
plot_qc()
Plot QC metrics
doublet_removal()
Remove doublets using DoubletFinder
doublet_rate_dictionary()
Lookup expected doublet rate based on cells loaded
seurat_basic_info()
get basic summary of a seurat object
remove_unwant_hvg()
Remove unwanted HVGs
subset_srt()
Subset srt obj easily
write_10x_triple()
Export Seurat object to 10x-style triple files
metadata_write_10x()
Export Seurat meta.data aligned to barcodes

Metadata Management

Metadata manipulation functions located in R/metadata.R

metadata_get_colnames()
Get specified metadata columns from a Seurat object
metadata_keep()
Keep only selected metadata columns in a Seurat object
metadata_drop()
Drop specified metadata columns from a Seurat object

Cell Annotation & Markers

Marker identification and annotation utilities located in R/annotation.R

format_markers_for_upload()
Format marker-gene lists for (bulk) upload
sort_string_numeric_clusters()
Sort string-based cluster labels numerically and refactor
get_cluster_counts()
Get cluster counts sorted descending
filter_clusters_by_percent_or_cell_count()
Filter Seurat clusters by percentage or absolute cell count
calcROGUE()
Calculate and plot ROGUE index for a Seurat object, with inline filtering
score_signature()
Score sc_obj with signature list
plot_score_signature_heatmap()
Plot heatmap of signature scores
locate_most_different_g_in_2_group()
Locate most distinguishing markers between two clusters
gimme_marker()
Give me marker!

Dictionaries & Reference Data

Built-in markers and reference data located in R/dict.R

leo.marker
Marker hub

Celltype & Cluster analysis

Advanced cell type analysis located in R/celltype.R

ROIE()
Calculate ROIE
leo.ROIE()
Compute Ro/e and draw heatmap
leo.augur()
Run Augur analysis
leo.milo()
Run Milo differential abundance workflow on a Seurat object
leo_milo_vis()
Visualize MiloR DA results

In-silico Knockout

Virtual gene perturbation simulations located in R/sko.R

silico_ko()
In-silico Knock-Out / Knock-In Analysis (single gene)

Multi-batch Processing

Functions for handling multi-batch datasets located in R/multi_batch.R

Visualizations

Generic plotting utilities located in R/sc.plot.R

plot_alluvial()
Draw alluvial bars with optional custom palette
plot_alluvial_sc()
Alluvial plot from a Seurat object
plot_gw_density()
Plot gene-weighted density
plot_highlight_cluster()
Highlight a cluster
plot_dbee()
Different-effect-variable Beeswarm Plot