Methods Reference¶

All 20 ChatSpatial tools with parameters and options.

Quick Reference¶

Category	Tools
Data	`load_data`, `preprocess_data`, `compute_embeddings`, `export_data`, `reload_data`
Spatial	`analyze_spatial_statistics`, `find_spatial_genes`, `identify_spatial_domains`
Cells	`annotate_cell_types`, `deconvolve_data`, `analyze_cell_communication`
Genes	`find_markers`, `compare_conditions`, `analyze_enrichment`
Dynamics	`analyze_velocity_data`, `analyze_trajectory_data`, `analyze_cnv`
Multi-sample	`integrate_samples`, `register_spatial_data`
Output	`visualize_data`

Data Management¶

load_data¶

Load spatial transcriptomics data.

Parameter	Type	Description
`data_path`	str	Path to file or folder
`data_type`	str	`visium`, `xenium`, `slide_seq`, `merfish`, `seqfish`, `generic`
`name`	str	Optional dataset name

Supported formats: H5AD, 10X Visium folders, H5, MTX

preprocess_data¶

Normalize, filter, and prepare data.

Parameter	Default	Description
`normalization`	`log`	`log`, `sct`, `pearson_residuals`, `scvi`, `none`
`n_hvgs`	2000	Highly variable genes
`n_pcs`	30	Principal components
`n_neighbors`	15	Neighbor graph
`clustering_resolution`	1.0	Leiden clustering
`filter_genes_min_cells`	3	Min cells per gene
`filter_cells_min_genes`	30	Min genes per cell
`filter_mito_pct`	20.0	Max mitochondrial %
`scale`	False	Scale to unit variance before PCA

Advanced options:

Parameter	Default	Description
`scrublet_enable`	False	Enable doublet detection (for single-cell resolution data)
`normalize_target_sum`	None	Target counts per cell (None=median, 1e4=Visium, 1e6=MERFISH)
`remove_mito_genes`	True	Exclude mito genes from HVG
`batch_key`	`batch`	Batch column for batch-aware normalization

compute_embeddings¶

Compute dimensionality reduction and clustering.

Parameter	Default	Description
`compute_pca`	True	Compute PCA
`compute_umap`	True	Compute UMAP
`compute_clustering`	True	Leiden clustering
`compute_spatial_neighbors`	True	Spatial graph
`n_pcs`	30	Principal components
`clustering_resolution`	1.0	Clustering resolution
`force`	False	Recompute if exists

export_data / reload_data¶

Export dataset for external scripts, reload after modifications.

Parameter	Default	Description
`data_id`	required	Dataset ID
`path`	auto	Custom path (default: `~/.chatspatial/active/`)

Spatial Analysis¶

analyze_spatial_statistics¶

Analyze spatial patterns and autocorrelation.

Parameter	Default	Description
`analysis_type`	`neighborhood`	See types below
`cluster_key`	None	Required for group-based analyses
`genes`	None	Specific genes to analyze
`n_top_genes`	20	Top HVGs to analyze (if genes not specified)
`n_neighbors`	8	Spatial neighbors

Analysis types:

Type	Category	Requires cluster_key
`moran`	Gene	No
`local_moran`	Gene	No
`geary`	Gene	No
`getis_ord`	Gene	No
`bivariate_moran`	Gene	No
`neighborhood`	Group	Yes
`co_occurrence`	Group	Yes
`ripley`	Group	Yes
`join_count`	Group	Yes
`centrality`	Network	Optional

find_spatial_genes¶

Identify spatially variable genes.

Parameter	Default	Description
`method`	`sparkx`	`sparkx`, `flashs`, `spatialde`
`n_top_genes`	None	Top genes to return (None = all significant)

identify_spatial_domains¶

Find tissue domains and spatial niches.

Parameter	Default	Description
`method`	`spagcn`	`spagcn`, `stagate`, `graphst`, `leiden`, `louvain`
`n_domains`	7	Expected number of domains
`resolution`	0.5	Clustering resolution

Cell Analysis¶

annotate_cell_types¶

Assign cell types.

Parameter	Default	Description
`method`	`tangram`	See methods below
`reference_data_id`	None	Reference dataset (for transfer methods)
`cell_type_key`	None	Cell type column in reference
`marker_genes`	None	Marker dict (for CellAssign)

Methods:

Method	Requires Reference	Notes
`tangram`	Yes	Spatial mapping
`scanvi`	Yes	Deep learning transfer
`cellassign`	No	Marker-based
`sctype`	No	Automatic (R)
`singler`	No	Reference-based (R)
`mllmcelltype`	No	LLM-based

deconvolve_data¶

Estimate cell type proportions per spot.

Parameter	Default	Description
`method`	`flashdeconv`	See methods below
`reference_data_id`	required	Reference dataset
`cell_type_key`	required	Cell type column in reference

Methods:

Method	Speed	GPU	Notes
`flashdeconv`	Fast	No	Default, recommended
`cell2location`	Slow	Yes	High accuracy
`rctd`	Fast	No	R-based
`destvi`	Medium	Yes	scvi-tools
`stereoscope`	Slow	Yes	Alternative DL
`tangram`	Medium	Yes	Spatial mapping
`spotlight`	Fast	No	R-based
`card`	Fast	No	R-based, imputation

analyze_cell_communication¶

Analyze ligand-receptor interactions.

Parameter	Default	Description
`method`	`fastccc`	`fastccc`, `liana`, `cellphonedb`, `cellchat_r`
`species`	required	`human`, `mouse`, `zebrafish`
`cell_type_key`	required	Cell type column
`liana_resource`	`consensus`	LR database (`mouseconsensus` for mouse)

Gene Analysis¶

find_markers¶

Find differentially expressed genes.

Parameter	Default	Description
`group_key`	required	Grouping column
`group1`	None	First group (None = each vs rest)
`group2`	None	Second group
`method`	`wilcoxon`	`wilcoxon`, `t-test`, `t-test_overestim_var`, `logreg`, `pydeseq2`
`n_top_genes`	50	Top genes per group

compare_conditions¶

Compare experimental conditions (pseudobulk DESeq2).

Parameter	Default	Description
`condition_key`	required	Condition column
`condition1`	required	Treatment group
`condition2`	required	Control group
`sample_key`	required	Sample/patient column
`cell_type_key`	None	Stratify by cell type
`n_top_genes`	50	Top DEGs

analyze_enrichment¶

Gene set enrichment analysis.

Parameter	Default	Description
`species`	required	`human`, `mouse`, `zebrafish`
`method`	`pathway_ora`	`pathway_ora`, `pathway_gsea`, `pathway_ssgsea`, `spatial_enrichmap`
`gene_set_database`	`GO_Biological_Process`	See databases below

Databases: GO_Biological_Process, GO_Molecular_Function, KEGG_Pathways, Reactome_Pathways, MSigDB_Hallmark

Dynamics¶

analyze_velocity_data¶

RNA velocity analysis.

Parameter	Default	Description
`method`	`scvelo`	`scvelo`, `velovi`
`mode`	`stochastic`	`deterministic`, `stochastic`, `dynamical`

Requires: spliced and unspliced layers

analyze_trajectory_data¶

Trajectory and pseudotime inference.

Parameter	Default	Description
`method`	`cellrank`	`cellrank`, `palantir`, `dpt`
`root_cells`	None	Starting cells

Note: CellRank requires velocity data

analyze_cnv¶

Copy number variation detection.

Parameter	Default	Description
`method`	`infercnvpy`	`infercnvpy`, `numbat`
`reference_key`	required	Cell type column
`reference_categories`	required	Normal cell types

Multi-Sample¶

integrate_samples¶

Batch integration.

Parameter	Default	Description
`data_ids`	required	List of dataset IDs
`method`	`harmony`	`harmony`, `bbknn`, `scanorama`, `scvi`
`batch_key`	`batch`	Batch column

register_spatial_data¶

Align spatial sections.

Parameter	Default	Description
`source_id`	required	Source dataset
`target_id`	required	Target dataset
`method`	`paste`	`paste`, `stalign`

Visualization¶

visualize_data¶

Create all plot types.

Parameter	Default	Description
`plot_type`	`feature`	See types below
`subtype`	None	Visualization variant
`feature`	None	Gene(s) or column to show
`basis`	`spatial`	`spatial`, `umap`
`cluster_key`	None	Grouping column
`colormap`	`coolwarm`	Color scheme
`dpi`	300	Resolution
`output_format`	`png`	`png`, `pdf`, `svg`

Plot types and subtypes:

Type	Subtypes	Use
`feature`	—	Gene/metadata on spatial or UMAP
`expression`	`heatmap`, `violin`, `dotplot`, `correlation`	Aggregated expression
`deconvolution`	`spatial_multi`, `pie`, `dominant`, `diversity`, `umap`	Cell proportions
`communication`	`dotplot`, `tileplot`, `circle_plot`	LR interactions
`interaction`	—	Spatial LR pairs
`trajectory`	`pseudotime`, `fate_map`, `gene_trends`	Pseudotime
`velocity`	`stream`, `phase`, `paga`	RNA velocity
`statistics`	`neighborhood`, `co_occurrence`, `ripley`, `moran`	Spatial stats
`enrichment`	`barplot`, `dotplot`	Pathway results
`cnv`	`heatmap`, `spatial`	CNV results
`integration`	`batch`, `cluster`	Integration QC

GPU Acceleration¶

Set use_gpu=True for these methods:

Category	Methods
Preprocessing	scVI normalization
Annotation	Tangram, scANVI
Deconvolution	Cell2location, DestVI, Stereoscope, Tangram
Domains	STAGATE, GraphST
Velocity	VeloVI
Integration	scVI
CNV	inferCNVpy

Next Steps¶

Examples — See methods in action
Concepts — When to use which method
Troubleshooting — Common issues and fixes