Methods Reference

This page is the canonical human-readable reference for ChatSpatial tool names, method names, common defaults, accepted values, and user-facing parameter behavior. MCP clients expose the full schema for method-specific advanced options.

ChatSpatial's public interface is a set of 20 schema-validated MCP tools. Those tools orchestrate 65 spatial transcriptomics methods across 15 analytical categories. In this page, tool means the MCP entry point you or an AI client can call; method means an algorithm or analysis backend selected through a parameter such as method, analysis_type, plot_type, or subtype.

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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

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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

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preprocess_data

Normalize, filter, and prepare data.

Parameter	Default	Description
normalization	pearson_residuals	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
use_scrublet	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

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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

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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/{data_id}.h5ad)

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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
local_join_count	Group	Yes
centrality	Network	Yes
network_properties	Network	Yes
spatial_centrality	Network	Yes

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find_spatial_genes

Identify spatially variable genes.

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

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identify_spatial_domains

Find tissue domains and spatial niches.

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

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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)
sctype_tissue	None	scType tissue name, required unless custom markers are provided
sctype_db_	None	Local scType database path, or remote URL when explicitly allowed
sctype_custom_markers	None	Custom scType marker sets
sctype_scaled	True	Whether scType should treat the expression matrix as scaled
sctype_allow_remote	False	One-off opt-in to load scType remote R scripts and default marker database
sctype_allow_runtime_r_install	False	One-off opt-in to install missing R packages at runtime

scType remote resources: by default, scType does not load remote R scripts or the remote default marker database. For one-off exploratory runs, pass sctype_allow_remote=true. For production or offline workflows, prefer local R scripts via CHATSPATIAL_SCTYPE_R_DIR and a local sctype_db_ path.

{
  "method": "sctype",
  "sctype_tissue": "Immune system",
  "sctype_allow_remote": true
}

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

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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

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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)

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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

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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

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analyze_enrichment

Gene set enrichment analysis.

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

Databases: GO_Biological_Process, GO_Molecular_Function, GO_Cellular_Component, KEGG_Pathways, Reactome_Pathways, MSigDB_Hallmark, Cell_Type_Markers

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Dynamics

analyze_velocity_data

RNA velocity analysis.

Parameter	Default	Description
method	scvelo	scvelo, velovi
scvelo_mode	stochastic	deterministic, stochastic, dynamical

Requires: spliced and unspliced layers

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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

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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

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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

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register_spatial_data

Align spatial sections.

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

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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, eps, tiff, jpg

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, imputation	Cell proportions
communication	dotplot, tileplot, circle_plot	LR interactions
interaction	—	Spatial LR pairs
trajectory	pseudotime, circular, fate_map, gene_trends, fate_heatmap, palantir	Pseudotime
velocity	stream, phase, proportions, heatmap, paga	RNA velocity
statistics	neighborhood, co_occurrence, ripley, moran, centrality, getis_ord	Spatial stats
enrichment	barplot, dotplot	Pathway results
cnv	heatmap, spatial	CNV results
integration	batch, cluster, highlight	Integration QC

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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