ChatSpatial

# Examples Natural language commands for spatial transcriptomics analysis. --- ## Standard Workflow A typical analysis follows this flow: ``` Load → Preprocess → Analyze → Visualize ``` ### 1. Load Your Data ``` "Load /path/to/spatial_data.h5ad" "Load my Visium data from /path/to/visium_folder" ``` ### 2. Preprocess ``` "Preprocess the data" "Normalize with log transformation and find 2000 variable genes" "Preprocess with SCTransform normalization" ``` ### 3. Analyze Choose your analysis type below. ### 4. Visualize ``` "Show the spatial plot" "Visualize CD3D expression on tissue" "Create a UMAP colored by clusters" ``` --- ## Analysis Types ### Spatial Domains Identify tissue regions and niches. ``` "Identify spatial domains" "Find 7 spatial domains using SpaGCN" "Cluster the tissue into regions with STAGATE" "Use Leiden clustering with resolution 0.5" ``` **Methods**: SpaGCN (default), STAGATE, GraphST, Leiden, Louvain --- ### Cell Type Annotation Assign cell types to spots or cells. ``` "Annotate cell types using the reference dataset" "Transfer labels from reference with Tangram" "Use scANVI for label transfer" "Annotate with marker genes using CellAssign" ``` **Methods**: Tangram, scANVI, CellAssign, scType, SingleR, mLLMCelltype **Requires**: Reference dataset with cell type labels (for transfer methods) --- ### Deconvolution Estimate cell type proportions in each spot. ``` "Deconvolve the spatial data" "Estimate cell type proportions with FlashDeconv" "Use Cell2location for deconvolution" "Run RCTD deconvolution" ``` **Methods**: FlashDeconv (default, fastest), Cell2location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, CARD **Requires**: Reference single-cell dataset with cell type annotations --- ### Spatial Statistics Analyze spatial patterns and autocorrelation. ``` "Analyze spatial autocorrelation" "Calculate Moran's I for marker genes" "Find spatial hotspots with Getis-Ord" "Compute neighborhood enrichment" "Analyze co-occurrence of cell types" ``` **Methods**: Moran's I, Local Moran's I, Geary's C, Getis-Ord Gi*, Ripley's K, neighborhood enrichment, co-occurrence --- ### Spatially Variable Genes Find genes with spatial expression patterns. ``` "Find spatially variable genes" "Identify spatial genes with SpatialDE" "Use SPARK-X to find spatial patterns" ``` **Methods**: SPARK-X (default, fast), SpatialDE --- ### Differential Expression Compare gene expression between groups. ``` "Find marker genes for cluster 0" "Compare gene expression between tumor and normal" "Find differentially expressed genes in domain 3" ``` --- ### Condition Comparison Compare experimental conditions with proper statistics. ``` "Compare treatment vs control across patients" "Find genes differentially expressed between conditions" "Analyze condition effects stratified by cell type" ``` **Requires**: Sample/patient identifiers for pseudobulk analysis --- ### Cell Communication Analyze ligand-receptor interactions. ``` "Analyze cell-cell communication" "Find ligand-receptor interactions with LIANA" "Identify spatial communication patterns" "Which cell types are communicating?" ``` **Methods**: FastCCC (default, fastest), LIANA, CellPhoneDB, CellChat **Requires**: Cell type annotations --- ### RNA Velocity Understand cellular dynamics. ``` "Analyze RNA velocity" "Run scVelo velocity analysis" "Use VeloVI for velocity estimation" ``` **Methods**: scVelo (deterministic/stochastic/dynamical), VeloVI **Requires**: Spliced and unspliced count layers --- ### Trajectory Analysis Infer developmental trajectories. ``` "Infer cellular trajectories" "Calculate pseudotime with Palantir" "Use CellRank for fate mapping" "Compute diffusion pseudotime" ``` **Methods**: CellRank (requires velocity), Palantir, DPT --- ### Pathway Enrichment Find enriched biological pathways. ``` "Perform pathway enrichment analysis" "Find enriched GO terms" "Analyze KEGG pathway enrichment" "Run GSEA on marker genes" ``` **Methods**: ORA (default), GSEA, ssGSEA, Enrichr --- ### CNV Analysis Detect copy number variations. ``` "Detect copy number variations" "Analyze CNV using immune cells as reference" "Find chromosomal alterations in tumor cells" ``` **Methods**: inferCNVpy (default), Numbat **Requires**: Normal cell types as reference --- ### Multi-Sample Integration Combine multiple datasets. ``` "Integrate these three samples" "Remove batch effects with Harmony" "Combine datasets using scVI" ``` **Methods**: Harmony (default), BBKNN, Scanorama, scVI --- ### Spatial Registration Align tissue sections. ``` "Align these two tissue sections" "Register spatial slices for 3D reconstruction" ``` **Methods**: PASTE, STalign --- ## Visualization Examples ### Basic Plots ``` "Show spatial expression of CD3D" "Create UMAP plot" "Plot violin of marker genes by cluster" "Generate heatmap of top markers" ``` ### Deconvolution Results ``` "Show cell type proportions on tissue" "Create pie charts of cell composition" "Visualize dominant cell type per spot" ``` ### Communication Results ``` "Show ligand-receptor dotplot" "Visualize communication network" "Plot top interacting cell types" ``` ### Spatial Statistics ``` "Show neighborhood enrichment heatmap" "Visualize spatial hotspots" "Plot Moran's I results" ``` --- ## Complete Workflows ### Basic Spatial Analysis (5 min) ``` 1. "Load /path/to/visium_data.h5ad" 2. "Preprocess the data" 3. "Identify spatial domains" 4. "Find marker genes for each domain" 5. "Visualize the domains on tissue" ``` ### Deconvolution Workflow (10 min) ``` 1. "Load spatial data from /path/to/spatial.h5ad" 2. "Load reference data from /path/to/reference.h5ad" 3. "Preprocess both datasets" 4. "Deconvolve using the reference" 5. "Show cell type proportions on tissue" ``` ### Cell Communication Workflow (10 min) ``` 1. "Load the spatial data" 2. "Preprocess with clustering" 3. "Annotate cell types" (or use existing annotations) 4. "Analyze cell-cell communication" 5. "Show the communication network" ``` ### Trajectory Workflow (15 min) ``` 1. "Load the data" 2. "Preprocess the data" 3. "Analyze RNA velocity" 4. "Infer trajectories with CellRank" 5. "Visualize velocity streams on tissue" ``` --- ## Tips **Be specific when needed** - General: "Analyze the data" → ChatSpatial chooses defaults - Specific: "Use SpaGCN with 7 domains" → ChatSpatial uses your settings **Chain commands naturally** - "Load the data, preprocess it, and identify spatial domains" **Reference previous results** - "Find markers for the domains we just identified" - "Visualize the deconvolution results" **Ask for help** - "What methods are available for deconvolution?" - "How should I preprocess my data?" --- ## Next Steps - [Concepts](concepts.md) — Understand when to use which method - [Methods Reference](advanced/methods-reference.md) — Full parameter details for all 20 tools - [Troubleshooting](advanced/troubleshooting.md) — Solutions to common issues