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saeyslab / spatial_ccc_experiments

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spatial_ccc_experiments

This repository contains experiments with performing spatial ccc. Our current aims are to:

Candidate datasets

Input format

Related issue: #2

AnnData object
 obs: 'celltype'
 obsm: 'spatial'
 layers: 'counts'

How to read in Python:

import anndata as ad

adata = ad.read_h5ad('path/to/file.h5ad')

# Access celltype
adata.obs['celltype']

# Access spatial coordinates
adata.obsm['spatial']

# Access counts
adata.layers['counts']

How to read in R:

library(anndata)

adata <- read_h5ad('path/to/file.h5ad')

# Access celltype
adata$obs[["celltype"]]

# Access spatial coordinates
adata$obsm[["spatial"]]

# Access counts
adata$layers[["counts"]]

Output format

Related issue: #1

Spatial CCC methods typically output one of three formats.

Per-spot prediction scores

Examples: SpatialDM, LIANA+, NICHES

Per slide

That is, one summary of interactions for the whole slide / sample

Examples: COMMOT / MISTy / LIANA+

Per source-target

That is, a summary for the whole slide but with respect to the cell type annotations

Examples: Giotto / CellChatv2

Implement a selection of spatial CCC methods

The goal of the group was to run multiple spatial CCC methods, compare evaluations/visualizations and results. We selected the methods from Armingol et al., 2024.

Tool Data Input Output Synthetic data Experimental Visualizations Benchmarked against Comments
COMMOT Mouse MERFISH, mouse placenta & brain STARMap, mouse hippocampus Slide-seqV2, Visium LR pairs from CellChat DB (Heteromeric/homomeric binding) or manually defined genes, Log1p counts Sparse matrix (in adata.obsp) with interaction scores for cell to cell LR binding Immunostaining of proteins; RNAScope imaging Spatial signaling direction between spots/cells, heatmaps of DGE in CCC CellChat, Giotto, CellPhoneDB v.3 Got output; Easy to set up and run, but took a long time to run; Weird output because every interaction is in separate files
stMLNet Breast cancer ST, seqFISH+, MERFISH, Slide-seq v2, Visium glioma & COVID-19 Counts, location, and cell type annotations; method only provides human DB Ligand-receptors scores for each sender-receiver-target gene combination Data following ligand diffusion model N/A Sankey plots, CCC network, chord diagram NicheNet, CytoTalk, MISTy Ran on the example dataset from their documentation; Tried running on chosen dataset, but the preprocessing is quite slow; Returns folders per cell type pair
SpatialDM Visium intestine, melanoma, seqFISH subventricular zone CellChat DB SVCA (Gaussian process model) simulated ST data Chord diagram, spatial plots for interaction pattern clustering CellChat, Giotto, SpaTalk Ran the global part; The local part, but it's currently error Apparently LIANA+ already has SpatialDM included so we're running this – ran through and much faster, will check the results Initial implementation is pretty slow
SpaTalk Many untargeted and targeted datasets Counts, location, cell type annotation Prioritized ligand-receptors for each sender-receiver pair; enriched pathways Simulated spots for deconvolution N/A Chord diagrams, snakey plots, heatmaps Giotto, SpaOTsc, NicheNet, CytoTalk, CellCall, CellPhoneDB, and CellChat No Seurat v5 compatibility
HoloNet Breast cancer & liver cancer Visium, Slide-seq v2 Strategy of Tang et al., generated spatial patterning of two cell types N/A CCC network overlaid on spatial data NicheNet, SpaTalk N/A N/A N/A
CellNeighborEX seqFISH mouse embryo, slide-seq from mouse embryo, mouse liver, mouse brain Artificial heterotypic spots Cell isolation and sorting with specific markers Spatial visualization of cell neighbor-dependent gene expression N/A N/A N/A N/A
LIANA+ (cellphonedb) Visium, Slide-tags, Spatial metabolite-transcriptome (MALDI-MSI x Visium) - Cell type specific score of communication events (lr_means) Intracellular signalling network, ugly dotplots, heatmaps Slide-tags spatially-informed vs not, classification of malignant spots, classification of conditions Received dotplots and heatmaps for interactions cell-type specific, not spot specific Need to investigate the resulting matrix, didn't yet find the specific ligand-receptor pairs
MEBOCOST scRNAseq Gene expression of enzymes and knowledge repository of enzyme-metabolite-sensor partners (scFEA, COMPASS, HMDB, literature mining) Number of communication events and score (sum of -log10(FDR)) of detected metabolite-sensor communications between a pair of cell types - - - - Got metabolite-abundance estimation, is not comparable to other outputs N/A
NicheCompass seqFISH mouse organogenesis; Slide-seq mouse hippocampus - Circular plot CellCharter; STACI; GraphST Not very easy to run, because we encountered a bug Not very responsive on GitHub N/A
Giotto seqFISH+, merFISH, osmFISH, STARmap, Visium 10X brain & kidney, Slide-seq, t-cyCIF, MIBI, CODEX a gene-by-cell count matrix and the spatial coordinates for the centroid position of each cell score based on adjusted p-value and log2 fold change was used to rank a ligand-receptor pair across all identified cells of interacting cell types Simulated seqFISH+ and spatial pattern N/A All types of plots, GiottoVisuals, GiottoViewer N/A Not very easy to run; Not user friendly; Working running the method
CellChat v2 Visium 10X mouse brain and human fetal intestine Normalized and log-transformed gene-by-cell matrix, spatial coordinates,Metadata (cell type labels and sample names), CellChatDB Ligand, receptor, source, target, interaction and their probabilities, pathways N/A N/A Circular plot, heatmap, spatial plot N/A Result is a single dataframe with predicted interactions and corresponing probabilities; Easy to visualize; Need to try different values for scaling (pairwise) distances (between regions/cells) while computing cell communication probabilities for different spatial technologies

Run all methods on all datasets

Compare outputs

Either visually or with a metric