SWARM

This is the Github-Repository of the MoPiTas Project in the Master Practical course of DaisyBioLab 2025.

Dataset-Table

https://docs.google.com/spreadsheets/d/1RH_lB4OEUEgZMIYu59_RTwcPmkC-ciyApEnlhvPSJ7E/edit?usp=sharing

Overview

Before Starting SWARM

Example Data

Use the download_example_data.sh Script to autimatically download and move the example data to the right places.

Backend

Start the backend development server:

python backend/main.py

Frontend

Start the frontend development server:

cd frontend
npm start

OR use

bash launch.sh

Format Requirements

The web-tool is working with the anndata-format. This is a tutorial on how counts and scores should be saved.

• Counts
  • Stored in anndata.X
• Observations (per Spot)
  • Stored in anndata.obs (all observation columns will be shown, so make sure to clean your anndata before upload).
• Clustering
  • Save the Leiden clustering for Leiden-based scores in anndata.obs["leiden"]
• Gene-wise scores
  • Moran's I: stored in anndata.uns["moranI"] as DataFrame with columns:
    • I
    • pval_norm
    • var_norm
    • pval_norm_fdr_bh
  • Geary's C: stored in anndata.uns["gearyC"] as DataFrame
    • C
    • pval_norm
    • var_norm
    • pval_norm_fdr_bh
• Leiden cluster scores
  • Leiden centrality: stored in anndata.uns["leiden_centrality_scores"] as DataFrame with columns:
    • degree_centrality
    • average_clustering
    • closeness_centrality
  • Leiden co-occurrence: stored in anndata.uns["leiden_co_occurrence"] as dictionary with entries:
    • intervals: interval boundaries, array of shape (n_intervals - 1,)
    • occ: 3D array of shape (n_clusters, n_clusters, n_intervals) (Make sure to select the right num_intervals in Upload form)
  • Leiden neighborhood enrichment: stored in anndata.uns["leiden_nhood_enrichment"] as dictionary with entries:
    • counts: array of shape (n_clust, n_clust)
    • zscore: array of shape (n_clust, nclust)
• Regulon scores
  • Gene sets: stored in anndata.uns["genie_genesets"] and anndata.uns["sponge_genesets"] as dictionaries with format:
    • regulator: [gene1, gene2, ..., geneN]
  • AUCell: stored in anndata.obsm["aucell_scores_{sponge|genie3}"] as DataFrames with regulators as columns.
  • GSVA: stored in anndata.obsm["spongeffects_GSVA_scores_{sponge|genie3}"] as DataFrames with regulators as columns.
  • ssGSEA: stored in anndata.obsm["spongeffects_ssGSEA_scores_{sponge|genie3}"] as DataFrames with regulators as columns.
  • Viper: stored in anndata.obsm["viper_scores_genie3"] as DataFrame with regulators as columns.
• Tangram
  • Cell type compositions: stored in anndata.obsm["tangram_ct_pred"] as DataFrame with cell types as columns.
• LIANA+ scores
  • Ligand receptor relationships:
    • Cosine similarity: stored in anndata.obsm["ligand_receptor_cosine_similarity"] as array of shape (n_spots, n_interactions)
    • P-value: stored in anndata.obsm["ligand_receptor_p_value"] as array of shape (n_spots, n_interactions)
    • Category: stored in anndata.obsm["ligand_receptor_category"] as array of shape (n_spots, n_interactions)
    • NMF factors: stored in anndata.obsm["ligand_receptor_NMF_factors"] as DataFrame with factors as columns.
    • Global scores: stored in anndata.uns["ligand_receptor_global_scores"] as DataFrame with columns:
      • cosine_similarity_mean
      • cosine_similarity_std
      • ligand_receptor_morans: Moran's R score
    • Interaction names: stored in anndata.uns["liana_columns"]["ligand_receptor"] as array of shape (n_interactions,)
  • Cell type composition - TF activity similarity:
    • Cosine similarity: stored in anndata.obsm["cell_comp_tf_activity_cosine_similarity"] as array of shape (n_spots, n_interactions)
    • Category: stored in anndata.obsm["cell_comp_tf_activity_category"] as array of shape (n_spots, n_interactions)
    • Global scores: stored in anndata.uns["cell_comp_tf_activity_global_scores"] as DataFrame with columns:
      • cosine_similarity_mean
      • cosine_similarity_std
    • Interaction names: stored in anndata.uns["liana_columns"]["cell_comp_tf_activity"] as array of shape (n_interactions,)
  • TF activity:
    • ULM score: stored in adata.obsm["tf_activity_score_ulm"] as DataFrame with TFs as columns.
    • ULM p-adjusted value: stored in adata.obsm["tf_activity_padj_ulm"] as DataFrame with TFs as columns.
  • Pathway activity:
    • MLM score: stored in adata.obsm["pathway_activity_score_mlm"] as DataFrame with pathways as columns.
    • MLM p-adjusted value: stored in adata.obsm["pathway_activity_padj_mlm"] as DataFrame with pathways as columns.

We furthermore use GRNs computed using sponge and genie3, both CSV-formatted.

Genie:

regulatoryGene,targetGene,weight
FOXM1,KIF20A,0.06364686682258

Sponge:

geneA,geneB,df,cor,pcor,mscor,p.val,p.adj
ENSG00000182141,ENSG00000258630,1,0.121684631317227,0.0764637550413806,0.0452208762758468,0.029426,0.519790968019617