Hotspot Algorithm

Purpose

Use the Hotspot algorithm to identify spatial gene co-expression gene sets.

Usage

SDAS coexpress hotspot -i st.h5ad -o outdir --bin_size 100 \
--layer raw_counts \
--selected_genes top5000  \
--moran_path ./moran.csv \
--n_cpus 8 \
--seed 42  \
--fdr_cutoff 0.05 \
--model bernoulli

Input Parameter Description

Parameter	Required	Default	Description
-i / --input	Yes		Stereo-seq h5ad, must contain raw expression matrix
-o / --output	Yes		Output directory
--bin_size	Yes	50	Bin size for resolution (20, 50, 100, 200, cellbin), consistent with input h5ad, required for plotting and calculation
--layer	No		Specify the layer of the raw expression matrix in h5ad (e.g. layers['raw_counts'])
--selected_genes	No	top5000	Selected gene mode : full(all the genes), topn(top n genes in Moran'I index)
--moran_path	No		Path to the precomputed Moran'I index csv file
--n_cpus	No	8	Number of parallel jobs for a speedup on multi-core machines
--seed	No	42	Random seed
--fdr_cutoff	No	0.05	FDR threshold for statistical testing of spatial highly variable genes and co-expression gene sets
--model	No	normal	Test statistc Null model used in Gene and Modules: danb: Depth-Adjusted Negative Binomial,bernoulli: Models probability of detection,normal: Depth-Adjusted Normal,none: Assumes data has been pre-standardized

Output Results Display

Result File	Description
<input_name>_hotspot.module.csv	The result csv of spatial highly variable genes (gene symbol+gene id) corresponding to the co-expression gene set (module)
<input_name>_hotspot.h5ad	h5ad file containing the results of co-expression gene sets (adata.obsm['module_score_hotspot'])
<input_name>_hotspot_module_score_hotspot.png/pdf	Spatial heatmap of gene set scoring for co-expression gene sets
<input_name>_hotspot.all_coex_heatmap.png/pdf	Similarity heatmap of co-expression gene sets
<input_name>_hotspot.moran.csv	If topn is used for calculation, outputs all gene Moran index and P values

• Result csv of co-expression gene sets: <input_name>_hotspot.module.csv, separated by commas. Hotspot output shows the spatial highly variable genes identified and their corresponding co-expression gene sets (modules)

geneid	real_gene_name	FDR	Module
ENSG00000163209	SPRR3	0.0	Module-1
ENSG00000151632	AKR1C2	0.0	Module-1
ENSG00000170345	FOS	0.0	Module-1
ENSG00000164433	FABP5	0.0	Module-1
ENSG00000120129	DUSP1	0.0	Module-1

• Spatial heatmap of gene set scoring for co-expression gene sets <input_name>_hotspot_module_score_hotspot.png/pdf: Visualizes the spatial distribution patterns of all co-expression gene sets (Modules). The color intensity in the figure indicates the expression level of the co-expression gene set.

• Similarity heatmap of co-expression gene sets <input_name>_hotspot.all_coex_heatmap.png/pdf: Shows the similarity clustering relationship between different co-expression gene sets (Modules). The color in the figure indicates the similarity between different co-expression gene sets, with red indicating high similarity.

Result Interpretation

• The co-expression gene sets start from Module1. Module-1/no Module means genes that do not meet the clustering requirements of co-expression gene sets.

Parameter Tuning Suggestions

• If the number of genes in bin20/50 samples is less than 200, or for other special samples, and the identified spatial highly variable genes/co-expression gene sets are few, it is recommended to change the model parameter from normal to bernoulli, and set fdr_cutoff to 0.05.