NeST Algorithm

Purpose

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

Usage

SDAS coexpress nest -i st.h5ad -o outdir --bin_size 100 \
--layer raw_counts \
--selected_genes top5000  \
--moran_path ./moran.csv \
--n_cpus 8 \
--seed 42  \
--hotspot_min_size 30  \
--hotspot_min_samples 4 \
--min_cells 100

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
--hotspot_min_size	No	30	single_hotspot: Minimum number of spots/cells to form a single-gene hotspot
--hotspot_min_samples	No	4	single_hotspot: Minimum number of neighboring spots/cells covered by DBSCAN
--min_cells	No	100/30	coexpress_hotspot: Minimum number of spots/cells to form a module in Module QC default: 100 for cellbin/bin20/bin50; 30 for bin100/bin200

Output Results Display

Result File	Description
<input_name>_nest.module.csv	The result csv of spatial highly variable genes (gene symbol+gene id) corresponding to the co-expression gene set (module)
<input_name>_nest.h5ad	h5ad file containing the results of co-expression gene sets (adata.obsm['module_score_nest'])
<input_name>_nest_module_score_nest.png/pdf	Spatial heatmap of gene set scoring for co-expression gene sets
<input_name>_nest.all_coex_hotspots/_nest.all_coex_structure.png/pdf	Spatial location and hierarchical structure of co-expression gene sets
<input_name>_nest.separate_coex_hotspots.png/pdf	Spatial location and gene count of co-expression gene sets
<input_name>_nest.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>_nest.module.csv, separated by commas. NeST output shows the spatial highly variable genes identified and their corresponding co-expression gene sets (modules)

Module	geneid	real_gene_name
Module0	ENSG00000130649	EPAS1
Module0	ENSG00000102882	CHCHD3
Module0	ENSG00000179144	MDGA2

• Spatial heatmap of gene set scoring for co-expression gene sets <input_name>_nest_module_score_nest.png: 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.

• Spatial location and hierarchical structure of co-expression gene sets <input_name>_nest.all_coex_hotspots.png/pdf; <input_name>_nest.all_coex_structure.png/pdf: Shows the hierarchical relationship between different co-expression gene sets (Modules). The color in the figure indicates the spatial regions where different co-expression gene sets are located.

• Spatial location and gene count of co-expression gene sets <input_name>_nest.separate_coex_hotspots.png/pdf: Visualizes the spatial regions where all co-expression gene sets (Modules) are located and the number of genes contained.

Result Interpretation

• The co-expression gene sets start from Module0. 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 co-expression gene sets are few, it is recommended to lower hotspot_min_size to 10.

• If the identified spatial co-expression gene sets are few, it is recommended to lower min_cells to 10.

• If the identified patterns are too fine and "NumPy Unable to allocate X GiB array" error occurs, it is recommended to increase hotspot_min_size and hotspot_min_samples.