SPOTlight Algorithm
Purpose
Use SPOTlight for deconvolution-based cell annotation.
Usage
SDAS cellAnnotation spotlight -i st.h5ad -o outdir --reference sc.h5ad --bin_size 20 --label_key annotation2 \
--input_gene_symbol_key _indexInput Parameter Description
-i / --input
Yes
Stereo-seq h5ad, must contain the raw expression matrix
-o / --output
Yes
Output folder
--reference
Yes
Single-cell ref h5ad, must contain the raw expression matrix
--label_key
Yes
Name of the column in single-cell ref h5ad.obs indicating cell type
--bin_size
Yes
Bin size, used to control the size of points in the plot, not used for calculation, e.g., 20, 50, 100, cellbin (equivalent to 20)
--input_layer
No
Layer in Stereo-seq h5ad storing raw counts
--ref_layer
No
Layer in single-cell ref h5ad storing raw counts
--input_gene_symbol_key
No
real_gene_name
Name of the column in Stereo-seq h5ad.var indicating gene symbol (index means using h5ad.var.index)
--ref_gene_symbol_key
No
_index
Name of the column in single-cell ref h5ad.var indicating gene symbol (_index means using h5ad.var.index)
--slice_key
No
sampleID
Name of the column in multi-slice h5ad.obs indicating slice ID, used for plotting
--filter_rare_cell
No
100
The minimum cell count for a cell type to be included
--n_cells
No
100
Number of cells randomly selected per cell type from the single-cell ref for training the SPOTlight model
--n_hvg
No
3000
Number of highly variable genes in the single-cell ref; highly variable genes and marker genes together form the gene set
--auc_threshold
No
0.5
AUC threshold for filtering marker genes of each cell type in the single-cell ref; highly variable genes and marker genes together form the gene set
--norm_sc
No
Whether to use the logNormCounts function to process single-cell ref data
--norm_sp
No
Whether to use the logNormCounts function to process Stereo-seq data
--seed
No
42
Random seed
--n_threads
No
8
Number of threads to use
Output Results
<input_name>_anno_spotlight.csv
Annotation results for each spot, including scores for each cell type
<input_name>_anno_spotlight.h5ad
Input h5ad + annotation results. Scores for each cell type are stored in obsm['anno_score_spotlight'], and the type with the highest score is stored in obs['anno_spotlight']
<input_name>_anno_spotlight.png/pdf
Overall annotation result plot; for multiple slices, one plot per slice; both png and pdf are output
<input_name>_anno_spotlight_split.png/pdf
Separate display plot for each cell type; for multiple slices, one plot per slice; both png and pdf are output
<input_name>_anno_score_spotlight.png/pdf
Score plot for each cell type; for multiple slices, one plot per slice; both png and pdf are output
For detailed explanations and specific result displays, please refer to the following link (cell2location algorithm → cell annotation → output results).
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