High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE
For each ST resolution and scRNA-seq noise level, we estimated the fractional abundance of known cell types in the ST sample via Spatial Seurat, as described in the ‘Estimating cell type fractions’ subsection. We then ran CytoSPACE with the ‘generated cells’ option and with the lapjv solver implemented in Python .
For our benchmarking, we provided DEEPsc with all input cells and spots, with each input matrix CPM normalized and then log transformed via log1p and with genes restricted to those present in both matrices. DEEPsc was run with 50,000 iterations in parallel mode for training as previously describedSpaGE, or Spatial Gene Enhancement using scRNA-seq, is a method for increasing gene coverage in ST measurements by integrating spatial data with higher-coverage scRNA-seq datasets.
To obtain the integration space representations, we followed the standard Harmony protocol. We first merged Seurat objects created from the scRNA-seq and ST count matrices and then applied the standard Seurat processing pipeline of NormalizeData, FindVariableFeatures, ScaleData and RunPCA, all with default parameters. With the resulting Seurat object, we ran Harmony version 0.1 with group.by.vars=‘orig.ident’ and otherwise default parameters.
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