Table 3 |.
Single-cell and spatial integration strategies
| Algorithm | Strategy type | Recommended data | Output | Refs |
|---|---|---|---|---|
| Deconvolution | ||||
| SPOTlight | Non-negative least squares regression | scRNA-seq and spatial barcoding | Estimated cell-type proportions for each capture spot | 91 |
| SpatialDWLS | Dampened weighted least squares regression | scRNA-seq and spatial barcoding | Estimated cell-type proportions for each capture spot | 102 |
| stereoscope | Probabilistic modelling: negative binomial distribution | scRNA-seq and spatial barcoding | Estimated cell-type proportions for each capture spot based on MAP estimation | 92 |
| Robust cell-type decomposition (RCTD) | Probabilistic modelling: Poisson distribution | scRNA-seq and spatial barcoding | Estimated cell-type proportions for each capture spot based on MAP estimation | 90 |
| cell2location | Probabilistic modelling: negative binomial distribution | scRNA-seq and spatial barcoding | Estimated cell-type proportions for each capture spot on MAP estimation | 103 |
| Multimodal intersection analysis (MIA) | Enrichment analysis | Cell type-specific genes from scRNA-seq and region-specific genes from spatial barcoding | Enrichment or depletion values for certain scRNA-seq cell types in tissue regions annotated from H&E of spatial barcoding data | 34 |
| Relative expression scoring | Deconvolution and mapping (enrichment analysis) | scRNA-seq (for cell subtypes) applicable to spatial barcoding and HPRI | Relative cell-type expression scores for each capture spot or single cell | 62,66 |
| Mapping | ||||
| pciSeq | Probabilistic model: variation Bayesian mean-field approximation | scRNA-seq and HPRI | Pie chart of cell-type assignment probabilities for each cell | 89 |
| Harmony | Principal component analysis followed by k-means clustering | scRNA-seq and HPRI | Each cell in the HPRI data is assigned a cell type derived from scRNA-seq based on the shared clusters | 115 |
| LIGER | Integrative non-negative matrix factorization followed by shared factor neighbourhood graph clustering | scRNA-seq and HPRI | Each cell in the HPRI data is assigned a cell type derived from scRNA-seq based on the shared clusters; gene expression of spatially resolved single cells is imputed through average of nearest scRNA-seq neighbours in aligned factor space | 113 |
| Seurat Integration | Canonical correlation analysis followed by mutual nearest neighbour clustering | scRNA-seq and HPRI | Cell type for HPRI cell is assigned based on anchor’s cell type; gene expression of each HPRI cell is imputed through its anchored scRNA-seq pair | 114 |
| SpaGE | Principal component analysis followed by k-nearest-neighbour clustering | scRNA-seq and HPRI | Gene expression of each HPRI cell is imputed through k-nearest-neighbour regression | 117 |
| Spatially informed ligand-receptor analysis | ||||
| Fawkner-Corbett et al. (2021) | Fit model to test whether receptor expression is dependent on ligand expression | scRNA-seq and spatial barcoding | p value and coefficient used to determine whether the pair spatially co-localizes; output can be generated for within individual capture spots or between adjacent capture spots | 26 |
| Giotto | Proximity of ligand-receptor co-expression | HPRI or spatial barcoding | Cell-cell communication score for every pair of cell types | 128 |
| SpaOTsc | Optimal transport problem and downstream signalling targets | scRNA-seq and HPRI | Cell-cell signalling map in 2D or 3D | 129 |
| SVCA (spatial variance component analysis) | Gaussian mixture model that considers three factors: intrinsic, environmental and cell-cell interaction | HPRI | Quantifies how much variance for each gene can be explained by cell-cell interactions (among other factors) | 133 |
Listed on the deconvolution to mapping spectrum (FIG. 4). Also includes the main spatially informed intercellular communication algorithms. H&E, haematoxylin & eosin; HPRI, high-plex RNA imaging; MAP, maximum a posteriori; scRNA-seq, single-cell RNA sequencing.