Figure 2:

Robust Cell Type Decomposition enables cross-platform learning of cell types.

a) Left: RCTD inputs: a scRNA-seq dataset, annotated by cell type, and a spatial transcriptomics dataset with unknown cell types. Middle: RCTD uses a scRNA-seq reference-based probabilistic model to predict cell types on a single pixel containing a mixture of two cell types (e.g. Bergmann/Purkinje), with unknown cell type proportions. RCTD predicts the maximum likelihood cell type proportions. In doublet mode, RCTD constrains each pixel to contain at most two cell types; alternatively, RCTD can estimate the best fit at a pixel using all cell types. Right: RCTD outputs a spatial map of cell types, with opacity representing the inferred cell type proportion.

b) Scatter plot of measured vs predicted platform effect (by RCTD) for each gene between the single-cell and single-nucleus cerebellum datasets. Line is the identity line. Measured platform effect is calculated as the log₂ ratio of average gene expression between platforms.

c) Confusion matrix for RCTD’s performance on cross-platform (trained on single-nucleus RNA-seq, tested on single-cell RNA-seq) cell type assignments for single cells. Color represents the proportion of the cell type on the x-axis classified as the cell type on the y-axis. The diagonal representing ground truth is boxed in red.