Sparse deconvolution of cell type medleys in spatial transcriptomics
Fig 5
Predictive performance evaluation on real heart data with selected cell types.
Top: Predictions for ventricular cardiomyocytes. Bottom: Predictions for outflow tract cells. Blue denotes unrealistic negative predictive coefficients, while the red gradient represents the cell type percentages in the corresponding capture spots. A and G, DWLS predictions reveal numerous spots with unrealistic negative coefficients. Biologically incorrect positive coefficients are also evident, particularly around the atria and outflow tract, especially for ventricular cardiomyocyte cell prediction. B and H, RCTD shows a high abundance of false-positive predictions for ventricular cardiomyocyte cells. For the outflow tract, four spots represent false-positive predictions at a low percentage. C and I, S-DWLS exhibits improved predictions; however, false-positive predictions persist for both ventricular cardiomyocytes and the outflow tract. One spot shows a negative coefficient in C. D and J, SPOTlight predictions indicate overpredicted capture spots in the corresponding tissue. E and K, Stereoscope also overpredicts cells belonging to ventricular cardiomyocytes. Moreover, cells for the outflow tract are incorrectly predicted in the epicardial zone of the heart. F and L, WISpR predictions accurately emphasize abundant ventricular cardiomyocytes and outflow tract cells in associated spots. M, WISpR estimates an average of cell types per spot, aligning with expected spatial complexity. N–O, WISpR has the lowest positive spots for ventricular cardiomyocytes (n = 119) and one of the lowest for outflow tract (n = 33), and achieves the highest precision (0.92 and 0.88) across validated regions. P, WISpR and S-DWLS show the most highest proportion estimates for dominant cell types in biologically validated region. R, WISpR demonstrates the lowest off-target assignment rates, confirming its sparsity-aware design minimizes overfitting and enhances biological fidelity.