BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
Fig 3
BarDensr outperforms the existing methods by discovering more rolonies.
(A) Performance of BarDensr on simulated data. What percentage of rolonies are correctly detected? We use the Receiver Operating Characteristic curve (ROC curve) to look at this percentage (the complement of False Negative Rates, or 1-FNR) as a function of the tolerated False Positive Rate (FPR), for BarDensr (red), starfish (orange), Single Round Matching (SRM, green), as well as the correlation-based method (‘corr’, gray); cf. S1 Appendix, Sections E and F for details on these other methods. S4 Fig illustrates these simulation data. In drawing these curves, we consider two qualitatively different kinds of errors: errors because a rolony isn’t detected at all (dotted lines), and errors because a rolony is detected but it is assigned the wrong barcode (solid lines). The left plots show these curves for simulated data. The right plots show these curves for simulated data with ‘dropout’—a form of noise present in some spatial transcriptomic methods (cf. S1 Appendix, Section G for details). For all four kinds of simulations, we found BarDensr is able to find significantly more spots. (B) Performance of BarDensr on the hybrid simulation. Simulated data is always imperfect; to try to measure performance on a more realistic dataset, we used a hybrid method a la [18]. We injected fake rolonies into real data, and quantified how well different methods could recover these fake spots. The plots above show 1-FNR (y-axis) as the function of scale intensity of the fake rolonies (x-axis) and number of fake rolonies injected (S, colored lines), without (top) and with (bottom) dropout, using BarDensr (left) and SRM (right). See S1 Appendix, Section G for details.