BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
Fig 5
BarDensr can be scaled up with sparsifying and coarse-to-fine approaches.
(A) Coarse-to-fine acceleration. The Area Under the ROC (AUROC) summarizes the performance of a method by calculating the integral of the ROC curve (higher is better). The black curve plots the AUROC performance on the simulated data, against the number of seconds the default iterative algorithm has been allowed to use, up to a maximum of 15 iterations. We can also use a coarse-to-fine strategy, where we first run the algorithm on downsampled data for 20 iterations, and use the results to perform 10 additional iterations on the full high-resolution data; the red curve plots the performance for this strategy. (B) BarDensr can take advantage of gene-sparsity. Here we used two different approaches to analyze a 1000 × 1000 region of the experimental data. The first approach uses BarDensr naively, applying it directly to the image. The second approach, illustrated on the first two plots, accelerates the method using a ‘coarse-to-fine’ method by taking advantage of ‘gene-sparsity.’ Specifically, we split this region into 4 × 4 patches (the borders of these patches are indicated as the white lines on the left plot). After the relatively fast ‘coarse’ step, the barcodes that have very low maximum rolony densities were removed before the following ‘fine’ step. This keeps only a relatively small number of barcodes to consider for each patch (ranging from 38 to 65 out of 81 barcodes, as shown in the middle plot), therefore reducing the computation time and the memory usage for the ‘fine’ step later (cf. S1 Appendix, Section K for more detail). We here show that both methods yield nearly the same result, as shown in the ROC curves on the right plot. In particular, we treated one method as the ‘truth’ and constructed an ROC curve indicating the accuracy of the other method. We can then do the reverse, treating the other method as ‘truth.’ The results suggest strong agreement.