BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
Fig 2
BarDensr accurately estimates sparse, per-gene rolony densities from the proposed observation model.
(A) Rolony densities make it easier to detect rolonies. The left plot shows the max-projection of the original experimental image across all rounds and channels; detecting blob-like structures in this image can be challenging, especially when two rolonies are in close proximity. By contrast, the rolony densities for particular genes are sparser, so it is easier to identify the positions of individual rolonies in the tissue. The middle and right plots show examples of these rolony densities. The orange marks represent rolonies detected by a hand-curated approach. Note that the rolony densities appear to show several rolonies which were missed by the hand-curated approach (see S3 Fig for further details). (B) BarDensr accurately recovers the ground truth in simulated data. The left plot shows the simulated data in all rounds and channels. In the right plot, we applied BarDensr to this simulated data, and found that we were able to largely recover the true rolonies in this simulation (shown on the first column). The final column of plots shows the rolony densities learned by BarDensr, which shows that the algorithm accurately recovers most of the simulated ground truth rolonies, with a few mistakes. The middle column of plots shows a blurred version of the rolony densities and the spots discovered from these rolony densities.