BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
Fig 7
Using cleaned images and SVD to examine model fit quality and variability.
(A) SVD analysis example using one gene. Spots are identified in Fj* for each barcode j* using local-max-peak-finding. For the gene barcode j* (Deptor) shown here (top left), three spots with the highest accuracy are being analyzed. The right panel shows the zoomed-in R × C plots of the raw image X (top) and ‘cleaned’ image X(j*) (bottom) at these three spot locations for barcode j*. Note that ‘cleaned’ images are significantly sparser than the raw images, as desired. We then applied SVD to the cleaned image X(j*) at these three spot locations. The first two columns on the bottom left show the zoomed-in image of the original spot (KF)j* and the learned weighted barcode matrix (Gj*) corresponding to this gene barcode j*. The top singular vectors are plotted in the last two columns (showing a good match with Gj* and the cropped (KF)j*). R2 is the squared correlation coefficient between X(j*) and the outer product of these two singular vectors; the high R2 values seen here indicate that the model accurately summarizes X(j*). (B) Results of SVD analysis of cleaned images for the top high-R2 spots. This plot summarizes the results of the analysis illustrated in (A). The first column shows (KF)j* around the brightest spots; the second column shows the top spatial singular vectors for the same region, and the last column shows the top temporal singular vectors for these spots (the top row shows the scaled Gj* learned from the model, and the bottom row shows the corresponding top temporal singular vectors for these spots). Only six barcodes that are most abundant in the selected region are shown here; S10 Fig provides a more complete illustration.