STARCall integrates image stitching, alignment, and read calling to enable scalable analysis of in situ sequencing data
Fig 2
ConStitch performs stitching and alignment jointly on multi-cycle image sets.
A) Imaging procedure for in situ sequencing. Each sequencing cycle contains four channels corresponding to each nucleotide, and a fiducial channel to help with alignment. Positional errors are introduced into the collected images in two ways: intra-cycle, due to inaccuracies of microscope stage motor and sensors, and inter-cycle, due to removal and replacement of the well plate from the microscope. B) Procedure for stitching and aligning multi-cycle image sets. Overlapping pairs of images are aligned to each other, regions with no features are excluded from the alignment process, then the system of equations containing all pairwise alignments is solved to find global positions for each image. C) Performance of different solving methods with high levels of noise: Mean absolute error solver (default), mean squared error solver, spanning tree solver, and an individual cycle solver where each cycle is stitched independently with the MAE solver, then all stitched cycles are aligned with a single translation. Performance is tested on image set 4.1. To better show performance differences in suboptimal conditions, Gaussian noise of sigma = 100 was added to the pixel intensities of the image. D) Ratio of mean dot intensity to mean pixel intensity (top) and percent of reads miscalled (bottom) when sequencing images are shifted by a fractional pixel offset using spline interpolation. E) Percent of reads miscalled when alignment error is introduced into a random cycle.