Shuquan Su

STUltra integrates spatial transcriptomics data to map disease traits onto spatiotemporal tissue landscapes, outperforming existing methods in scalability and speed.

This paper surveys PCR-deduplication and error-correction methods on short reads, finding that computational approaches differ significantly from UMI-based methods and leave many errors uncorrected.

This paper introduces a method to correct millions of erroneous short reads by leveraging graph learning on edit distances, improving downstream applications like genome abundance quantification and SNP profiling.