A Bayesian Multivariate Mixture Model for High Throughput Spatial Transcriptomics
Published in Biometrics, 2022
High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we see…
Recommended citation: Carter Allen, Yuzhou Chang, Brian Neelon, Won Chang, Hang J. Kim, Zihai Li, Qin Ma, Dongjun Chung. (2022). "A Bayesian Multivariate Mixture Model for High Throughput Spatial Transcriptomics" Biometrics.
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