A Bayesian Multivariate Mixture Model for Spatial Transcriptomics Data
Published in bioRxiv (Cold Spring Harbor Laboratory), 2021
Abstract 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 dat…
Recommended citation: Carter Allen, Yuzhou Chang, Brian Neelon, Won Chang, Hang J. Kim, Zihai Li, Qin Ma, Dongjun Chung. (2021). "A Bayesian Multivariate Mixture Model for Spatial Transcriptomics Data" bioRxiv (Cold Spring Harbor Laboratory).
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