MAPLE: A Hybrid Framework for Multi-Sample Spatial Transcriptomics Data
Published in bioRxiv (Cold Spring Harbor Laboratory), 2022
High throughput spatial transcriptomics (HST) technologies provide unprecedented opportunity to identify spatially resolved cell sub-populations in tissue samples. However, existing methods preclude joint analysis of multiple HST samples, do not allow for differential abundance analysis (DAA), and ignore uncertainty…
Recommended citation: Hyeongseon Jeon, Carter Allen, José Antonio Ovando-Ricárdez, Yuzhou Chang, Lorena Rosas, Natalia-Del Pilar Vanegas, Hao Cheng, Juan Xie, et al.. (2022). "MAPLE: A Hybrid Framework for Multi-Sample Spatial Transcriptomics Data" bioRxiv (Cold Spring Harbor Laboratory).
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