Statistical analysis of spatially resolved transcriptomic data by incorporating multiomics auxiliary information.

ABSTRACT: Effective control of false discovery rate is key for multiplicity problems. Here, we consider incorporating informative covariates from external datasets in the multiple testing procedure to boost statistical power while maintaining false discovery rate control. In particular, we focus on the statistical analysis of innovative high-dimensional spatial transcriptomic data while incorporating external multiomics data that provide distinct but complementary information to the detection of spatial expression patterns. We extend OrderShapeEM, an efficient covariate-assisted multiple testing procedure that incorporates one auxiliary study, to make it permissible to incorporate multiple external omics studies, to boost statistical power of spatial expression pattern detection. Specifically, we fi

SUBMITTER: Li Y 

PROVIDER: S-EPMC9339334 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature