TY  - JOUR
A1  - Miller, Brendan F.
A1  - Bambah-Mukku, Dhananjay
A1  - Dulac, Catherine
A1  - Zhuang, Xiaowei
A1  - Fan, Jean
T1  - Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities
Y1  - 2021/10/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1843 
EP  - 1855 
DO  - 10.1101/gr.271288.120 
VL  - 31 
IS  - 10 
UR  - http://genome.cshlp.org/content/31/10/1843.abstract 
N2  - Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and nonuniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial autocorrelation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell–cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomic data. We applied MERINGUE to a variety of spatially resolved transcriptomic data sets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomic data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease. 
ER  -