Table 2.

Summary of methods for analyzing spatial molecular profiling data

Method	Framework	Data	Implementation	Link
SpatialDE	GP	spatial gene expression profile	Python	https://github.com/Teichlab/SpatialDE
SPARK	GP		R	https://xzhoulab.github.io/SPARK/
trendsceek	marked point process		R	https://github.com/edsgard/trendsceek
staNMF	matrix factorization		Python	https://github.com/greenelab/staNMF
SVCA	GP		Python	https://github.com/damienArnol/svca
Moran’s I	spatial autocorrelation		R	https://cran.r-project.org/web/packages/lctools/index.html
K,G,F,J,L function	point process	spatial coordinates	R	https://cran.r-project.org/web/packages/spatstat/index.html
BayesHiddenPottsMixture	Potts model	spatial coordinates, cell type annotation	R	https://github.com/liqiwei2000/BayesHiddenPottsMixture
BayesMarkInteractionModel	marked point process		R	https://github.com/liqiwei2000/BayesMarkInteractionModel
histoCAT	NA	image	Matlab	http://www.bodenmillerlab.com/research-2/histocat/
GripDL	neural network	spatial gene expression profile, gene regulatory network	Python	https://github.com/2010511951/GripDL
SpaCell	neural network	spatial gene expression profile, image	Python	https://github.com/BiomedicalMachineLearning/Spacell