Tony A Slieman, Troy Camarata
Case-Based Group Learning Using Concept Maps to Achieve Multiple Educational Objectives and Behavioral Outcomes
Journal of medical education and curricular development
spatial transcriptomics techniques measuring gene expression retain spatial location information aiding situ studies organismal tissue architecture progression pathological processes techniques generate vast amounts omics data necessitating development computational methods reveal underlying tissue microenvironment heterogeneity main directions spatial transcriptomics data analysis spatial domain detection spatial deconvolution identify spatial functional regions parse distribution cell types spatial transcriptomics data integrating singlecell transcriptomics data two research directions many computational methods successively proposed article categorize three types machine learningbased methods probabilistic modelsbased methods deep learningbased methods list discuss representative algorithms type along advantages disadvantages describe datasets evaluation metrics used assess computational methods facilitating researchers selecting suitable computational methods according research needs finally combining latest technological developments advantages disadvantages current algorithms article look forward future directions computational method development