Before revision:  Furthermore, we have designed a high performance machine-learning AI-pipeline in quantifying spatial distribution of cell phenotypes from tissue images with various complexities, and extract single-cell or even subcellular resolution profiling-map of protein and RNA expression over a million cells tissue section .
After revision:  Furthermore, we have designed a high performance machine-learning AI-pipeline in quantifying spatial distribution of cell phenotypes from tissue images with a mixture of biomarkers. The results achieved in this study suggest that RRScell provides a robust enough way for extracting cytometric single cell morphology as well as biomarker content in various tissue types, while the build-in markerUMAP tool secures the efficiency of dimension reduction, making it viable as a general tool in the spatial analysis of high dimensional tissue image .