PMID- 32368363 OWN - NLM STAT- PubMed-not-MEDLINE VI - 2019 TI - Geostatistical visualization of ecological interactions in tumors. PG - 2741-2749 LA - eng PT - Journal Article PL - United States TA - Proceedings (ieee Int Conf Bioinformatics Biomed) JT - Proceedings. IEEE International Conference on Bioinformatics and Biomedicine JID - 101525347 IS - 2156-1125 (Print) LID - 10.1109/bibm47256.2019.8983076 [doi] FAU - Boyce, Hunter Bryan AU - Boyce HB AD - Program in Biomedical Informatics, Stanford University, Stanford, CA, USA. FAU - Mallick, Parag AU - Mallick P AD - Canary Center at Stanford for Cancer Early Detection, Stanford University, Palo Alto, CA, USA. IS - 2156-1125 (Linking) OTO - NOTNLM OT - Agent-Based Modeling OT - Cancer OT - Geostatistics OT - Pathology OT - Tumor Ecology OT - Visualization PMC - PMC7198084 LR - 20201101 DP - 2019 Nov DEP - 20200206 AB - Recent advances in our understanding of cancer progression have highlighted the roles played by molecular heterogeneity and by the tumor microenvironment in driving drug resistance and metastasis. The coupling of single-cell measurement technologies with algorithms, such as t-sne and SPADE, have enabled deep investigation of tumor heterogeneity. However, such techniques only capture molecular heterogeneity and do not enable the quantification nor visualization of intercellular interactions. They additionally do not allow the visualization of ecological niches that are critical to understanding tumor behavior. Novel computational tools to quantify and visualize spatial patterns in the tumor microenvironment are critically needed. Here, we take a tumor ecology perspective to examine how predation, mutualism, commensalism, and parasitism may impact tumor development and spatial patterning. We additionally quantify local spatial heterogeneity and the emergent global spatial behavior of the models using geostatistics. By visualizing emergent spatial patterns we demonstrate the potential utility of a geostatistical analysis in differentiating amongst cell-cell interactions in the tumor microenvironment. These studies introduce both an ecological framework for characterizing intercellular interactions in cancer and a novel way of quantifying and visualizing spatial patterns in cancer.