Time-lapse microscopy provides 4D imaging data for monitoring and studying down to single-cell, the stochastic processes involved as bacterial colonies grow and interact under different stress conditions. Two main factors prevent high throughput analysis: a) cell segmentation and tracking are very time-consuming and error-prone and b) analytics tools are lacking to interpret the plethora of features extracted from a complex “cell-movie.” To address both limitations, we have recently developed a multi-resolution Bio-image Analysis & Single-Cell Analytics framework, called BaSCA. Our end-to-end pipeline segments accurately single-cells and tracks them across frames, identifying cell divisions and constructing each cell colony's lineage and division tree. Here we present the capabilities of an R- package, which enables users to correlate and visually explore the spatiotemporal trends of single-cell attributes, discover and study epigenetic effects across many cell generations and even identify and correct segmentation errors. All these unique tools empower research towards deciphering microbial community dynamic behaviors, such as inter-species competition/cooperation, “persister” (dormant) cells stochastic emergence, etc., with a huge impact on human health.