Using BASIN-ML for Machine Learning-Based Statistical Analysis and Reporting for Biofilm Datasets

Abstract

Biological and biomedical microscope image (bioimage) comparison remains useful to approach many research challenges—from biofilms to human diseases. This powerful technology allows researchers to provide the community with a quick visual snapshot of varying experimental conditions. But a two-condition comparison still relies on a researcher’s eyes to draw conclusions despite the availability of multiple— often complex—digital image analysis tools. Our Bioimage Analysis, Statistic, and Comparison (BASIN) software provides an easy, objective, reproducible comparison leveraging inferential statistics to bridge image data analysis with other biomedical data modalities such as gene expression. Users have access to a machine learning module to assist with image segmentation using modern, trainable algorithms. BASIN also provides several key data points including images’ object counts, net and mean pixel intensities, net and mean object surface areas, plus a variety of other potentially useful data. Hypothesis testing is performed on mean object intensities and surface areas using the statistical power of the R programming language. These features allow BASIN to extend the current scope of image comparison. It gives researchers a multi-model knowledge about matters such as drug protein marker response, the significance of cell population changes, and changes in cell morphology. To improve BASIN’s accessibility and transparency we implemented it in R using Shiny framework and provided both an online trial version and a customizable offline version. We also have a batch version to run on datasets with hundreds of biomedical images. BASIN workflows consist of five core modules including image upload, feature extraction, statistical analysis, visualization, and report generation.