Machine learning development environment for single-cell sequencing data analyses

Machine learning (ML) is transforming single-cell sequencing data analysis; however, the barriers of technology complexity and biology knowledge remain challenging for the involvement of the ML community in single-cell data analysis. Here we present an ML development environment for single-cell sequencing data analyses, together with a diverse set of realistic and accessible ML-Ready benchmark datasets. A cloud-based platform is built to dynamically scale workflows for collecting, processing, and managing various single-cell sequencing data to make them ML-ready. In addition, benchmarks for each problem formulation and a code-level and web-interface IDE for single-cell analysis method development are provided. These efforts provide an automated end-to-end single-cell analysis ML pipeline that simplifies and standardizes the process of single-cell data formatting, loading, model development, and model evaluation.