Overview

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. We present an ML development environment for single-cell sequencing data analyses with a diverse set of AI-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.

OSCB aims to provide automated end-to-end single-cell analyses ML pipelines to simplify and standardize the process of single-cell data formatting, quality control, loading, model development, and model evaluation.

Workflows are developed for collecting, processing, and managing diverse single-cell sequencing data to make them ML-ready and build benchmarks.

IDE is provided for supporting partial method development.

Assessment utilities are provided for evaluating results and report generation.

This end-to-end pipeline transforms the traditional “static” machine Learning into continuous learning on extensive new data.

By in-depth fusing models with data, this platform could ultimately help many single-cell sequencing researchers substantially.

OSCB is an on-going effort, and we are planning to increase our coverage in the future.