Accelerating biological discovery with automated machine learning for single-cell data analysis

生物研究正变得越来越量化，对产生的大量数据的分析和解释是提取洞察力的关键步骤。特别是，单细胞rna测序（scRNA-seq）的出现已经彻底改变了研究的许多方面，转录组范围的基因表达数据通常是从数十万个细胞中生成的。然而，分析和解释结果数据仍然是研究人员的一个关键瓶颈。特别是，大量可能的数据分析方法，包括质量控制、归一化、聚类（将细胞分配到离散细胞类型或状态）和降维（用于可视化或谱系推断）的算法选择，导致可能的工作流程的组合数量太大，计算生物学家无法在实践中分析数据。此外，在相同的数据集上，不同的工作流可能会产生截然不同的结果，几乎没有指标来评估给定工作流的执行情况，也没有特定于数据集的工作流推荐方法。因此，临时手工工作流程的选择和优化仍然是生物数据分析的主要瓶颈，可以显著改变下游结果和解释。

Biological research is becoming increasingly quantitative, with the analysis and interpretation of the vast quantities of data produced being key steps to extracting insight. In particular, the advent of single-cell RNA-sequencing (scRNA-seq) has revolutionized many aspects of research, with transcriptome-wide gene expression data routinely being generated from hundreds of thousands of cells. However, analyzing and interpreting the resulting data remains a key bottleneck to researchers. In particular, the large number of possible ways to analyze the data including choices of algorithms for quality control, normalization, clustering (assigning cells to discrete cell types or states) and dimensionality reduction (for visualization or lineage inference) leads to a combinatorial number of possible workflows that is far too large for the computational biologist analyzing the data to explore in practice. Furthermore, different workflows can have dramatically different results on the same dataset, with few metrics to assess how well a given workflow has performed and no data set -specific workflow recommendation methods to--date. Consequently, ad--hoc manual workflow choice and optimization remains a major bottleneck in biological data analysis that can significantly alter downstream results and interpretation. To address these issues, this program aims to investigate and develop novel statistical machine learning approaches to automatically learn optimal workflows for single--cell data analysis. This will be the first research that uses machine learning to predict which workflow will perform optimally for a given scRNA-seq dataset, and will create the first models to intelligently perform real--time recommendation of workflows in a dataset--specific manner. Furthermore, it will include significant and novel research into automated machine learning (AutoML) models, an expanding research domain that aims to automatically select optimal models and hyperparameters for machine learning tasks. In contrast with existing work we will research AutoML models for unsupervised learning problems, of which most single--cell analysis workflows are comprised. Consequently, we aim to establish the field of BioAutoML and automate aspects of single--cell data analysis while contributing novel machine learning research, leveraging both advances in artificial intelligence research and the large number of single--cell datasets currently available. In the longer term, our vision is to develop a suite of models and algorithms for intelligent, machine-learning driven workflow recommendation and execution in the biological sciences, reducing the bottleneck of human-intensive data analysis and interpretation.