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

生物学研究正变得越来越量化，对产生的大量数据进行分析和解释是获得洞察力的关键步骤。特别是，单细胞RNA测序(scRNA-seq)的出现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产生的。然而，分析和解释由此产生的数据仍然是研究人员的一个关键瓶颈。特别是，分析数据的大量可能方法--包括选择用于质量控制、归一化、集群(将细胞分配给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--导致组合数量的可能工作流对于分析数据的计算生物学家来说太大而无法在实践中探索。此外，不同的工作流在同一数据集上可能会有非常不同的结果，几乎没有衡量标准来评估给定工作流的执行情况，而且到目前为止还没有特定于数据集的工作流推荐方法。因此，特别的人工工作流程选择和优化仍然是生物数据分析中的一个主要瓶颈，它可以显著改变下游结果和解释。为了解决这些问题，该计划旨在研究和开发新的统计机器学习方法，以自动学习单细胞数据分析的最佳工作流程。这将是第一次使用机器学习来预测对于给定的scrna-seq数据集，哪些工作流将表现最佳，并将创建第一个模型，以特定于数据集的方式智能地执行对工作流的实时推荐。此外，它还将包括对自动机器学习(AutoML)模型的重大和新颖的研究，这是一个不断扩大的研究领域，旨在为机器学习任务自动选择最优模型和超参数。与现有工作相比，我们将研究用于无监督学习问题的AutoML模型，其中包括大多数单细胞分析工作流。因此，我们的目标是建立BioAutoML领域，实现单细胞数据分析的自动化，同时利用人工智能研究的进展和现有的大量单细胞数据集，贡献新的机器学习研究。从长远来看，我们的愿景是开发一套模型和算法，用于生物科学中智能的、机器学习驱动的工作流建议和执行，减少人类密集型数据分析和解释的瓶颈。

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.