An Explainable Machine Learning Platform for Single Cell Data Analysis

单细胞RNA测序技术的快速发展使我们能够在生命的基本单位，单细胞内捕获基因签名。这使得能够发现和表征细胞类型，包括多细胞生物体中的新细胞类型;细胞间通讯和组织中各种细胞类型之间的复杂相互作用;在单细胞水平上对器官进行空间分辨映射;以及识别在不同背景下受影响的生物体的特定细胞类型中的基因和途径。该项目引入了一套新颖且可解释的机器学习方法，专为单细胞数据分析而设计。将开发一个可解释的机器学习平台，能够支持对单细胞RNA测序数据的高级分析，并做出可解释的预测，将表型与特定细胞类型中的基因和途径直接联系起来。该项目的方法可以转化为任何具有低样本量或多变量的表格数据集，这些数据集在生物研究中很普遍。该项目的单细胞测序数据分析工具，结果和生成的数据将成为研究项目的优秀范例，使本科生，研究生，女性和少数民族学生能够开发和应用可解释的机器学习方法，以促进我们对生物学的理解单细胞水平。此外，开发和应用强大的机器学习工具，从复杂的生物数据集产生可解释的结果，并广泛使用这些方法，工具和结果，将最大限度地发挥其价值，加速生物发现，并促进我们对细胞和分子生物学的理解。具体而言，该项目将开发一个新的机器学习方法平台，以生成单细胞RNA测序数据潜在空间中细胞和基因的解纠缠表示，可用于对表型进行可解释的预测。该项目包含三个协同任务：（1）开发可解释的基于细胞原型的方法来进行单细胞RNA测序数据分析，（2）开发可解释的基于概念的机器学习模型用于单细胞RNA测序数据分析，以及（3）开发机器学习方法，从批量RNA测序数据中生成代表性的单细胞表达数据。通过这些任务，该项目开发了算法，模型和工具，使最先进的可解释机器学习的全部功能能够应用于单细胞数据分析和单细胞分辨率的表型预测。该项目产生的算法和工具将广泛适用于在不同生物体的单细胞水平上预测基因和途径。该平台可以显着提高单细胞数据的实用性，并帮助研究人员分析自己的数据集。该项目的结果可以在以下网站上找到：https://www.cs.virginia.edu/~az9eg/website/projects.html.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The rapid advances in single-cell RNA sequencing technologies have enabled us to capture gene signatures within the fundamental units of life, single cells. This enables discovery and characterization of cell types including novel ones in multicellular organisms; cell-cell communication and the complex interactions between various cell types in tissues; spatially resolved mapping of organs at the single cell level; and identification of genes and pathways in specific cell types of an organism affected in different contexts. The project introduces a set of novel and explainable machine learning approaches tailored to single-cell data analysis. A platform for explainable machine learning will be developed capable of supporting advanced analysis of single-cell RNA sequencing data and making explainable predictions to directly link phenotypes with genes and pathways in specific cell types. The approaches from this project are translational to any tabular datasets with low-sample-size or many variables, which are prevalent in biological research. The project’s single-cell sequencing data analysis tools, results, and generated data will be excellent exemplars of research projects for exposing undergraduates, graduates, women, and minority students to the development and application of explainable machine learning approaches to advance our understanding of biology at the single cell level. Moreover, development and application of powerful machine leaning tools that yield interpretable results from complex biological datasets and broad accessibility to these methods, tools and results will maximize their value, accelerate biological discovery and advance our understanding of cellular and molecular biology. Specifically, the project will develop a platform of novel machine learning approaches to generate disentangled representations of cells and genes in latent spaces for single-cell RNA sequencing data, which can be used to make explainable predictions of phenotypes. The project contains three synergistic tasks: (1) develop explainable cell-prototype-based approaches to single-cell RNA sequencing data analysis, (2) develop explainable concept-based machine learning models for single-cell RNA sequencing data analysis, and (3) develop machine learning methods to generate representative single cell expression data from bulk RNA sequencing data. Through these tasks, the project develops algorithms, models and tools that enable the full power of state-of-the art explainable machine learning to be applied to single cell data analysis and phenotype prediction at single cell resolution. The algorithms and tools produced by this project will be broadly applicable to predicting genes and pathways at the single cell level in different organisms. The platform can significantly enhance the utility of single-cell data, and assist researchers in analysis of their own datasets. The results of the project can be found at: https://www.cs.virginia.edu/~az9eg/website/projects.html.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.