Automated analysis of high dimensional flow cytometry data

背景 流式细胞术（FCM）产生的数据量的最近增加带来了独特的信息学和统计学挑战。人们普遍认识到，这些技术的一个基本挑战是简化数据和统计信息的提取。传统上，大多数FCM实验都是通过视觉分析，每次通过一个或两个维度的耗时和主观的连续检查。 上一笔赠款（2013年以上）的研究活动进展 与人工分析相比，我们在过去六年中的进展表明，计算分析可以是鲁棒的，可重复的和快速的。我们通过开发以下算法完成了NSERC DG之前的短期目标：（1）监督细胞群体识别;（2）用于无监督细胞群体识别的最佳机器学习方法;（3）最佳生物标志物发现算法;（4）用于细胞群体鉴定的无监督方法可以合并来自多个管样品的数据以鉴定细胞群体。我们开展了广泛的KT活动，将这些算法应用于来自全球合作者网络的复杂数据集，以测试他们对化合物作用机制等问题的假设。我们的研究结果支持当前提案的目标。 目标 长期目标：开发一个用户友好，强大，免费/开源的计算平台，用于FCM数据的高通量分析，该数据已广泛应用于自然科学研究。 短期目标： 目的1.开发一种强有力的方法来匹配样本中共同的细胞群体谱，以揭示未标记样本组之间以前未知的关系。 目标2.通过实现并行化扩展flowType以支持HPC 目标3.通过新的数据汇总统计提高flowType的性能。 目标4.将算法应用于合作者的自然科学数据集。 方法 短期目标将通过2名博士生的努力完成，由本科生支持。 影响 我的学术生涯的影响将是显着更大的，由于社区的努力，利用我们提出的算法开发比任何我可以单独完成我自己的。在本建议的范围内也是如此。我们开发的一种匹配细胞群体特征的方法将立即应用于国际小鼠表型鉴定联盟，该联盟耗资9亿美元，旨在鉴定哺乳动物基因组中每个基因的功能。我们与Genentech合作开发的下一代flowType/RchyOptimyx将立即应用于了解新化合物如何影响免疫系统的机制。所有HQP都培养数据科学家的技能，以满足行业和学术界对该领域科学家的高需求。

Background The recent increase in the amount of data generated by flow cytometry (FCM) poses unique informatics and statistical challenges. It is widely recognized that one basic challenge for these technologies is to simplify the extraction of data and statistical information. Traditionally, the majority of FCM experiments have been analyzed visually, through time consuming and subjective serial inspection of one or two dimensions at a time. Progress in research activities from the previous grant (2013+) In contrast to manual analysis, our progress over the last six years has shown computational analysis can be robust, reproducible and rapid. We completed the short term objectives of the previous of the NSERC DG through the development of algorithms for: (1) Supervised cell population identification; (2) The top-performing machine learning approach for unsupervised cell population identification; (3) The top-performing biomarker discovery algorithm; (4) A unsupervised method for cell population identification can incorporate data from multiple tube samples to identify cell populations. We undertook extensive KT activities to apply these algorithms to complex datasets from a worldwide network of collaborators to test their hypothesis on such matters as the mechanism of action of compounds. Our results support the Objectives of the current proposal. Objectives Long term objective: Develop a user-friendly, robust, free/open source computational platform for the high-throughput analysis of FCM data that becomes widely applied for Natural Science research. Short-term objectives: Objective 1. Develop a robust approach to match cell populations profiles in common across samples to uncover previously unknown relationships between groups of unlabelled samples. Objective 2. Extend flowType to support HPC by implementing parallelization Objective 3. Improve the performance of flowType through new data summary statistics. Objective 4. Apply algorithms to collaborator's natural science datasets. Methodology The short-term objectives will be completed through the efforts of 2 PhD students, supported by undergraduates. Impact The impact of my academic career will be significantly larger as a result of community efforts leveraging the algorithms we propose to develop than any I could accomplish solely on my own. This is true also within the context of this proposal. Our development of an approach to match cell population profiles will be immediately applied in the context of the International Mouse Phenotyping Consortium, a $900M effort targeted to the identification of the function of every gene in a mammalian genome. Our work with Genentech in the development of the next iteration of flowType/RchyOptimyx will immediately be applied to understand the mechanisms underlying how new chemical compounds affect the immune system. All HQP develop skills as data scientists, addressing the high demand for scientists in this area in industry and academia.