信息保留成分分析：流式细胞术分析的数据投影
Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis
摘要：
流式细胞术通常用于表征白血病和淋巴瘤患者中的恶性细胞，追踪到单个细胞的水平。通常，流式细胞术数据分析通过一系列到数据集的轴上的二维投影来执行。多年来，临床医生已经确定了不同荧光标记物的组合，这些标记物产生了白血病和淋巴瘤-造血系统癌症-特定亚型的相对已知的表达模式。通过仅查看一系列二维投影，很少利用数据的高维性质。在本文中，我们提出了一种方法来确定一个低维投影，保持不同的肿瘤数据集之间的高维关系（即信息）。通过使用机器学习技术，我们允许临床医生以所有可用标记的线性组合定义的低维度可视化数据，而不是一次仅2个。这为诊断类似形式的癌症提供了帮助，也为探索性流式细胞术研究中的变量选择提供了手段。我们将我们的方法称为信息保持成分分析（IPCA）。
英文摘要：
Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have determined combinations of different fluorescent markers which generate relatively known expression patterns for specific subtypes of leukemia and lymphoma -- cancers of the hematopoietic system. By only viewing a series of 2-dimensional projections, the high-dimensional nature of the data is rarely exploited. In this paper we present a means of determining a low-dimensional projection which maintains the high-dimensional relationships (i.e. information) between differing oncological data sets. By using machine learning techniques, we allow clinicians to visualize data in a low dimension defined by a linear combination of all of the available markers, rather than just 2 at a time. This provides an aid in diagnosing similar forms of cancer, as well as a means for variable selection in exploratory flow cytometric research. We refer to our method as Information Preserving Component Analysis (IPCA).
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