Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA.
BMC Bioinformatics. 2012 Sep 28;13:252. doi: 10.1186/1471-2105-13-252.
Effective quality assessment is an important part of any high-throughput flow cytometry data analysis pipeline, especially when considering the complex designs of the typical flow experiments applied in clinical trials. Technical issues like instrument variation, problematic antibody staining, or reagent lot changes can lead to biases in the extracted cell subpopulation statistics. These biases can manifest themselves in non-obvious ways that can be difficult to detect without leveraging information about the study design or other experimental metadata. Consequently, a systematic and integrated approach to quality assessment of flow cytometry data is necessary to effectively identify technical errors that impact multiple samples over time. Gated cell populations and their statistics must be monitored within the context of the experimental run, assay, and the overall study.
We have developed two new packages, flowWorkspace and QUAliFiER to construct a pipeline for quality assessment of gated flow cytometry data. flowWorkspace makes manually gated data accessible to BioConductor's computational flow tools by importing pre-processed and gated data from the widely used manual gating tool, FlowJo (Tree Star Inc, Ashland OR). The QUAliFiER package takes advantage of the manual gates to perform an extensive series of statistical quality assessment checks on the gated cell sub-populations while taking into account the structure of the data and the study design to monitor the consistency of population statistics across staining panels, subject, aliquots, channels, or other experimental variables. QUAliFiER implements SVG-based interactive visualization methods, allowing investigators to examine quality assessment results across different views of the data, and it has a flexible interface allowing users to tailor quality checks and outlier detection routines to suit their data analysis needs.
We present a pipeline constructed from two new R packages for importing manually gated flow cytometry data and performing flexible and robust quality assessment checks. The pipeline addresses the increasing demand for tools capable of performing quality checks on large flow data sets generated in typical clinical trials. The QUAliFiER tool objectively, efficiently, and reproducibly identifies outlier samples in an automated manner by monitoring cell population statistics from gated or ungated flow data conditioned on experiment-level metadata.
有效的质量评估是任何高通量流式细胞术数据分析管道的重要组成部分，特别是在考虑临床试验中典型流式实验的复杂设计时。技术问题，如仪器变化、抗体染色问题或试剂批次变化，可能导致提取的细胞亚群统计数据出现偏差。这些偏差可能以不明显的方式表现出来，如果不利用研究设计或其他实验元数据的信息，可能很难检测到。因此，需要系统地、综合地评估流式细胞术数据的质量，以便有效地识别随时间推移影响多个样本的技术错误。必须在实验运行、测定和整个研究的背景下监测门控细胞群体及其统计数据。
我们开发了两个新的软件包，flowWorkspace 和 QUAliFiER，用于构建流式细胞术门控数据质量评估的管道。flowWorkspace 通过从广泛使用的手动门控工具 FlowJo（Tree Star Inc，Ashland OR）导入预处理和门控数据，使手动门控数据可用于 BioConductor 的计算流式工具。QUAliFiER 包利用手动门控来对门控细胞亚群进行广泛的统计质量评估检查，同时考虑数据和研究设计的结构，以监测群体统计数据在染色面板、个体、等分试样、通道或其他实验变量之间的一致性。QUAliFiER 实现了基于 SVG 的交互式可视化方法，允许研究人员跨数据的不同视图检查质量评估结果，并且具有灵活的接口，允许用户根据自己的数据分析需求定制质量检查和异常值检测例程。
我们提出了一个由两个新的 R 包构建的管道，用于导入手动门控流式细胞术数据，并执行灵活和强大的质量评估检查。该管道满足了对能够对典型临床试验中生成的大型流式数据集执行质量检查的工具的日益增长的需求。QUAliFiER 工具通过根据实验级元数据对门控或非门控流式数据的细胞群体统计数据进行条件化，以自动化方式客观、高效和可重复地识别异常样本。
