Automated analysis of flow cytometry data with minimal training files: Research evaluation of an elastic image registration algorithm for TBNK, stem cell enumeration, and lymphoid screening tube assays.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Allison Irvine, Suhail Tahir, Vishnu Tripathi, Farzad Oreizy, Moen Sen, Anthony Giuliano, Anna Lin, Angela Chen, Chih-Hung Lai, Imelda Omana-Zapata, Yang Zeng, Paresh Jain, Scott J Bornheimer
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Abstract

Automated analysis of flow cytometry data can improve objectivity and reduce analysis time but has generally required work by software and algorithm experts. Here, we investigated the performance of BD ElastiGate™ Software (hereafter ElastiGate), which allows users to automate gating by selecting gated training files, then uses elastic image registration to gate new files. Three assays of increasing complexity were examined: TBNK, stem cell enumeration (SCE), and lymphoid screening tube (LST). For TBNK analysis, 60 peripheral blood (PB) samples from normal, HIV+, and controls were tested with ground truth analysis by an existing automated method. For SCE, 128 samples including bone marrow (BM), cord blood (CB), and apheresis were tested with analysis by multiple manual analysts. For LST, 80 PB and 28 BM samples were tested with manual analysis. For ElastiGate, a minimal number of training files was selected. Results were compared by Bland-Altman or F1 score analysis. For TBNK, ElastiGate using three training files (1 control, 1 normal, 1 HIV+) showed mean %bias across all reported populations between -1.48% and 7.13% (average 2.08%). For SCE, ElastiGate using three BM and two CB training files showed median F1 scores >0.93 in comparison to >0.94 and >0.92 for two other manual analysts. For LST, ElastiGate using four training files for each of PB and BM showed median F1 scores >0.945 for 13 of 14 PB populations and 10 of 14 BM populations, with generally similar or better performance for normal samples compared to abnormal; populations with lower scores were often associated with lower agreement between manual analysts. Based on analysis of three assays with four sample types of increasing complexity, ElastiGate with minimal training files may perform as an automated gating assistant. The results reported here are for research use only, not for use in diagnostic or therapeutic procedures.

用最少的训练文件自动分析流式细胞仪数据:对用于TBNK、干细胞计数和淋巴筛管检测的弹性图像配准算法进行研究评估。
流式细胞仪数据的自动分析可提高客观性并缩短分析时间,但通常需要软件和算法专家的工作。在此,我们研究了 BD ElastiGate™ 软件(以下简称 ElastiGate)的性能,该软件允许用户通过选择门控训练文件来自动门控,然后使用弹性图像配准来门控新文件。我们研究了三种复杂程度不断增加的检测方法:TBNK、干细胞计数(SCE)和淋巴细胞筛查管(LST)。在 TBNK 分析中,使用现有的自动方法对来自正常、HIV+ 和对照组的 60 份外周血(PB)样本进行了地面实况分析测试。对于 SCE,128 份样本(包括骨髓 (BM)、脐带血 (CB) 和采血)接受了检测,并由多名人工分析师进行了分析。对于 LST,80 份 PB 和 28 份 BM 样本通过人工分析进行了测试。对于 ElastiGate,选择了最少数量的训练文件。结果通过 Bland-Altman 或 F1 分数分析进行比较。对于 TBNK,ElastiGate 使用三个训练文件(1 个对照组、1 个正常组、1 个 HIV+ 组),结果显示所有报告人群的平均偏倚率在 -1.48% 到 7.13% 之间(平均为 2.08%)。在 SCE 方面,ElastiGate 使用三个 BM 和两个 CB 训练文件显示的中位 F1 分数大于 0.93,而其他两个人工分析仪的中位 F1 分数分别大于 0.94 和 0.92。在 LST 方面,ElastiGate 对 PB 和 BM 各使用了四个训练文件,结果显示 14 个 PB 群体中有 13 个和 14 个 BM 群体中有 10 个的中位 F1 分数大于 0.945,与异常样本相比,正常样本的表现一般相似或更好;分数较低的群体往往与人工分析师之间的一致性较低有关。根据对三种检测方法和四种复杂程度不断增加的样本类型的分析,ElastiGate 只需少量的培训文件就可作为自动分选助手。此处报告的结果仅供研究使用,不能用于诊断或治疗程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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