An automatic analysis and quality assurance method for lymphocyte subset identification

MinYang Zhang; YaLi Zhang; JingWen Zhang; JiaLi Zhang; SiYuan Gao; ZeChao Li; KangPei Tao; XiaoDan Liang; JianHua Pan; Min Zhu

doi:10.1515/cclm-2023-1141

Article

An automatic analysis and quality assurance method for lymphocyte subset identification

MinYang Zhang , YaLi Zhang , JingWen Zhang , JiaLi Zhang , SiYuan Gao , ZeChao Li , KangPei Tao , XiaoDan Liang , JianHua Pan and Min Zhu

Published/Copyright: January 15, 2024

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From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 7

Abstract

Objectives

Lymphocyte subsets are the predictors of disease diagnosis, treatment, and prognosis. Determination of lymphocyte subsets is usually carried out by flow cytometry. Despite recent advances in flow cytometry analysis, most flow cytometry data can be challenging with manual gating, which is labor-intensive, time-consuming, and error-prone. This study aimed to develop an automated method to identify lymphocyte subsets.

Methods

We propose a knowledge-driven combined with data-driven method which can gate automatically to achieve subset identification. To improve accuracy and stability, we have implemented a Loop Adjustment Gating to optimize the gating result of the lymphocyte population. Furthermore, we have incorporated an anomaly detection mechanism to issue warnings for samples that might not have been successfully analyzed, ensuring the quality of the results.

Results

The evaluation showed a 99.2 % correlation between our method results and manual analysis with a dataset of 2,000 individual cases from lymphocyte subset assays. Our proposed method attained 97.7 % accuracy for all cases and 100 % for the high-confidence cases. With our automated method, 99.1 % of manual labor can be saved when reviewing only the low-confidence cases, while the average turnaround time required is only 29 s, reducing by 83.7 %.

Conclusions

Our proposed method can achieve high accuracy in flow cytometry data from lymphocyte subset assays. Additionally, it can save manual labor and reduce the turnaround time, making it have the potential for application in the laboratory.

Keywords: anomaly detection; automated gating; automated methods; flow cytometry; lymphocyte subsets; quality assurance

Corresponding authors: JianHua Pan, PhD, Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., No. 10 Luoxuan 3rd Road, International Biotech Island, Guangzhou, Guandong 510000, P.R. China, Phone: +86 20 22283222, E-mail: labpjh@kingmed.com.cn; and Min Zhu, PhD, Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., No. 10 Luoxuan 3rd Road, International Biotech Island, Guangzhou, Guandong 510000, P.R. China, Phone: +86 20 22283222, E-mail: zb-zhumin@kingmed.com.cn

MinYang Zhang and YaLi Zhang share first authorship.

Research ethics: This study was approved by an Ethical Review Committee of Guangzhou KingMed Center for Clinical Laboratory. The approval ID is 2023158.
Informed consent: Not applicable.
Author contributions: All the authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The funding organization played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication. The authors state no conflict of interest.
Research funding: This research was supported by the Science and Technology Program of Guangzhou, China (Grant Number: 2024A03J0752).
Data availability: Not applicable.

Appendix

To provide a clear description of our method, some concepts need to be explained or defined mathematically.

Gated cell population

We refer to the cell population obtained through gating as the “gated cell population” and treat it as a set denoted by capital letters (such as L stands for gated lymphocyte population). Mathematical operations are defined on it, as described in Table A1.

Density distribution

We refer to the density function of the intensity of cell population C on the marker X as d _C@X(∙). We define the set composed of the peaks, which are points with a larger value than the window range, as follows.

(3) P e a k s C @ X = { x | ∀ y ∈ ( x − l 2 , x ) ∪ ( x , x + l 2 ) , d C @ X ( x ) > d C @ X ( y ) } ∩ { x | d C @ X ( x ) > 0.25 × Max d C @ X }

where l is the length of the window and Max d C @ X is the maximum value in the density distribution function d _C@X(∙), which facilitates the removal of short peaks and avoids the influence of scattered cells.

We refer to the minimum density value between the nth and (n + 1)th peak as T r o u g h C @ X n . However, the minimum value between two peaks may not be unique. We define the set of all such values as a set T r o u g h S e t C @ X n and select the middle value as the trough. We denote the set of all troughs as Troughs _C@X and its relative value as T r o u g h s C @ X % , which is an important consideration when determining the threshold.

Empirical interval

Based on past samples, we select an empirical interval of the threshold for each marker. The lower bound and upper bound of the empirical interval are denoted as LB _X and UB _X respectively, where X is the marker. In general, the threshold should fall within the empirical interval, making the troughs in the empirical interval potential threshold candidates.

Proportional splitting

If our cell population mainly consists of only one cell subset, the distribution generally has no troughs. To select a threshold, a proportional splitting approach can be used to divide the entire cell subset into the same quadrant. If we consider the proportion of the subset in the cell population to be n%, we should choose a number greater than n% as the splitting ratio. This is a hyperparameter, denoted as SR(%) in our method.

Referred to conventional usage, we defined certain symbols and functions as shown in Table A1. Based on this, our threshold selection strategy can be summarized in formula form as Table A2, and our anomaly detection rules can be summarized in formula form as Table A3.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2023-1141).

Received: 2023-10-12

Accepted: 2023-12-20

Published Online: 2024-01-15

Published in Print: 2024-06-25

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Keywords for this article

anomaly detection; automated gating; automated methods; flow cytometry; lymphocyte subsets; quality assurance