Machine Learning (ML)-Enabled Automation for High-Throughput Data Processing in Flow Cytometry

计算机科学 人口 流式细胞术 细胞仪 公制(单位) 聚类分析 人工智能 医学 免疫学 工程类 运营管理 环境卫生
作者
Anna Kamysheva,Dmitrii V. Fastovets,Roman N. Kruglikov,Arseniy A. Sokolov,Anastasiya S. Fefler,Anastasiia A. Bolshakova,Anastasia Radko,Ilya Krauz,Sheila T. Yong,M. Goldberg,Ravshan Ataullakhanov,Aleksandr Zaitsev
出处
期刊:Blood [American Society of Hematology]
卷期号:142 (Supplement 1): 905-905 被引量:1
标识
DOI:10.1182/blood-2023-180146
摘要

Introduction Flow cytometry is widely used in clinical and research laboratories for diagnostics, biomarker discovery, and immune system monitoring. Flow cytometry data processing still uses gating- and clustering-based approaches that are highly time-consuming and subjective. Data processing time increases with panel size and number of detected populations, posing challenges to the search for new biomarkers. Low reproducibility and method limitations have thus far hindered efforts to automate and standardize flow cytometry data processing; hence, these efforts have not yielded any significant advancements in data processing methods. Here we present a new ML-based algorithm for automated cell-type labeling. Our supervised ML approach allows us to classify every event in a flow cytometry data file solely based on the presence and absence of markers, without the need for prior knowledge or assumption about cell population content in the sample. This approach enables the detection of rare and/or new cell populations with a high average quality metric (f1-score). The rapid and high-quality analysis our algorithm can perform renders it applicable in clinical settings, particularly for detecting hematological abnormalities and cancers. Methods We processed 500 blood samples from a cohort of healthy donors and patients with various cancer diagnoses using 10 different 18-channel multicolor flow cytometry panels. We then used data from either the entire or a portion of these 500 samples in a 3:1 split for training:test datasets to train and test our algorithm on each cytometry panel. To do this, we manually matched cells with certain cellular phenotypes to create 10 high-quality training sets for supervised learning and 10 test datasets, one pair for each of the 10 panels. To train the cell type classifier, we set up a two-level boosting-based model. The first-level model filters out outliers, including dead cells, cellular debris, beads, and other undefined particles, in order to hone in on the target population. The second-level model for predicting cell types within a target population is defined by two approaches. The population-based approach detects major subpopulation types in a target population and predicts the precise population labels. This approach is useful for labeling a small number of previously known or predicted subpopulations. The marker-based approach is useful for target populations with large numbers of subpopulations, such as T cells harboring different combinations of cell-surface receptors. It predicts the presence or absence of specific markers on each cell to assign its phenotype. It also allows us to construct complex hierarchies in order to detect new populations that are challenging to identify manually. Figure 1 outlines our workflow. Results We validated our final set of 10 trained models on our test dataset. The summarized number of detected cell populations in the test dataset was 221, which corresponds to the number of unique cell types predicted by our models. Table 1 shows the evaluation metrics for our algorithm for populations with > 0.1% whole blood cells (WBCs).The average quality metric (f1-score) for all antibody panels used is 0.86. This value is the mean of all f1-scores calculated for all cell populations identified by our algorithm. Mean f1-score is the highest (0.96) for large populations, lower (0.87) for mid-sized populations, and lowest but acceptable (0.77) for small populations. Mean quality score for the marker-based models is also high (0.96). Compared to manual evaluation that took approximately 1 hour to analyze one data file, the algorithm completed analysis within 10 seconds. Conclusion Our new algorithm automates cell labeling and produces high-quality outputs that are comparable to manual processing, but with a much shorter turnaround time (TAT) and without the need for prior knowledge or expert competence from the user. Importantly, it allows us to effectively and accurately filter out outliers, identify the target population, and divide this target population into multiple cell subtypes including new and rare cell subpopulations, all without a priori assumptions about cell population content in the sample. Given its ability to perform high-quality cell population analysis and its short TAT, our algorithm provides rapid, unbiased, and precise cell typing that will have utility for the diagnosis of heme malignancies and immunoprofiling.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
grs完成签到,获得积分10
刚刚
王sir完成签到 ,获得积分10
1秒前
1秒前
丁丁丁发布了新的文献求助10
2秒前
2秒前
111完成签到,获得积分10
2秒前
yycc发布了新的文献求助10
3秒前
4秒前
威威完成签到,获得积分10
4秒前
Liar完成签到,获得积分10
6秒前
6秒前
yyy完成签到,获得积分10
6秒前
6秒前
guan完成签到,获得积分10
7秒前
阿悦完成签到 ,获得积分10
7秒前
AU完成签到,获得积分10
8秒前
超级玛丽完成签到 ,获得积分10
8秒前
Owen应助魏伯安采纳,获得10
9秒前
淡淡的小懒虫完成签到,获得积分10
9秒前
坦率大米完成签到,获得积分10
10秒前
今天也要好好学习完成签到,获得积分10
10秒前
11秒前
Janice完成签到,获得积分10
11秒前
Tangyartie完成签到 ,获得积分10
11秒前
lshl2000完成签到,获得积分10
12秒前
迁小yan完成签到 ,获得积分10
12秒前
13秒前
山月完成签到,获得积分10
13秒前
言溪完成签到 ,获得积分10
13秒前
干净土豆完成签到,获得积分10
14秒前
似画关注了科研通微信公众号
16秒前
16秒前
酷小裤发布了新的文献求助10
17秒前
李新光完成签到 ,获得积分10
18秒前
18秒前
19秒前
范先生完成签到,获得积分10
19秒前
魏伯安发布了新的文献求助10
19秒前
小王同学发布了新的文献求助10
19秒前
今后应助科研通管家采纳,获得10
20秒前
高分求助中
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Chen Hansheng: China’s Last Romantic Revolutionary 500
宽禁带半导体紫外光电探测器 388
Case Research: The Case Writing Process 300
Global Geological Record of Lake Basins 300
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3142849
求助须知:如何正确求助?哪些是违规求助? 2793801
关于积分的说明 7807889
捐赠科研通 2450113
什么是DOI,文献DOI怎么找? 1303653
科研通“疑难数据库(出版商)”最低求助积分说明 627017
版权声明 601350