Flow cytometry is widely used to identify cell populations in patient-derived
fluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). While
ubiquitous in research and clinical practice, flow cytometry requires gating,
i.e. cell type identification which requires labor-intensive and error-prone
manual adjustments. To facilitate this process, we designed GateNet, the first
neural network architecture enabling full end-to-end automated gating without
the need to correct for batch effects. We train GateNet with over 8,000,000
events based on N=127 PB and CSF samples which were manually labeled
independently by four experts. We show that for novel, unseen samples, GateNet
achieves human-level performance (F1 score ranging from 0.910 to 0.997). In
addition we apply GateNet to a publicly available dataset confirming
generalization with an F1 score of 0.936. As our implementation utilizes
graphics processing units (GPU), gating only needs 15 microseconds per event.
Importantly, we also show that GateNet only requires ~10 samples to reach
human-level performance, rendering it widely applicable in all domains of flow
cytometry.

通过使用 GateNet 神经网络架构，本研究提出的自动门控方法在流式细胞术中实现了无需矫正批处理效应的全自动门控，验证了其与人类表现相媲美的准确性，并展示了其在各个领域中广泛适用的特点。

GateNet：一种用于自动流式细胞术分割的新型神经网络架构

GateNet: A novel Neural Network Architecture for Automated Flow  Cytometry Gating

When systems use data-based models that are based on machine learning (ML),
errors in their results cannot be ruled out. This is particularly critical if
it remains unclear to the user how these models arrived at their decisions and
if errors can have safety-relevant consequences, as is often the case in the
medical field. In such cases, the use of dependable methods to quantify the
uncertainty remaining in a result allows the user to make an informed decision
about further usage and draw possible conclusions based on a given result. This
paper demonstrates the applicability and practical utility of the Uncertainty
Wrapper using flow cytometry as an application from the medical field that can
benefit from the use of ML models in conjunction with dependable and
transparent uncertainty quantification.

本文展示了不确定性包装器在医学领域中与可靠方法和透明不确定性量化相结合的机器学习模型的应用和实用价值，以流式细胞术作为一个示例。

医疗领域的不确定性封装：在实践中确立透明的不确定性量化方法，针对非透明的机器学习模型

Uncertainty Wrapper in the medical domain: Establishing transparent  uncertainty quantification for opaque machine learning models in practice

Flow cytometry mainly used for detecting the characteristics of a number of
biochemical substances based on the expression of specific markers in cells. It
is particularly useful for detecting membrane surface receptors, antigens,
ions, or during DNA/RNA expression. Not only can it be employed as a biomedical
research tool for recognising distinctive types of cells in mixed populations,
but it can also be used as a diagnostic tool for classifying abnormal cell
populations connected with disease. Modern flow cytometers can rapidly analyse
tens of thousands of cells at the same time while also measuring multiple
parameters from a single cell. However, the rapid development of flow
cytometers makes it challenging for conventional analysis methods to interpret
flow cytometry data. Researchers need to be able to distinguish
interesting-looking cell populations manually in multi-dimensional data
collected from millions of cells. Thus, it is essential to find a robust
approach for analysing flow cytometry data automatically, specifically in
identifying cell populations automatically. This thesis mainly concerns
discover the potential shortcoming of current automated-gating algorithms in
both real datasets and synthetic datasets. Three representative automated
clustering algorithms are selected to be applied, compared and evaluated by
completely and partially automated gating. A subspace clustering ProClus also
implemented in this thesis. The performance of ProClus in flow cytometry is not
well, but it is still a useful algorithm to detect noise.

研究了自动化门控算法在流式细胞术中的局限性，并比较了三种典型自动聚类算法和子空间聚类算法 ProClus 在识别细胞群中的性能。

流式细胞仪数据分析的机器学习

Machine Learning for Flow Cytometry Data Analysis

We propose a simple method to learn linear causal cyclic models in the
presence of latent variables. The method relies on equilibrium data of the
model recorded under a specific kind of interventions ("shift interventions").
The location and strength of these interventions do not have to be known and
can be estimated from the data. Our method, called backShift, only uses second
moments of the data and performs simple joint matrix diagonalization, applied
to differences between covariance matrices. We give a sufficient and necessary
condition for identifiability of the system, which is fulfilled almost surely
under some quite general assumptions if and only if there are at least three
distinct experimental settings, one of which can be pure observational data. We
demonstrate the performance on some simulated data and applications in flow
cytometry and financial time series. The code is made available as R-package
backShift.

该研究提出了一种名为 backShift 的方法，这种方法使用了特定类型的干预数据来学习具有潜在变量的线性因果循环模型，其性能在一些模拟数据以及在流式细胞术和金融时间序列应用中得到了验证。