Department of Biostatistics, Vanderbilt University, 2525 West End Avenue, Suite 1100, Nashville, TN 37203-1741, United States.
Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, 340 Light Hall, 2215 Garland Ave, Nashville, TN 37232, United States.
Bioinformatics. 2024 Jun 3;40(6). doi: 10.1093/bioinformatics/btae356.
Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data.
To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation.
The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.
多重免疫荧光（mIF）是一种新兴的多通道蛋白成像检测方法，可解析组织中的细胞水平空间特征。然而，现有的自动化细胞表型分析方法，如聚类，在实现实验间一致性方面面临挑战，并且通常需要主观评估。因此，mIF 分析通常会恢复到基于原始成像数据的手动阈值标记门控。
为了解决需要可评估的半自动算法的问题，我们开发了 GammaGateR，这是一个用于交互式标记门控的 R 包，专门用于 mIF 图像中分割的细胞水平数据。基于新颖的封闭形式伽马混合模型，GammaGateR 提供了标记阳性细胞比例的估计值和标记阳性细胞的软聚类。该模型结合了用户指定的约束条件，为每个幻灯片提供一致但特定于幻灯片的模型拟合。我们将 GammaGateR 与最新的用于注释 mIF 数据的无监督方法进行了比较，使用两个结肠数据集和一个卵巢癌数据集进行了评估。我们表明，GammaGateR 产生的结果与通过手动注释建立的银标准高度相似。此外，我们通过映射结肠中 CD68 和 MUC5AC 细胞之间已知的空间相互作用，以及通过将表型概率作为输入用于机器学习方法来准确预测卵巢癌患者的生存，证明了其在识别生物学信号方面的有效性。GammaGateR 是一种高效的工具，可以提高标记门控结果的可重复性，同时减少手动分割的时间。
