GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

• Type of Material: Article
• Authors: Jiangmei Xiong, Vanderbilt University; Harsimran Kaur, Vanderbilt University; Cody N. Heiser, Vanderbilt University; Eliot T. McKinley, Vanderbilt University; Joseph T. Roland, Vanderbilt University; Robert J. Coffey, Vanderbilt University; Martha Shrubsole, Vanderbilt University; Julia Wrobel, Emory University; Siyuan Ma, Vanderbilt University; Ken S. Lau, Vanderbilt University; Simon Vandekar, Vanderbilt University
• Language: English
• Date: 2023-09-23
• Publisher: NIH
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): https://doi.org/10.1101/2023.09.20.558645
• Title of Journal or Parent Work: bioRxiv
• Start Page: 558645
• Grant/Funding Information: This research was supported by National Cancer Institute grants [U54CA274367 to K.S.L & M.J.S, U2CCA233291 to K.S.L & M.J.S & R.J.C., R01DK103831 to K.S.L., P50CA236733 to R.J.C].
• Keywords: mxIF; CODEX; classification; mixture model; CyCIF; Phenotyping
• Research Categories: Health Sciences, Oncology; Biology, Biostatistics; Biology, Bioinformatics; Health Sciences, Immunology

Abstract

Motivation 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. Results 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.