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

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 o...

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Autores principales: Xiong, Jiangmei, Kaur, Harsimran, Heiser, Cody N, McKinley, Eliot T, Roland, Joseph T, Coffey, Robert J, Shrubsole, Martha J, Wrobel, Julia, Ma, Siyuan, Lau, Ken S, Vandekar, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10541135/
https://www.ncbi.nlm.nih.gov/pubmed/37781604
http://dx.doi.org/10.1101/2023.09.20.558645
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author Xiong, Jiangmei
Kaur, Harsimran
Heiser, Cody N
McKinley, Eliot T
Roland, Joseph T
Coffey, Robert J
Shrubsole, Martha J
Wrobel, Julia
Ma, Siyuan
Lau, Ken S
Vandekar, Simon
author_facet Xiong, Jiangmei
Kaur, Harsimran
Heiser, Cody N
McKinley, Eliot T
Roland, Joseph T
Coffey, Robert J
Shrubsole, Martha J
Wrobel, Julia
Ma, Siyuan
Lau, Ken S
Vandekar, Simon
author_sort Xiong, Jiangmei
collection PubMed
description 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. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.
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spelling pubmed-105411352023-10-01 GammaGateR: semi-automated marker gating for single-cell multiplexed imaging Xiong, Jiangmei Kaur, Harsimran Heiser, Cody N McKinley, Eliot T Roland, Joseph T Coffey, Robert J Shrubsole, Martha J Wrobel, Julia Ma, Siyuan Lau, Ken S Vandekar, Simon bioRxiv Article 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. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR. Cold Spring Harbor Laboratory 2023-09-23 /pmc/articles/PMC10541135/ /pubmed/37781604 http://dx.doi.org/10.1101/2023.09.20.558645 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Xiong, Jiangmei
Kaur, Harsimran
Heiser, Cody N
McKinley, Eliot T
Roland, Joseph T
Coffey, Robert J
Shrubsole, Martha J
Wrobel, Julia
Ma, Siyuan
Lau, Ken S
Vandekar, Simon
GammaGateR: semi-automated marker gating for single-cell multiplexed imaging
title GammaGateR: semi-automated marker gating for single-cell multiplexed imaging
title_full GammaGateR: semi-automated marker gating for single-cell multiplexed imaging
title_fullStr GammaGateR: semi-automated marker gating for single-cell multiplexed imaging
title_full_unstemmed GammaGateR: semi-automated marker gating for single-cell multiplexed imaging
title_short GammaGateR: semi-automated marker gating for single-cell multiplexed imaging
title_sort gammagater: semi-automated marker gating for single-cell multiplexed imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10541135/
https://www.ncbi.nlm.nih.gov/pubmed/37781604
http://dx.doi.org/10.1101/2023.09.20.558645
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