Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1785113850546225152 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10541135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT xiongjiangmei gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT kaurharsimran gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT heisercodyn gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT mckinleyeliott gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT rolandjosepht gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT coffeyrobertj gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT shrubsolemarthaj gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT wrobeljulia gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT masiyuan gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT laukens gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging AT vandekarsimon gammagatersemiautomatedmarkergatingforsinglecellmultiplexedimaging |