On Clustering for Cell Phenotyping in Multiplex Immunohistochemistry (mIHC) and Multiplexed Ion Beam Imaging (MIBI) Data

Seal, Souvik; Wrobel, Julia; Johnson, Amber; Nemenoff, Raphael A.; Schenk, Erin L.; Bitler, Benjamin G.; Jordan, Kimberly R.; Ghosh, Debashis

doi:10.21203/rs.3.rs-609920/v1

Cited by 5 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We apply our method on two real datasets, an mIHC lung cancer dataset (Johnson and others , 2021; Seal and others , 2021) from Vectra 3.0 platform and a MIBI triple-negative breast cancer dataset (Keren and others , 2018). In all the subsequent analyses, the marker intensities were scaled to have expression value between zero and one.…”

Section: Real Data Analysismentioning

confidence: 99%

See 1 more Smart Citation

MIAMI: Mutual Information-based Analysis of Multiplex Imaging data

Seal

Ghosh

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Motivation: Studying the interaction or co-expression of the proteins or markers in the tumor microenvironment (TME) of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for each marker, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lack of robustness. Results: We present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyze a lung cancer dataset and a triple-negative breast cancer dataset finding the co-expression of the markers, HLA-DR and CK and the co-expression of markers, HLA-DR, CD45RO, HLA-Class-1, H3K27me3 and H3K9ac to be associated with survival in the two datasets respectively. We also demonstrate the robustness of our method through different simulation studies.

show abstract

Section: Real Data Analysismentioning

confidence: 99%

“…We applied our method on two real datasets, an mIHC lung cancer dataset (Seal et al ., 2021) from Vectra 3.0 platform and a MIBI triple-negative breast cancer dataset (Keren et al ., 2018). We also applied the traditional thresholding-based method on both the datasets.…”

Section: Real Data Analysismentioning

confidence: 99%

MIAMI: Mutual Information-based Analysis of Multiplex Imaging data

Seal

Ghosh

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…After the initial step of identifying cellular boundaries through single-cell segmentation, different cell types, such as CD4 + T-helper cells, CD8 + cytotoxic T cells, and tumor cells, are detected based on supervised or semisupervised cluster-ing 13,14 using the continuous-valued intensity of the respective surface or phenotypic markers. 15 Once the cell types have been identified, their relative abundance and additionally spatial organization can be studied.…”

Section: ■ Introductionmentioning

confidence: 99%

SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA

Seal,

Neelon,

Angel

et al. 2024

J. Proteome Res.

Self Cite

View full text Add to dashboard Cite

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.

show abstract

“…In recent years, various technologies are being used for probing single-cell spatial biology, for example, multiparameter immunofluorescence ( Bataille et al , 2006 ), imaging mass cytometry ( Ali et al , 2020 ), multiplex immunohistochemistry (mIHC) ( Tan et al , 2020 ; Vu et al , 2021 ) and multiplexed ion beam imaging (MIBI) ( Angelo et al , 2014 ; Seal et al , 2021 ). These technologies, often referred to as multiplex tissue imaging, offer the potential for researchers to explore the basis of many different biological mechanisms.…”

Section: Introductionmentioning

confidence: 99%

DenVar: density-based variation analysis of multiplex imaging data

Seal

Ghosh

et al. 2022

Bioinformatics Advances

View full text Add to dashboard Cite

Multiplex imaging platforms have become popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular protein, and the other to classify the subjects based on the proportion of the positive cells. We present a threshold-free approach in which distance between a pair of subjects is computed based on the probability density of the protein in their TMEs. The distance matrix can either be used to classify the subjects into meaningful groups or can directly be used in a kernel machine regression framework for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling easier but interpretable analysis. We analyze a lung cancer dataset, finding the difference in the density of protein HLA-DR to be significantly associated with the overall survival and a triple-negative breast cancer dataset, analyzing the effects of multiple proteins on survival and recurrence. The reliability of our method is demonstrated through extensive simulation studies. Availability The associated R package can be found here, https://github.com/sealx017/DenVar. Supplementary information The Supplementary Material is attached.

show abstract

On Clustering for Cell Phenotyping in Multiplex Immunohistochemistry (mIHC) and Multiplexed Ion Beam Imaging (MIBI) Data

Cited by 5 publications

References 18 publications

MIAMI: Mutual Information-based Analysis of Multiplex Imaging data

MIAMI: Mutual Information-based Analysis of Multiplex Imaging data

SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA

DenVar: density-based variation analysis of multiplex imaging data

Contact Info

Product

Resources

About