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\n  \n 2022\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n MIAMI: mutual information-based analysis of multiplex imaging data.\n \n \n \n \n\n\n \n Seal, S.; and Ghosh, D.\n\n\n \n\n\n\n Bioinformatics,btac414. June 2022.\n \n\n\n\n
\n\n\n\n \n \n \"MIAMI:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{seal_miami_2022,\n\ttitle = {{MIAMI}: mutual information-based analysis of multiplex imaging data},\n\tissn = {1367-4803},\n\turl = {https://doi.org/10.1093/bioinformatics/btac414},\n\tdoi = {10.1093/bioinformatics/btac414},\n\tabstract = {Studying the interaction or co-expression of the proteins or markers in the tumor microenvironment 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 all the markers, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness.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 analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies.The associated R package can be found here, https://github.com/sealx017/MIAMI.Supplementary data are available at Bioinformatics online.},\n\turldate = {2022-07-05},\n\tjournal = {Bioinformatics},\n\tauthor = {Seal, Souvik and Ghosh, Debashis},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {btac414},\n}\n\n
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\n Studying the interaction or co-expression of the proteins or markers in the tumor microenvironment 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 all the markers, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness.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 analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies.The associated R package can be found here, https://github.com/sealx017/MIAMI.Supplementary data are available at Bioinformatics online.\n
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\n \n\n \n \n \n \n \n \n On clustering for cell-phenotyping in multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) data.\n \n \n \n \n\n\n \n Seal, S.; Wrobel, J.; Johnson, A. M.; Nemenoff, R. A.; Schenk, E. L.; Bitler, B. G.; Jordan, K. R.; and Ghosh, D.\n\n\n \n\n\n\n BMC Research Notes, 15(1): 215. June 2022.\n \n\n\n\n
\n\n\n\n \n \n \"OnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{seal_clustering_2022,\n\ttitle = {On clustering for cell-phenotyping in multiplex immunohistochemistry ({mIHC}) and multiplexed ion beam imaging ({MIBI}) data},\n\tvolume = {15},\n\tissn = {1756-0500},\n\turl = {https://doi.org/10.1186/s13104-022-06097-x},\n\tdoi = {10.1186/s13104-022-06097-x},\n\tabstract = {Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) images are usually phenotyped using a manual thresholding process. The thresholding is prone to biases, especially when examining multiple images with high cellularity.},\n\tnumber = {1},\n\tjournal = {BMC Research Notes},\n\tauthor = {Seal, Souvik and Wrobel, Julia and Johnson, Amber M. and Nemenoff, Raphael A. and Schenk, Erin L. and Bitler, Benjamin G. and Jordan, Kimberly R. and Ghosh, Debashis},\n\tmonth = jun,\n\tyear = {2022},\n\tpages = {215},\n}\n\n
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\n Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) images are usually phenotyped using a manual thresholding process. The thresholding is prone to biases, especially when examining multiple images with high cellularity.\n
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\n \n\n \n \n \n \n \n \n DenVar: density-based variation analysis of multiplex imaging data.\n \n \n \n \n\n\n \n Seal, S.; Vu, T.; Ghosh, T.; Wrobel, J.; and Ghosh, D.\n\n\n \n\n\n\n Bioinformatics Advances, 2(1): vbac039. January 2022.\n \n\n\n\n
\n\n\n\n \n \n \"DenVar:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{seal_denvar_2022,\n\ttitle = {{DenVar}: density-based variation analysis of multiplex imaging data},\n\tvolume = {2},\n\tissn = {2635-0041},\n\tshorttitle = {{DenVar}},\n\turl = {https://academic.oup.com/bioinformaticsadvances/article/doi/10.1093/bioadv/vbac039/6590640},\n\tdoi = {10.1093/bioadv/vbac039},\n\tabstract = {Abstract\n            \n              Summary\n              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.\n            \n            \n              Availability and implementation\n              The associated R package can be found here, https://github.com/sealx017/DenVar.\n            \n            \n              Supplementary information\n              Supplementary data are available at Bioinformatics Advances online.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2022-06-03},\n\tjournal = {Bioinformatics Advances},\n\tauthor = {Seal, Souvik and Vu, Thao and Ghosh, Tusharkanti and Wrobel, Julia and Ghosh, Debashis},\n\teditor = {Bateman, Alex},\n\tmonth = jan,\n\tyear = {2022},\n\tpages = {vbac039},\n}\n\n
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\n Abstract Summary 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 and implementation The associated R package can be found here, https://github.com/sealx017/DenVar. Supplementary information Supplementary data are available at Bioinformatics Advances online.\n
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\n \n\n \n \n \n \n \n \n Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies.\n \n \n \n \n\n\n \n Seal, S.; Datta, A.; and Basu, S.\n\n\n \n\n\n\n PLOS Genetics, 18(4): 1–19. April 2022.\n Publisher: Public Library of Science\n\n\n\n
\n\n\n\n \n \n \"EfficientPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{seal_efficient_2022,\n\ttitle = {Efficient estimation of {SNP} heritability using {Gaussian} predictive process in large scale cohort studies},\n\tvolume = {18},\n\turl = {https://doi.org/10.1371/journal.pgen.1010151},\n\tdoi = {10.1371/journal.pgen.1010151},\n\tabstract = {With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.},\n\tnumber = {4},\n\tjournal = {PLOS Genetics},\n\tauthor = {Seal, Souvik and Datta, Abhirup and Basu, Saonli},\n\tmonth = apr,\n\tyear = {2022},\n\tnote = {Publisher: Public Library of Science},\n\tpages = {1--19},\n}\n\n
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\n With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.\n
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\n \n\n \n \n \n \n \n \n Estimating SNP heritability in presence of population substructure in biobank-scale datasets.\n \n \n \n \n\n\n \n Lin, Z.; Seal, S.; and Basu, S.\n\n\n \n\n\n\n Genetics. February 2022.\n _eprint: https://academic.oup.com/genetics/advance-article-pdf/doi/10.1093/genetics/iyac015/42363060/iyac015.pdf\n\n\n\n
\n\n\n\n \n \n \"EstimatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{lin_estimating_2022,\n\ttitle = {Estimating {SNP} heritability in presence of population substructure in biobank-scale datasets},\n\tissn = {1943-2631},\n\turl = {https://doi.org/10.1093/genetics/iyac015},\n\tdoi = {10.1093/genetics/iyac015},\n\tabstract = {SNP heritability of a trait is measured as the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms (SNPs). Linear mixed models are routinely used to estimate SNP heritability for many complex traits, which requires estimation of a genetic relationship matrix (GRM) among individuals. Heritability is usually estimated by the restricted maximum likelihood (REML) or method of moments (MOM) approaches such as Haseman-Elston (HE) regression. The common practice of accounting for such population substructure is to adjust for the top few principal components of the GRM as covariates in the linear mixed model. This can get computationally very intensive on large biobank-scale datasets. Here we propose an MOM approach for estimating SNP heritability in presence of population substructure. Our proposed method is computationally scalable on biobank datasets and gives an asymptotically unbiased estimate of heritability in presence of discrete substructures. It introduces the adjustments for population stratification in a second-order estimating equation. It allows these substructures to vary in their SNP allele frequencies and in their trait distributions (means and variances) while the heritability is assumed to be the same across these substructures. Through extensive simulation studies and the application on 7 quantitative traits in the UK Biobank cohort, we demonstrate that our proposed method performs well in the presence of population substructure and much more computationally efficient than existing approaches.},\n\tjournal = {Genetics},\n\tauthor = {Lin, Zhaotong and Seal, Souvik and Basu, Saonli},\n\tmonth = feb,\n\tyear = {2022},\n\tnote = {\\_eprint: https://academic.oup.com/genetics/advance-article-pdf/doi/10.1093/genetics/iyac015/42363060/iyac015.pdf},\n}\n\n
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\n SNP heritability of a trait is measured as the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms (SNPs). Linear mixed models are routinely used to estimate SNP heritability for many complex traits, which requires estimation of a genetic relationship matrix (GRM) among individuals. Heritability is usually estimated by the restricted maximum likelihood (REML) or method of moments (MOM) approaches such as Haseman-Elston (HE) regression. The common practice of accounting for such population substructure is to adjust for the top few principal components of the GRM as covariates in the linear mixed model. This can get computationally very intensive on large biobank-scale datasets. Here we propose an MOM approach for estimating SNP heritability in presence of population substructure. Our proposed method is computationally scalable on biobank datasets and gives an asymptotically unbiased estimate of heritability in presence of discrete substructures. It introduces the adjustments for population stratification in a second-order estimating equation. It allows these substructures to vary in their SNP allele frequencies and in their trait distributions (means and variances) while the heritability is assumed to be the same across these substructures. Through extensive simulation studies and the application on 7 quantitative traits in the UK Biobank cohort, we demonstrate that our proposed method performs well in the presence of population substructure and much more computationally efficient than existing approaches.\n
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\n \n\n \n \n \n \n \n RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.\n \n \n \n\n\n \n Seal, S.; Li, Q.; Basner, E. B.; Saba, L. M; and Kechris, K.\n\n\n \n\n\n\n bioRxiv. 2022.\n Publisher: Cold Spring Harbor Laboratory\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{seal_rcfgl_2022,\n\ttitle = {{RCFGL}: {Rapid} {Condition} adaptive {Fused} {Graphical} {Lasso} and application to modeling brain region co-expression networks},\n\tjournal = {bioRxiv},\n\tauthor = {Seal, Souvik and Li, Qunhua and Basner, Elle Butler and Saba, Laura M and Kechris, Katerina},\n\tyear = {2022},\n\tnote = {Publisher: Cold Spring Harbor Laboratory},\n}\n
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\n  \n 2021\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Host methylation predicts SARS-CoV-2 infection and clinical outcome.\n \n \n \n\n\n \n Konigsberg, I. R; Barnes, B.; Campbell, M.; Davidson, E.; Zhen, Y.; Pallisard, O.; Boorgula, M. P.; Cox, C.; Nandy, D.; Seal, S.; and others\n\n\n \n\n\n\n Communications medicine, 1(1): 1–10. 2021.\n Publisher: Nature Publishing Group\n\n\n\n
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@article{konigsberg_host_2021,\n\ttitle = {Host methylation predicts {SARS}-{CoV}-2 infection and clinical outcome},\n\tvolume = {1},\n\tnumber = {1},\n\tjournal = {Communications medicine},\n\tauthor = {Konigsberg, Iain R and Barnes, Bret and Campbell, Monica and Davidson, Elizabeth and Zhen, Yingfei and Pallisard, Olivia and Boorgula, Meher Preethi and Cox, Corey and Nandy, Debmalya and Seal, Souvik and {others}},\n\tyear = {2021},\n\tnote = {Publisher: Nature Publishing Group},\n\tpages = {1--10},\n}\n\n
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\n  \n 2020\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Modeling the Dependence Structure in Genome Wide Association Studies of Binary Phenotypes in Family Data.\n \n \n \n\n\n \n Seal, S.; Boatman, J. A; McGue, M.; and Basu, S.\n\n\n \n\n\n\n Behavior genetics, 50(6): 423–439. 2020.\n Publisher: Springer\n\n\n\n
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@article{seal_modeling_2020,\n\ttitle = {Modeling the {Dependence} {Structure} in {Genome} {Wide} {Association} {Studies} of {Binary} {Phenotypes} in {Family} {Data}},\n\tvolume = {50},\n\tnumber = {6},\n\tjournal = {Behavior genetics},\n\tauthor = {Seal, Souvik and Boatman, Jeffrey A and McGue, Matt and Basu, Saonli},\n\tyear = {2020},\n\tnote = {Publisher: Springer},\n\tpages = {423--439},\n}\n\n
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