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Computational Methods for Single-Cell Proteomics

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Biomedical Data Science 1000年第1期6卷 47-71页

作者： Sophia M. Guldberg Trine Line Hauge Okholm Elizabeth E. McCarthy Matthew H. Spitzer 3Gladstone-UCSF Institute for Genomic Immunology San Francisco California USA 2Biomedical Sciences Graduate Program University of California San Francisco California USA 1Department of Otolaryngology–Head and Neck Surgery and Department of Microbiology and Immunology University of California San Francisco California USA email: matthew.spitzer@ucsf.edu 4Helen Diller Family Comprehensive Cancer Center University of California San Francisco California USA 5Institute for Human Genetics Division of Rheumatology Department of Medicine Medical Scientist Training Program and Biological and Medical Informatics Graduate Program University of California San Francisco California USA 6Parker Institute for Cancer Immunotherapy San Francisco California USA 7Chan Zuckerberg Biohub San Francisco California USA

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

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Mind the performance gap: Examining dataset shift during prospective validation

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arXiv 2021年

作者： Ötles, Erkin Oh, Jeeheh Li, Benjamin Bochinski, Michelle Joo, Hyeon Ortwine, Justin Shenoy, Erica Washer, Laraine Young, Vincent B. Rao, Krishna Wiens, Jenna Department of Industrial & Operations Engineering University of Michigan Medical Scientist Training Program University of Michigan Medical School Division of Computer Science & Engineering University of Michigan Nursing Informatics Michigan Medicine Department of Anesthesiology University of Michigan Medical School Department of Learning Health Sciences University of Michigan Medical School Infection Control Unit Division of Infectious Diseases Massachusetts General Hospital Harvard Medical School Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Department of Microbiology & Immunology University of Michigan Medical School

Once integrated into clinical care, patient risk stratification models may perform worse compared to their retrospective performance. To date, it is widely accepted that performance will degrade over time due to changes in care processes and patient populations. However, the extent to which this occurs is poorly understood, in part because few researchers report prospective validation performance. In this study, we compare the 2020-2021 (’20-’21) prospective performance of a patient risk stratification model for predicting healthcare-associated infections to a 2019-2020 (’19-’20) retrospective validation of the same model. We define the difference in retrospective and prospective performance as the performance gap. We estimate how i) "temporal shift", i.e., changes in clinical workflows and patient populations, and ii) "infrastructure shift", i.e., changes in access, extraction and transformation of data, both contribute to the performance gap. Applied prospectively to 26,864 hospital encounters during a twelve-month period from July 2020 to June 2021, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.767 (95% confidence interval (CI): 0.737, 0.801) and a Brier score of 0.189 (95% CI: 0.186, 0.191). Prospective performance decreased slightly compared to’19-’20 retrospective performance, in which the model achieved an AUROC of 0.778 (95% CI: 0.744, 0.815) and a Brier score of 0.163 (95% CI: 0.161, 0.165). The resulting performance gap was primarily due to infrastructure shift and not temporal shift. So long as we continue to develop and validate models using data stored in large research data warehouses, we must consider differences in how and when data are accessed, measure how these differences may negatively affect prospective performance, and work to mitigate those differences. Copyright © 2021, The Authors. All rights reserved.

关键词： Data warehouses

Explainable AI for trees: From local explanations to global understanding

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arXiv 2019年

作者： Lundberg, Scott M. Erion, Gabriel Chen, Hugh DeGrave, Alex Prutkin, Jordan M. Nair, Bala Katz, Ronit Himmelfarb, Jonathan Bansal, Nisha Lee, Su-In Paul G. Allen School of Computer Science and Engineering University of Washington Medical Scientist Training Program University of Washington Division of Cardiology Department of Medicine University of Washington Department of Anesthesiology and Pain Medicine University of Washington Harborview Injury Prevention and Research Center University of Washington Kidney Research Institute Division of Nephrology Department of Medicine University of Washington

Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are the most popular non-linear predictive models used in practice today, yet comparatively little attention has been paid to explaining their predictions. Here we significantly improve the interpretability of tree-based models through three main contributions: 1) The first polynomial time algorithm to compute optimal explanations based on game theory. 2) A new type of explanation that directly measures local feature interaction effects. 3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to i) identify high magnitude but low frequency non-linear mortality risk factors in the general US population, ii) highlight distinct population sub-groups with shared risk characteristics, iii) identify non-linear interaction effects among risk factors for chronic kidney disease, and iv) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model's performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision trees

Cerebrospinal fluid ratios with Aβ₄₂ predict preclinical brain β-amyloid accumulation

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Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring 2016年 2卷 27-38页

作者： Racine, Annie M. Koscik, Rebecca L. Nicholas, Christopher R. Clark, Lindsay R. Okonkwo, Ozioma C. Oh, Jennifer M. Hillmer, Ansel T. Murali, Dhanabalan Barnhart, Todd E. Betthauser, Tobey J. Gallagher, Catherine L. Rowley, Howard A. Dowling, N. Maritza Asthana, Sanjay Bendlin, Barbara B. Blennow, Kaj Zetterberg, Henrik Carlsson, Cynthia M. Christian, Bradley T. Johnson, Sterling C. Alzheimer's Disease Research Center University of Wisconsin School of Medicine and Public Health Madison WI United States Neuroscience Training Program University of Wisconsin-Madison Madison WI United States Neuroscience and Public Policy Program University of Wisconsin-Madison Madison WI United States Institute on Aging University of Wisconsin-Madison Madison WI United States Wisconsin Alzheimer's Institute University of Wisconsin School of Medicine and Public Health Madison WI United States Geriatric Research Education and Clinical Center William S. Middleton Memorial Veterans Hospital Madison WI United States Department of Medical Physics University of Wisconsin School of Medicine and Public Health Madison WI United States Waisman Laboratory for Brain Imaging and Behavior University of Wisconsin-Madison Madison WI United States Department of Psychiatry University of Wisconsin School of Medicine and Public Health Madison WI United States Department of Radiology University of Wisconsin School of Medicine and Public Health Madison WI United States Institute of Neurology University College London London United Kingdom Clinical Neurochemistry Laboratory Institute of Neuroscience and Physiology The Sahlgrenska Academy at the University of Gothenburg Mölndal Sweden

Introduction: Biomarkers are urgently needed for the critical yet understudied preclinical stage of Alzheimer's disease (AD). Methods: Cerebrospinal fluid (CSF) collection, [C-11]Pittsburgh compound B (PiB) amyloid imaging, and magnetic resonance imaging were acquired in 104 cognitively healthy adults enriched with risk for sporadic AD. Image-derived cerebral β-amyloid (Aβ) burden, measured concurrently and longitudinally, was regressed on CSF measures of Aβ, neural injury, and inflammation, as well as ratios with Aβ₄₂. Linear mixed-effects regression was used to model the effect of the CSF measures that predicted longitudinal brain amyloid accumulation on longitudinal cognitive decline, measured by memory test scores. Results: At baseline, Aβ₄₂/Aβ₄₀ and all CSF ratios to Aβ₄₂ were associated with PiB binding in AD-vulnerable regions. Longitudinally, Aβ₄₂/Aβ₄₀ and ratios of total tau (t-tau), phosphorylated-tau (p-tau), neurofilament light protein, and monocyte chemoattractant protein-to Aβ₄₂ were associated with increased Aβ deposition over 2 years, predominantly in lateral parietal and temporal cortex. However, these CSF ratios were not significantly associated with cognitive decline, and the effect seems to be largely driven by Aβ₄₂ in the denominator. Discussion: These results corroborate previous findings that t-tau/Aβ₄₂ and p-tau/Aβ₄₂ are the strongest candidate biomarkers during the preclinical time frame. They support a framework in which neural injury and amyloid deposition are likely occurring simultaneously. It may be that neurodegenerative processes influence progressive amyloid accumulation, even in the preclinical time frame. CSF biomarkers for nonspecific axonal injury and inflammation may provide more information at more advanced stages of the preclinical time course. © 2016.

关键词： Alzheimer's disease Amyloid Aβ42 Biological parametric mapping Biomarkers Cerebrospinal fluid Linear mixed-effects MCP-1 NFL PET PiB Preclinical AD Tau YKL-40

A Dataset of Anatomical Environments for medical Robots: Modeling Respiratory Deformation

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arXiv 2023年

作者： Fried, Inbar Hoelscher, Janine Akulian, Jason A. Alterovitz, Ron The Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States The Medical Scientist Training Program University of North Carolina School of Medicine Chapel HillNC27599 United States The Division of Pulmonary Diseases and Critical Care Medicine The University of North Carolina Chapel HillNC27599 United States

Anatomical models of a medical robot’s environment can significantly help guide design and development of a new robotic system. These models can be used for benchmarking motion planning algorithms, evaluating controllers, optimizing mechanical design choices, simulating procedures, and even as resources for data generation. Currently, the time-consuming task of generating these environments is repeatedly performed by individual research groups and rarely shared broadly. This not only leads to redundant efforts, but also makes it challenging to compare systems and algorithms accurately. In this work, we present a collection of clinically-relevant anatomical environments for medical robots operating in the lungs. Since anatomical deformation is a fundamental challenge for medical robots operating in the lungs, we describe a way to model respiratory deformation in these environments using patient-derived data. We share the environments and deformation data publicly by adding them to the medical Robotics Anatomical Dataset (Med-RAD), our public dataset of anatomical environments for medical robots. © 2023, CC BY-NC-SA.

关键词： Machine design

A Clinical Dataset for the Evaluation of Motion Planners in medical Applications

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arXiv 2022年

作者： Fried, Inbar Akulian, Jason A. Alterovitz, Ron The Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States The Medical Scientist Training Program University of North Carolina School of Medicine Chapel HillNC27599 United States The Division of Pulmonary Diseases and Critical Care Medicine The University of North Carolina Chapel HillNC27599 United States

The prospect of using autonomous robots to enhance the capabilities of physicians and enable novel procedures has led to considerable efforts in developing medical robots and incorporating autonomous capabilities. Motion planning is a core component for any such system working in an environment that demands near perfect levels of safety, reliability, and precision. Despite the extensive and promising work that has gone into developing motion planners for medical robots, a standardized and clinically-meaningful way to compare existing algorithms and evaluate novel planners and robots is not well established. We present the medical Motion Planning Dataset (Med-MPD), a publicly-available dataset of real clinical scenarios in various organs for the purpose of evaluating motion planners for minimally-invasive medical robots. Our goal is that this dataset serve as a first step towards creating a larger robust medical motion planning benchmark framework, advance research into medical motion planners, and lift some of the burden of generating medical evaluation data. © 2022, CC BY-SA.

关键词： Motion planning

Modular, Topographically Patterned, Biomimetic Poly(Ethylene Glycol) Hydrogels as Customized Scaffolds for Organoid Culture

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SSRN

SSRN 2020年

作者： Wilson, Reid L. Swaminathan, Ganesh Ettayebi, Khalil Bomidi, Carolyn Zeng, Xi-Lei Blutt, Sarah E. Estes, Mary K. Grande-Allen, K. Jane Department of Bioengineering Rice University 6100 Main Street HoustonTX77005 United States Strand Life Sciences Department of Molecular Virology and Microbiology Baylor College of Medicine 1 Baylor Plaza HoustonTX77030 United States Medical Scientist Training Program Baylor College of Medicine 1 Baylor Plaza HoustonTX77030 United States

The recent development of stem cell-derived, organotypic in vitro models, known as organoids, has revolutionized our ability to study important biological processes in vitro. However, their continued development is limited by the failure of the hydrogel matrices in which they are grown to adequately replicate the tissue-specific ECM cues they experience in their native in vivo environment. Here, we present a highly customizable, modular hydrogel scaffold that can incorporate tissue-specific cues from the extracellular matrix. We demonstrate that these scaffolds can be functionalized with a wide variety of cell adhesion molecules, including peptides and full-length proteins, and can support the attachment and growth of intestinal epithelials organoids, a model organoid system. We also found that these scaffolds can be patterned with large, high-aspect ratio topographical features that mimic anatomical structures (such as intestinal villi) found in vivo. Finally, we show that organoids cultured on these hydrogel scaffolds retain their capacity for multi-lineage differentiation and their ability to model enteric infections. Together, these findings are an excellent proof-of-concept that such hydrogel scaffolds can facilitate the development of organoid models of many organ systems and improve our ability to study a variety of important developmental and pathological processes. © 2020, The Authors. All rights reserved.

关键词： Peptides

1683. Empathy Scale Validation Among Expectant Seroconcordant Couples Enrolled in HIV Care and Treatment in Zambézia Province, Mozambique

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Open Forum Infectious Diseases 2019年第SUPPLEMENT_2期6卷 S616–S616页

作者： Sack, Daniel DeSchacht, Caroline Erin, Graves Aaron, Kipp Barreto, Ezequiel Van Rompaey, Sara Audet, Carolyn Vanderbilt Medical Scientist Training Program Nashville Tennessee Friends in Global Health Maputo Maputo Mozambique Vanderbilt University Nashville Tennessee

Background Among patients enrolled in HIV care and treatment in rural Mozambique, 30% abandon treatment within a year. A cluster randomized controlled trial assessing the impact of couple-based vs. individual treatment for concordant couples on viral suppression (the HoPS+ trial) hypothesizes that harnessing family support will improve patient outcomes. Individuals with high levels of empathy will likely provide greater social support for treatment retention and adherence. This study validates a locally tailored version of the interpersonal reactivity index (IRI)—cognitive empathy (CE) and affective empathy (AE)—among expectant parents living with HIV in Zambézia province, Mozambique. Methods Using baseline data from 558 participants from the HOPS+ trial, we used a maximum likelihood exploratory factor analysis with a promax oblique rotation to assess the culturally relevant questions from the IRI. We examined discriminant and construct validity through analysis of subscale relationships by sex, age, education, and depression and intra-person reliability over time with an interclass correlation model ( n = 119). Results Our participants live in 6 districts and receive health care at 24 health facilities. The median age was 25 (IQR: 22 to 30), 50% were female, and 44% were single. Participants had a median of 5 years of formal education (IQR: 2–7). Half of them report their occupation as “farmer” and 17% screened positive for depression. On a scale of 0–4, the median baseline CE score was 2.6 (IQR: 1.9–3.2) and the median baseline AE score was 1.9 (IQR: 1–2.6). Males (2.6 vs. 2.4, P < 0.01), participants who finished primary school (2.7 vs. 2.5, P < 0.01), and older participants (2.6 vs. 2.5, P = 0.04) had higher CE scores, while depressed participants had higher AE scores (2.3 vs. 1.8, P < 0.01). We found moderate stability over time (CE ICC: 0.63, AE ICC: 0.54) in a subset of 119 study participants. Conclusion While depression is associated with 12.5% higher AE sco

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Women with thoracoabdominal aortic aneurysms have increased frailty and more complex aortic anatomy compared with men

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European Journal of Vascular & Endovascular Surgery 2022年第1期64卷 e7-e7页

作者： Edman, Natasha I., MA Zettervall, Sara L., MD, MPH Dematteis, Maianna N., BS Ghaffarian, Amir, MD Shalhub, Sherene, MD, MPH Sweet, Matthew P., MD, MS Division of Vascular Surgery Department of Surgery University of Washington Seattle WA Medical Scientist Training Program University of Washington Seattle WA