A5076114786- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
- Molecular Biology Techniques and Applications
- Single-cell and spatial transcriptomics
- Genetic Associations and Epidemiology
- Rheumatoid Arthritis Research and Therapies
- Gene Regulatory Network Analysis
- Cancer Genomics and Diagnostics
- Neural Networks and Applications
- Immune Cell Function and Interaction
- Machine Learning and Data Classification
- Machine Learning in Bioinformatics
- T-cell and B-cell Immunology
- Genomics and Chromatin Dynamics
- Chronic Lymphocytic Leukemia Research
- Artificial Intelligence in Healthcare
- Machine Learning in Healthcare
- Systemic Lupus Erythematosus Research
- Clinical practice guidelines implementation
- Autoimmune and Inflammatory Disorders Research
- Health Sciences Research and Education
- Explainable Artificial Intelligence (XAI)
- Health, Environment, Cognitive Aging
- Chronic Disease Management Strategies
- Medical Coding and Health Information
Allen Institute for Immunology
2023-2025
University of Arizona
2017-2021
Institute for Medical Informatics and Biostatistics
2018-2019
Abstract Age-associated changes in the T cell compartment are well described. However, limitations of current single-modal or bimodal single-cell assays, including flow cytometry, RNA-seq (RNA sequencing) and CITE-seq (cellular indexing transcriptomes epitopes by sequencing), have restricted our ability to deconvolve more complex cellular molecular changes. Here, we profile >300,000 single cells from healthy children (aged 11–13 years) older adults 55–65 using trimodal assay TEA-seq...
The generation and maintenance of protective immunity is a dynamic interplay between host environment that impacted by age. Understanding fundamental changes in the healthy immune system occur over lifespan critical developing interventions for age-related susceptibility to infections diseases. Here, we use multi-omic profiling (scRNA-seq, proteomics, flow cytometry) examined human peripheral 300 adults, with 96 young older adults followed two years yearly vaccination. resulting resource...
Objective This longitudinal case‐control study evaluated serum proteomics prior to a clinical diagnosis of rheumatoid arthritis (i.e. pre‐RA) evaluate biologic pathways disease development and inform prediction timing onset future disease. Methods Patients (cases, n=213) meeting the 1987 American College Rheumatology (ACR) classification criteria for RA, matched controls without RA (n=215) were identified in Department Defense Serum Repository. samples from cases pre‐ post‐RA tested...
Abstract The Allen Institute for Immunology was founded in 2018 to deeply profile the human immune system health and disease. As part of our mission understand how changes over time setting oncology, we recruited a cohort patients at diagnosis multiple myeloma (MM), followed these subjects throughout induction therapy, autologous stem cell transplant, after transplant recovery. We sampled same longitudinally, then performed profiling using scRNA-seq on peripheral blood cells, CITE-seq bone...
Abstract Multiple myeloma (MM) is a hematological malignancy characterized by an expansion of malignant plasma cells in the bone marrow. For newly diagnosed MM (NDMM), standard three-drug treatment regimens—such as combination bortezomib, lenalidomide, and dexamethasone (VRd)—and more recent inclusion fourth drug, anti-CD38 antibody immunotherapy, have significantly improved patient outcomes targeting multiple mechanisms tumorigenesis. This approach typically involves several cycles VRd...
Abstract Some autoimmune diseases, including rheumatoid arthritis (RA), are preceded by a critical subclinical phase of disease activity. Proactive clinical management is hampered lack biological understanding this ‘at-risk’ state and the changes underlying development. In cross-sectional longitudinal multi-omics study peripheral immunity in autoantibody-positive at-risk for RA period, we identified systemic inflammation, proinflammatory-skewed B cells, expanded Tfh17-like epigenetic bias...
Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying regulatory programs complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling technical drop-out with zero-inflated...
Gene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more ‘precision’ approach that integrates individual variability including ‘omics data into risk assessments, diagnoses, therapeutic decision making, whole transcriptome needs be interpreted meaningfully for single subjects. We propose an “all-against-one” framework uses biological replicates in isogenic conditions testing...
Abstract Calculating D ifferentially E xpressed G enes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two c onditions w ithout ( TCWR ) has been proposed, but not evaluated. Under conditions (e.g., unaffected tissue vs. tumor), differences transformed expression the proposed...
Abstract Background In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are features (i.e., transcript) than samples mice or human samples) in a study, poses major statistical challenges detection tasks traditional techniques underpowered high dimension. Second third order interactions these pose substantial combinatoric dimensional...
Abstract Background In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are features (i.e., transcripts) than samples mice or human samples) in a study, it poses major statistical challenges detection tasks traditional techniques underpowered high dimension. Second third order interactions these pose substantial combinatoric...
Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond small pools subjects implied by “precision medicine”. This manuscript proposes novel approach reference standard development to evaluate accuracy single-subject analyses metabolomes, proteomes, or transcriptomes. Since distributional...
Abstract Background Gene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more ‘precision’ approach that integrates individual variability including ‘omics data into risk assessments, diagnoses, therapeutic decision making, whole transcriptome analysis requires methodological advancements. One need is for users confidently be able make individual-level inferences from data. We propose...
Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond small pools subjects implied by “precision medicine”. This manuscript proposes novel approach reference standard development to evaluate accuracy single-subject analyses transcriptomes and offers extensions into proteomes metabolomes. In evaluation frameworks...
Abstract Motivation Identifying altered transcripts between very small human cohorts is particularly challenging and compounded by the low accrual rate of subjects in rare diseases or sub-stratified common disorders. Yet, single-subject studies (S3) can compare paired transcriptome samples drawn from same patient under two conditions (e.g. treated versus pre-treatment) suggest patient-specific responsive biomechanisms based on overrepresentation functionally defined gene sets. These improve...
<h3>Background</h3> RA-associated autoantibodies (RF, ACPA) are detectable before the onset of clinically-apparent inflammatory arthritis (IA) and classifiable RA (i.e. "Clinical RA"), defining a state known as 'At-Risk' for future RA. However, prevention trials in individuals this At-Risk have shown limited success prevention. A challenge to developing more effective preventive interventions is that immune phenotype remains incompletely understood. <h3>Objectives</h3> The purpose study was...
Abstract Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) has been increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying regulatory programs complex diseases. We developed MOCHA (Model-based single cell Open CHromatin Analysis) with advances over existing analysis tools, including: 1) improved identification of sample-specific open chromatin, 2) proper handling technical drop-out zero-inflated methods, 3)...