A5004736211- Genetic Associations and Epidemiology
- Single-cell and spatial transcriptomics
- Extracellular vesicles in disease
- Genomics and Phylogenetic Studies
- Adipose Tissue and Metabolism
- Genomics and Rare Diseases
- Gene expression and cancer classification
- Genomics and Chromatin Dynamics
- Adipokines, Inflammation, and Metabolic Diseases
- Genetic Mapping and Diversity in Plants and Animals
- Epigenetics and DNA Methylation
- Cardiac, Anesthesia and Surgical Outcomes
- Cancer-related molecular mechanisms research
- Machine Learning in Healthcare
- RNA Research and Splicing
- Healthcare Policy and Management
- COVID-19 and healthcare impacts
- Hip and Femur Fractures
- SARS-CoV-2 detection and testing
- MicroRNA in disease regulation
- Genetic Syndromes and Imprinting
- RNA modifications and cancer
- Bioinformatics and Genomic Networks
- Genetics, Bioinformatics, and Biomedical Research
- Healthcare Operations and Scheduling Optimization
University of California, Los Angeles
2016-2021
Abstract We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus (snRNA-seq) data to generate reference expression profile and learn gene-specific transformations robustly decompose data. These significantly improve decomposition performance compared existing methods when there is significant technical variation the generation of observed Importantly,...
Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts ambient RNA, which can lead biased downstream analyses, such as identification spurious types if overlooked. present a novel approach quantify and filter droplets experiments, called Debris Identification using Expectation Maximization (DIEM). Our...
Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based exist. Our aim was to develop evaluate a machine learning algorithm diagnose COVID-19 inpatient setting. The based on basic demographic laboratory features serve as screening tool at hospitals where scarce or unavailable. We used retrospectively collected data from UCLA Health System Los Angeles, California. included all emergency room cases receiving PCR who also had...
Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes insulin resistance. To disentangle whether causally drives resistance already in non-diabetic individuals, we utilized UK Biobank METSIM cohort perform a Mendelian randomization (MR) analyses individuals. Our results suggest that both systemic are caused by (p = 1.2×10−3 p 3.1×10−24). As reflects amount of body fat, next studied how adipose tissue affects We performed bulk...
Many disease risk loci identified in genome-wide association studies are present non-coding regions of the genome. Previous have found enrichment expression quantitative trait (eQTLs) loci, indicating that identifying causal variants for gene is important elucidating genetic basis not only but also complex traits. However, detecting challenging due to correlation among known as linkage disequilibrium (LD) and presence multiple within a locus. Although several fine-mapping approaches been...
Abstract We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) data to generate reference expression profile and learn gene-specific transformations robustly decompose data. These significantly improve decomposition performance compared existing methods when there is significant technical variation the generation of observed Importantly, methods, our extremely efficient,...
Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset these variants, however, may have poor quality due to limitations NGS or variant callers. In studies that analyze large number sequenced individuals, it is critical detect and remove those with as they cause spurious findings. this paper, we ForestQC, statistical tool for performing control on identified from data by combining traditional filtering approach machine...
Abstract Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization solid tissues. Contrary to single-cell seq (scRNA-seq), we observe that snRNA-seq is commonly subject contamination by high amounts extranuclear background RNA, which can lead identification spurious types downstream clustering analyses if overlooked. We present a novel approach remove debris-contaminated droplets experiments,...
ABSTRACT Next-generation sequencing technology (NGS) enables discovery of nearly all genetic variants present in a genome. A subset these variants, however, may have poor quality due to limitations or variant calling algorithms. In studies that analyze large number sequenced individuals, it is critical detect and remove those with as they cause spurious findings. this paper, we statistical approach for performing control on identified from NGS data by combining traditional filtering machine...
Late-onset Alzheimer’s disease (LOAD) is the most common type of dementia causing irreversible brain damage to elderly and presents a major public health challenge. Clinical research genome-wide association studies have suggested potential contribution endocytic pathway AD, with an emphasis on loci. However, rare variants in this AD has not been thoroughly investigated. In study, we focused effect by first applying rare-variant gene-set burden analysis using genes over 3,000 individuals...
Abstract Many disease risk loci identified in genome-wide association studies are present non-coding regions of the genome. It is hypothesized that these variants affect complex traits by acting as expression quantitative trait (eQTLs) influence nearby genes. This indicates many causal for likely to be gene expression. Hence, identifying important elucidating genetic basis not only but also traits. However, detecting challenging due correlation among known linkage disequilibrium (LD) and...
Abstract Background Predicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting outcomes, obtaining this classification requires clinician to review patient’s records. Our goal here create an improved risk...
Abstract Next-generation sequencing has allowed genetic studies to collect genome data from a large number of individuals. However, raw are not usually interpretable due fragmentation the and technical biases; therefore, analysis these requires many computational approaches. First, for each sequenced individual, aligned further processed account biases. Then, variant calling is performed obtain information on positions variants their corresponding genotypes. Quality control (QC) applied...
Abstract We benchmarked two approaches for the detection of cell-type-specific differential DNA methylation: Tensor Composition Analysis (TCA) and a regression model with interaction terms (CellDMC). Our experiments alongside rigorous mathematical explanations show that TCA is superior over CellDMC, thus resolving recent criticisms suggested by Jing et al. Following misconceptions colleagues modelling cell-type-specificity application TCA, we further discuss best practices performing...
Calling differential methylation at a cell-type level from tissue-level bulk data is fundamental challenge in genomics that has recently received more attention. These studies most often aim identifying statistical associations rather than causal effects. However, existing methods typically make an implicit assumption about the direction of effects, and thus far, little to no attention been given fact this directionality may not hold can consequently affect power control for false positives....
Abstract During the initial wave of COVID-19 pandemic in United States, hospitals took drastic action to ensure sufficient capacity, including canceling or postponing elective procedures, expanding number available intensive care beds and ventilators, creating regional overflow hospital capacity. However, most locations actual patients did not reach projected surge leaving available, unused As a result, may have delayed needed lost substantial revenue. These recommendations were made based...
Abstract Symptom screening is a widely deployed strategy to mitigate the COVID-19 pandemic and many public health authorities are mandating its use by employers for all employees in workplace. While symptom has benefit of reducing number infected individuals workplace, it raises some inherently difficult privacy issues as traditional approach requires employer collect data from each employee which essentially medical information. In this paper, we describe system implement Cryptographic...
Since the first human genome was sequenced in 2001, there has been a rapid growth number of bioinformatic methods to process and analyze next-generation sequencing (NGS) data for research clinical studies that aim identify genetic variants influencing diseases traits. To achieve this goal, one needs call from NGS data, which requires multiple computationally intensive analysis steps. Unfortunately, is lack an open-source pipeline can perform all these steps on manner, fully automated,...