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SUMMARY - Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers.
AVAILABILITY AND IMPLEMENTATION - scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package.
SUPPLEMENTARY INFORMATION - Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press.
Importance - Atrial fibrillation (AF) is the most common arrhythmia affecting 1% of the population. Young individuals with AF have a strong genetic association with the disease, but the mechanisms remain incompletely understood.
Objective - To perform large-scale whole-genome sequencing to identify genetic variants related to AF.
Design, Setting, and Participants - The National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine Program includes longitudinal and cohort studies that underwent high-depth whole-genome sequencing between 2014 and 2017 in 18 526 individuals from the United States, Mexico, Puerto Rico, Costa Rica, Barbados, and Samoa. This case-control study included 2781 patients with early-onset AF from 9 studies and identified 4959 controls of European ancestry from the remaining participants. Results were replicated in the UK Biobank (346 546 participants) and the MyCode Study (42 782 participants).
Exposures - Loss-of-function (LOF) variants in genes at AF loci and common genetic variation across the whole genome.
Main Outcomes and Measures - Early-onset AF (defined as AF onset in persons <66 years of age). Due to multiple testing, the significance threshold for the rare variant analysis was P = 4.55 × 10-3.
Results - Among 2781 participants with early-onset AF (the case group), 72.1% were men, and the mean (SD) age of AF onset was 48.7 (10.2) years. Participants underwent whole-genome sequencing at a mean depth of 37.8 fold and mean genome coverage of 99.1%. At least 1 LOF variant in TTN, the gene encoding the sarcomeric protein titin, was present in 2.1% of case participants compared with 1.1% in control participants (odds ratio [OR], 1.76 [95% CI, 1.04-2.97]). The proportion of individuals with early-onset AF who carried a LOF variant in TTN increased with an earlier age of AF onset (P value for trend, 4.92 × 10-4), and 6.5% of individuals with AF onset prior to age 30 carried a TTN LOF variant (OR, 5.94 [95% CI, 2.64-13.35]; P = 1.65 × 10-5). The association between TTN LOF variants and AF was replicated in an independent study of 1582 patients with early-onset AF (cases) and 41 200 control participants (OR, 2.16 [95% CI, 1.19-3.92]; P = .01).
Conclusions and Relevance - In a case-control study, there was a statistically significant association between an LOF variant in the TTN gene and early-onset AF, with the variant present in a small percentage of participants with early-onset AF (the case group). Further research is necessary to understand whether this is a causal relationship.
For decades, histopathology with routine hematoxylin and eosin staining has been and remains the gold standard for reaching a morphologic diagnosis in tissue samples from humans and veterinary species. However, within the past decade, there has been exponential growth in advanced techniques for in situ tissue biomarker imaging that bridge the divide between anatomic and molecular pathology. It is now possible to simultaneously observe localization and expression magnitude of multiple protein, nucleic acid, and molecular targets in tissue sections and apply machine learning to synthesize vast, image-derived datasets. As these technologies become more sophisticated and widely available, a team-science approach involving subspecialists with medical, engineering, and physics backgrounds is critical to upholding quality and validity in studies generating these data. The purpose of this manuscript is to detail the scientific premise, tools and training, quality control, and data collection and analysis considerations needed for the most prominent advanced imaging technologies currently applied in tissue sections: immunofluorescence, in situ hybridization, laser capture microdissection, matrix-assisted laser desorption ionization imaging mass spectrometry, and spectroscopic/optical methods. We conclude with a brief overview of future directions for ex vivo and in vivo imaging techniques.
© The Author(s) 2018. Published by Oxford University Press on behalf of the National Academy of Sciences. All rights reserved. For permissions, please email: firstname.lastname@example.org.
Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.
PURPOSE - To report on radiosurgery delivery positioning accuracy in the treatment of tremor patients with frameless image guided radiosurgery using the linear accelerator (LINAC) based ExacTrac system and to describe quality assurance (QA) procedures used.
METHODS AND MATERIALS - Between 2010 and 2015, 20 patients underwent radiosurgical thalamotomy targeting the ventral intermediate nucleus for the treatment of severe tremor. The median prescription dose was 140 Gy (range, 120-145 Gy) in a single fraction. The median maximum dose was 156 Gy (range, 136-162 Gy). All treatment planning was performed with the iPlan system using a 4-mm circular cone with multiple arcs. Before each treatment, QA procedures were performed, including the imaging system. As a result of the extremely high dose delivered in a single fraction, a well-defined circular mark developed on the posttreatment magnetic resonance imaging (MRI). Eight of these 20 patients were selected to evaluate treatment localization errors because their circular marks were available in posttreatment MRI. In this study, the localization error is defined as the distance between the center of the intended target and the center of the posttreatment mark.
RESULTS - The mean error of distance was found to be 1.1 mm (range, 0.4-1.5 mm). The mean errors for the left-right, anteroposterior, and superoinferior directions are 0.5 mm, 0.6 mm, and 0.7 mm, respectively.
CONCLUSIONS - The result reported in this study includes all tremor patients treated at our institution when their posttreatment MRI data were available for study. It represents a direct confirmation of target positioning accuracy in radiosurgery with a LINAC-based frameless system and its limitations. This level of accuracy is only achievable with an appropriate QA program in place for a LINAC-based frameless radiosurgery system.
Copyright © 2016 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.
White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.
Copyright © 2016 American Society of Human Genetics. All rights reserved.
The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC-MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm ). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation.
The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has developed a database built on XNAT housing over a quarter of a million scans. The database provides framework for (1) rapid prototyping, (2) large scale batch processing of images and (3) scalable project management. The system uses the web-based interfaces of XNAT and REDCap to allow for graphical interaction. A python middleware layer, the Distributed Automation for XNAT (DAX) package, distributes computation across the Vanderbilt Advanced Computing Center for Research and Education high performance computing center. All software are made available in open source for use in combining portable batch scripting (PBS) grids and XNAT servers.
Copyright © 2015 Elsevier Inc. All rights reserved.
INTRODUCTION - High-yielding, automated production of a PET tracer that reflects proliferation, 3'-deoxy-3'-[(18)F]fluorothymidine ([(18)F]FLT), is reported using a modified Bioscan Coincidence FDG reaction module.
METHODS - Production of [(18)F]FLT was implemented through: (1) modification of an original FDG manifold; (2) application of an alternate time sequence; and (3) altered solid-phase extraction (SPE) purification. Quality control testing, including standard radiochemical figures of merit and preclinical positron emission tomography (PET) imaging, was carried out.
RESULTS - High decay-corrected yields of [(18)F]FLT (16-39%) were reproducibly obtained. The product exhibited very high specific activity (4586.9TBq/mmol; 123,969Ci/mmol) and radiochemical purity (>99%). Overall, the [(18)F]FLT produced in this manner was superior to typical productions that utilized a GE TRACERlab FXF-N reaction module. Additionally, purification with SPE cartridges, followed by manual elution, accelerated overall run time and resulted in a two-fold increase in [(18)F]FLT concentration. PET imaging showed the [(18)F]FLT produced by this method was highly suitable for non-invasive tumor imaging in mice.
CONCLUSIONS - The Bioscan Coincidence GE FDG Reaction Module was readily adapted to reproducibly provide [(18)F]FLT in high yield, specific activity, and radiochemical purity. The approach was suitable to provide sufficient amounts of material for preclinical studies.
Copyright © 2014 Elsevier Ltd. All rights reserved.