Validation and Quality Control of Protein Microarray-based Analytical Methods

Protein microarray technology is used mainly in the research laboratory and it is being used to uncover important diagnostic and prognostic markers that may one day emerge as routine clinical laboratory tests. It is important that these methods are subject to control procedures, in order to ensure that data of the highest quality are obtained. If quality is not controlled, the assay may yield erroneous results that would mask or confound meaningful diagnostic or prognostic associations. This chapter surveys the range of strategies designed to assure the analytical quality of protein microarray methods and it also highlights some of the potential pitfalls when moving these arrays into routine clinical practice. With the development of appropriate quality control and assurance measures, we anticipate protein microarray-based assays will be of substantial benefit in the future practice of laboratory medicine.     

popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data

The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters.Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR.     

光电阅读机在大人群现场调查中数据处理及质量控制 的应用

目的:探讨光电阅读机在大样本现场调查项目质量控制和数据录入中的作用.方法:使用光电阅读机读入《子宫颈癌危险因素调查表》信息并进行表格质量评估,核查调查表中调查对象编码缺失、重复及进行读入信息一致性比对.结果:在录入的70160份数据中,调查对象编码缺失率为0.87％,调查对象编码重复率为0.79％,信息一致率为99.47％.3个项目点连续三个月质量控制,表格不合格率略有下降.结论:在大人群现场调查项目中通过光电阅读机进行质量控制及数据录入,能快速准确的将纸质信息转化为电子信息,并对电子信息进行核查,能迅速将调查表评估结果和建议反馈给调查表填写人员,及时指导调查员改进调查项目的编写方法和调查表内容的填写方法,从而提高了调查表信息采集质量,促进项目科研数据真实可信.     

Meta-QC-Chain: Comprehensive and Fast Quality Control Method for Metagenomic Data

Next-generation sequencing(NGS) technology has revolutionized and significantly impacted metagenomic research.However,the NGS data usually contains sequencing artifacts such as low-quality reads and contaminating reads,which will significantly compromise downstream analysis.Many quality control(QC) tools have been proposed,however,few of them have been verified to be suitable or efficient for metagenomic data,which are composed of multiple genomes and are more complex than other kinds of NGS data.Here we present a metagenomic data QC method named Meta-QC-Chain.Meta-QC-Chain combines multiple QC functions:technical tests describe input data status and identify potential errors,quality trimming filters poor sequencing-quality bases and reads,and contamination screening identifies higher eukaryotic species,which are considered as contamination for metagenomic data.Most computing processes are optimized based on parallel programming.Testing on an 8-GB real dataset showed that Meta-QC-Chain trimmed low sequencing-quality reads and contaminating reads,and the whole quality control procedure was completed within 20 min.Therefore,Meta-QC-Chain provides a comprehensive,useful and high-performance QC tool for metagenomic data.Meta-QC-Chain is publicly available for free at:http://computationalbioenergy.org/meta-qc-chain.html.     

SeqSQC: A Bioconductor Package for Evaluating the Sample Quality of Next-generation Sequencing Data

As next-generation sequencing (NGS) technology has become widely used to identify genetic causal variants for various diseases and traits,a number of packages for checking NGS data quality have sprung up in public domains. In addition to the quality of sequencing data,sample quality issues,such as gender mismatch,abnormal inbreeding coefficient,cryptic relatedness,and population outliers,can also have fundamental impact on downstream analysis. However,there is a lack of tools specialized in identifying problematic samples from NGS data,often due to the limitation of sample size and variant counts. We developed SeqSQC,a Bioconductor package,to automate and accelerate sample cleaning in NGS data of any scale. SeqSQC is designed for efficient data storage and access,and equipped with interactive plots for intuitive data visualization to expedite the identification of problematic samples. SeqSQC is available at http://bioconductor. org/packages/SeqSQC.     

采用关键点控制和反馈调节,提高微生物学实验课质量

将全面质量管理理论引入微生物学实验课教学,形成以关键点控制和反馈调节为核心的教学质量保障系统,实践证明,可有效地促进学生积极思考,提高实验技能。对教学过程中“关键点”．“关键点控制”的含义和特性以及“反馈信号”、“反馈调节”的含义和类型做了系统的阐述。以具体实验为例,详细说明了实验课教学中关键点控制和反馈调节的实施过程,对于保证教学质量和客观、公正地分析评价教学效果具有重要意义。     

i2dash: Creation of Flexible,Interactive, and Web-based Dashboards for Visualization of Omics Data

Data visualization and interactive data exploration are important aspects of illustrating complex concepts and results from analyses of omics data. A suitable visualization has to be intuitive and accessible. Web-based dashboards have become popular tools for the arrangement, consolidation, and display of such visualizations. However, the combination of automated data processing pipelines handling omics data and dynamically generated, interactive dashboards is poorly solved. Here, we present i2dash, an R package intended to encapsulate functionality for the programmatic creation of customized dashboards. It supports interactive and responsive (linked) visualizations across a set of predefined graphical layouts. i2dash addresses the needs of data analysts/software developers for a tool that is compatible and attachable to any R-based analysis pipeline, thereby fostering the separation of data visualization on one hand and data analysis tasks on the other hand. In addition, the generic design of i2dash enables the development of modular extensions for specific needs. As a proof of principle, we provide an extension of i2dash optimized for single-cell RNA sequencing analysis, supporting the creation of dashboards for the visualization needs of such experiments. Equipped with these features, i2dash is suitable for extensive use in large-scale sequencing/bioinformatics facilities. Along this line, we provide i2dash as a containerized solution, enabling a straightforward large-scale deployment and sharing of dashboards using cloud services. i2dash is freely available via the R package archive CRAN (https://CRAN.R-project.org/package=i2dash).     

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.     

基于全过程医疗质量管理思想的临床数据存储库设计

全过程医疗质量管理是指医院对患者诊疗全流程的每个环节、每个处置都处于受控的状态,把影响医疗质量的问题降到最低,以取得最佳的医疗效果。全过程医疗质量管理除了对终末质量（大约占质控点的20%）进行控制外,重点是对过程中质量（大约占质控点的80%）进行控制,如何把全过程医疗质量管理与IT技术更好的结合起来,利用临床数据存储库（Clinical Data Repository,CDR）的建立来实现对医疗质量的实时管控,已成为当前医院信息化建设工作的主要任务。     

LogViewer: A Software Tool to Visualize Quality Control Parameters to Optimize Proteomics Experiments using Orbitrap and LTQ-FT Mass Spectrometers

Visualization tools that allow both optimization of instrument''s parameters for data acquisition and specific quality control (QC) for a given sample prior to time-consuming database searches have been scarce until recently and are currently still not freely available. To address this need, we have developed the visualization tool LogViewer, which uses diagnostic data from the RAW files of the Thermo Orbitrap and linear trap quadrupole-Fourier transform (LTQ-FT) mass spectrometers to monitor relevant metrics. To summarize and visualize the performance on our test samples, log files from RawXtract are imported and displayed. LogViewer is a visualization tool that allows a specific and fast QC for a given sample without time-consuming database searches. QC metrics displayed include: mass spectrometry (MS) ion-injection time histograms, MS ion-injection time versus retention time, MS² ion-injection time histograms, MS² ion-injection time versus retention time, dependent scan histograms, charge-state histograms, mass-to-charge ratio (M/Z) distributions, M/Z histograms, mass histograms, mass distribution, summary, repeat analyses, Raw MS, and Raw MS². Systematically optimizing all metrics allowed us to increase our protein identification rates from 600 proteins to routinely determine up to 1400 proteins in any 160-min analysis of a complex mixture (e.g., yeast lysate) at a false discovery rate of <1%. Visualization tools, such as LogViewer, make QC of complex liquid chromotography (LC)-MS and LC-MS/MS data and optimization of the instrument''s parameters accessible to users.     

抗体偶联药物的研究进展与质量控制

抗体偶联药物（antibody-drug conjugates,ADC）因其良好的靶向性及抗癌活性目前已成为抗肿瘤抗体药物研发的新热点和重要趋势,受到越来越多的关注。ADC药物由单克隆抗体、高效应的细胞毒性物质以及连接臂三部分组成,它将抗体的靶向性与细胞毒性药物的抗肿瘤作用相结合,可以降低细胞毒性抗肿瘤药物的不良反应,提高肿瘤治疗的选择性,还能更好地应对靶向单抗的耐药性问题。与传统单抗药物相比,因其结构复杂,ADC药物质量属性分析方法的建立具有更大的难度和特殊性。对抗体偶联药物的研发现状、质量属性分析方法和挑战以及质量控制要点进行了简要介绍,为ADC药物的研究和质量控制提供参考。     

细胞药物的标准及质量控制——细胞药物连载之四

细胞药物最终用于人体,必须建立相应的质量标准,进行质量控制。系统地贯彻到供者筛查、组织采集、细胞分离、培养、冻存、复苏、放行、运输、使用等全过程,确保产品的安全性、有效性和稳定性。近年来,我国逐渐改变了把细胞治疗作为第三类医疗技术管理的思路。一方面,已有第三类医疗技术取消行政审批;另一方面,又把除自体外的干细胞移植纳入药物管理,并建立了相应的质量标准和质量管理办法。     

微生物培养基质量控制技术和标准

微生物培养基的酸碱度、凝胶强度和选择性等直接影响到培养基的质量,在理化试验方法中采用连接可渗透陶器型液体接头的电极和平头电极或者连接微型探头的电极可分别测定液体和固体培养基的pH值,而采用Gelometer和the LFRA Texture Analyser可测定固体培养基的凝胶强度。在微生物学方法中固体培养基采用倾注平板法、涂布法、划线法（半定量法）、改良的Miles-Misra法等测定生长情况,液体培养基采用稀释法测定生长率,用目标菌和杂菌的混合菌株评价选择性增菌培养基的选择性,利用OD值评价液体培养基生长率等。ICFMH（国际食品微生物学和卫生学委员会培养基工作组）、ISO、FDA以及我国卫生部等相继制定了培养基质量控制的标准,但目前还没有一个系统的适合我国国情的培养基质量控制国家标准,以致各相关单位采用的标准不一致,所以制定培养基质量控制国家标准非常关键。     

大、小鼠微生物检测质量控制探讨

实验动物质量直接影响科研数据的准确性,通过定期微生物检测,可对实验动物质量进行控制,因此对检测结果进行质量控制是对检测工作进行日常管理和监督的有效和必要的手段。实验室通过总结近年检测工作经验,对微生物检测质量控制工作进行探讨,为实验动物质量检测提供相关资料。     

全自动膜片钳数据采集控制系统USB接口的设计

介绍全自动膜片钳数据采集控制系统USB2.0接口的设计。该系统采用USB2.0控制器CY7C68013,实现了PC机和膜片钳放大器的数据传输。详细介绍了USB硬件接口、固件程序设计以及上位机应用程序的设计。     

Assessment of Data Quality: Errors of Measurement and Errors of Process

As projects progress from pilot studies with few simple variables and small samples, the research process as a whole becomes qualitatively more complex and subject to an array of contamination by errors and mistakes. Data usually undergo a series of manipulations (e.g., recording, computer entry, transmission) prior to final statistical analysis. The process, then, consists of numerous operations only ending with eventual statistical analysis and write-up. We present a means of estimating the impact of process error in the same terms as psychometric reliability and discuss the implications for reducing the impact of errors on overall data quality.     

Rejoinder to Use of Principal Component Analysis and the GE -Biplot for the Graphical Exploration of Gene Expression Data

Summary This note is in response to Wouters et al. (2003, Biometrics 59, 1131–1139) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to determine principal component analyses that are useful for exploratory analysis of microarray data. We also present another biplot representation, the GE‐biplot (Gene Expression biplot), that is a useful method for exploring gene expression data with the major advantage of being able to aid interpretation of both the samples and the genes relative to each other.     

实验动物与兽用生物制品检验和研发

在兽用生物制品的质量检验和研发中,实验动物的应用极其普遍.其应用的历史悠久、使用面广,涉及的动物种类多、数量大.本文对应用动物进行兽用生物制品检验和研发中存在的问题进行了分析,并提出了建立大动物基地、保证供应,完善标准、保证质量,更新观念、完善法规、减少动物用量,加强管理、确保生物安全等方面的建议.