Automated Quality Control Procedure Research Articles

Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc

Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation.

Discrimination of powdered herbal teas by Vis/NIR spectral reflectance and chemometrics

Abstract The herbal tea market is projected to grow at an annual rate of 4.8 %, with the discrimination of these products appearing as an issue of food quality and safety. In this study the Vis/NIR spectroscopy combined with chemometrics was applied for discriminating five popular herbal teas (chamomile, boldo, lemon grass, carqueja, fennel) by using powdered samples. Dynamic sampling was applied for measuring the spectral signatures and different spectral pre-treatments were evaluated aiming at improving the discrimination accuracy. The Partial Least Squares Discriminant Analysis (PLS-DA) achieved high prediction accuracies (77.8–100 %), specificities (89.4–100 %) and sensitivities (66.1–100 %), with detrending and object-wise standardization pre-treatments correctly discriminating 100 % of the samples during the external validation. The Vis/NIR spectroscopy combined with chemometric analysis has great potential to discriminate powdered herbal teas, providing a non-destructive, fast, safe and chemical-free solution for automated quality control procedures in industries of tea processing.

A pipeline for RNA-seq based eQTL analysis with automated quality control procedures

BackgroundAdvances in the expression quantitative trait loci (eQTL) studies have provided valuable insights into the mechanism of diseases and traits-associated genetic variants. However, it remains challenging to evaluate and control the quality of multi-source heterogeneous eQTL raw data for researchers with limited computational background. There is an urgent need to develop a powerful and user-friendly tool to automatically process the raw datasets in various formats and perform the eQTL mapping afterward.ResultsIn this work, we present a pipeline for eQTL analysis, termed eQTLQC, featured with automated data preprocessing for both genotype data and gene expression data. Our pipeline provides a set of quality control and normalization approaches, and utilizes automated techniques to reduce manual intervention. We demonstrate the utility and robustness of this pipeline by performing eQTL case studies using multiple independent real-world datasets with RNA-seq data and whole genome sequencing (WGS) based genotype data.ConclusionseQTLQC provides a reliable computational workflow for eQTL analysis. It provides standard quality control and normalization as well as eQTL mapping procedures for eQTL raw data in multiple formats. The source code, demo data, and instructions are freely available at https://github.com/stormlovetao/eQTLQC.

SalaciaML: A Deep Learning Approach for Supporting Ocean Data Quality Control

We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions.

Quality Control of Quantitative High Throughput Screening Data.

Quantitative high throughput screening (qHTS) experiments can generate 1000s of concentration-response profiles to screen compounds for potentially adverse effects. However, potency estimates for a single compound can vary considerably in study designs incorporating multiple concentration-response profiles for each compound. We introduce an automated quality control procedure based on analysis of variance (ANOVA) to identify and filter out compounds with multiple cluster response patterns and improve potency estimation in qHTS assays. Our approach, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported subgroups. Applying CASANOVA to 43 publicly available qHTS data sets, we found that only about 20% of compounds with response values outside of the noise band have single cluster responses. The error rates for incorrectly separating true clusters and incorrectly clumping disparate clusters were both less than 5% in extensive simulation studies. Simulation studies also showed that the bias and variance of concentration at half-maximal response (AC50) estimates were usually within 10-fold when using a weighted average approach for potency estimation. In short, CASANOVA effectively sorts out compounds with “inconsistent” response patterns and produces trustworthy AC50 values.

A fiber coherence index for quality control of B-table orientation in diffusion MRI scans

PurposeThe diffusion MRI “b-vector” table describing the diffusion sensitization direction can be flipped and permuted in dimension due to different orientation conventions used in scanners and incorrect or improperly utilized file formats. This can lead to incorrect fiber orientation estimates and subsequent tractography failure. Here, we present an automated quality control procedure to detect when the b-table is flipped and/or permuted incorrectly. MethodsWe define a “fiber coherence index” to describe how well fibers are connected to each other, and use it to automatically detect the correct configuration of b-vectors. We examined the performance on 3981 research subject scans (Baltimore Longitudinal Study of Aging), 1065 normal subject scans of high image quality (Human Connectome Project), and 202 patient scans (Vanderbilt University Medical Center), as well as 9 in-vivo and 9 ex-vivo animal data. ResultsThe coherence index resulted in a 99.9% (3979/3981) and 100% (1065/1065) success rate in normal subject scans, 98% (198/202) in patient scans, and 100% (18/18) in both in-vivo and ex-vivo animal data in detecting the correct gradient table in datasets without severe image artifacts. The four failing cases (4/202) in patient scans, and two failures in healthy subject scans (2/3981), all showed prominent motion or signal dropout artifacts. ConclusionsThe fiber coherence measure can be used as an automatic quality assurance check in any diffusion analysis pipeline. Additionally, the success of this fiber coherence measure suggests potential broader applications, including evaluating data quality, or even providing diagnostic value as a biomarker of white matter integrity.

Identifying Doppler Velocity Contamination Caused by Migrating Birds. Part I: Feature Extraction and Quantification

Abstract Radar echoes from migrating birds can severely contaminate Doppler velocity measurements. For meteorological applications, especially quantitative applications in radar data assimilation, it is necessary to remove bird-contaminated velocity scans by using an automated identification technique. Such a technique should be also useful for ornithologists in selecting bird echoes automatically from radar scans. This technique can be developed in two steps: (i) extract the main features of migrating-bird echoes from reflectivity and Doppler velocity images and find proper parameters to quantify these features; (ii) utilize these parameters to develop an automated quality control procedure to identify and flag migrating-bird-contaminated Doppler velocity scans (sweeps). The first step is accomplished in this study (Part I) by identifying possible migrating-bird echoes in the level II data collected from the Oklahoma KTLX radar during the 2003 spring migrating season. The identifications are further verified by polarimetric radar measurements from the National Severe Storms Laboratory (NSSL) KOUN radar, Geostationary Operational Environmental Satellite (GOES) IR images, and rawinsonde measurements. Three proper parameters are found, and their histograms are prepared for the second step of development (reported in Part II).

A Novel Mode of Gleevec Binding Is Revealed by the Structure of Spleen Tyrosine Kinase

Spleen tyrosine kinase (Syk) is a non-receptor tyrosine kinase required for signaling from immunoreceptors in various hematopoietic cells. Phosphorylation of two tyrosine residues in the activation loop of the Syk kinase catalytic domain is necessary for signaling, a phenomenon typical of tyrosine kinase family members. Syk in vitro enzyme activity, however, does not depend on phosphorylation (activation loop tyrosine --> phenylalanine mutants retain catalytic activity). We have determined the x-ray structure of the unphosphorylated form of the kinase catalytic domain of Syk. The enzyme adopts a conformation of the activation loop typically seen only in activated, phosphorylated tyrosine kinases, explaining why Syk does not require phosphorylation for activation. We also demonstrate that Gleevec (STI-571, Imatinib) inhibits the isolated kinase domains of both unphosphorylated Syk and phosphorylated Abl with comparable potency. Gleevec binds Syk in a novel, compact cis-conformation that differs dramatically from the binding mode observed with unphosphorylated Abl, the more Gleevec-sensitive form of Abl. This finding suggests the existence of two distinct Gleevec binding modes: an extended, trans-conformation characteristic of tight binding to the inactive conformation of a protein kinase and a second compact, cis-conformation characteristic of weaker binding to the active conformation. Finally, the Syk-bound cis-conformation of Gleevec bears a striking resemblance to the rigid structure of the nonspecific, natural product kinase inhibitor staurosporine.

Patterns in student-student commenting

The virtual classroom under consideration supports students submitting exercise answers online and comments on exercise answers that include numerical scores. Automated quality control procedures track the student scores. The patterns of scores vary as a function of the pragmatic import of these student scores. Clear consequences of commenting must be enforced in the classroom before students will engage in fruitful commenting. The managerial problems that arise in courses that rely on extensive student-student commenting can be partially solved with automated tools that guide students to comment in fair and flexible ways.

The Argo Project: Global Ocean Observations for Understanding and Prediction of Climate Variability

Abstract : Phase 1 (9/99 - 9/02) and Phase 2 (7/00 - 6/02) of US Argo provided a total of 187 CTD profiling floats in the Pacific, Atlantic and Indian Oceans. Objectives were to demonstrate technological capabilities for fabrication and for deployment of float arrays in remote ocean locations (Phase 1) and to demonstrate the capability for manufacture and deployment of large float arrays (Phase 2). Recent technology developments in profiling floats were also implemented, including new generation salinity sensors, improved depth capability, and deployment techniques using fast ships and aircraft. Development of the U.S. Argo Data System was part of Phase 1, on a collaborative basis with international partners. Objectives are to make all Argo data publicly available within a day of collection, applying automated quality control procedures consistent with international Argo practices. Data appropriate for research applications and for comparison with climate change models are not available for several months since they need quality control by salinity experts and evaluation of data over many (10 day) float cycles. Phase 3 is a 5-year project (8/01 - 6/06) including full implementation of the US component of Argo. This report includes Phase 3/Year 2, which deployed 290 CTD profiling floats during CY2003 plus support for these deployments, data management activities and for national and international coordination of Argo. Float deployments targeted the Atlantic, Pacific, Indian, and Southern Oceans. Priorities for US float deployment are set by the US Argo Advisory Panel.

Automated Quality Control Procedure for the "Water Equivalent of Snow on the Ground" Measurement

Abstract Snow water equivalent (SWE) has been measured daily by the United States National Weather Service since 1952, whenever snow depth is 2 in. (5 cm) or greater. These data are used to develop design snow loads for buildings, for hydrological forecasting, and as an indicator of climate change. To date they have not been subjected comprehensively to quality control. An automated quality control procedure for these data is developed here, which checks daily SWE values for common data entry errors, values beyond reasonable limits, and consistency with daily precipitation and estimated melt. Potential effects of drifting in high winds and of the intrinsic microscale variability of SWE are also considered. An SWE measurement is declared suspicious if a sufficient discrepancy is found with respect to the expected SWE. Data values flagged as potential errors are checked manually. Results of applying the procedure to available SWE data from the northeastern UnitedStates are also summarized.

Quality control in PET systems employing 2-D modular detectors

The authors have developed and put into operation three automated quality control procedures for PET (positron emission tomography) with 2-D modular detectors that are designed to detect problems quickly with a minimum effort on the part of the user. These procedures check the following: (1) the stability of the system in terms of the efficiencies of the detectors in a module or block; (2) resolution and its uniformity throughout the field of view; and (3) the stability of the whole system in terms of the data reproducibility. The stability of the detector modules is checked in terms of the uniformity of detector efficiencies by conducting a chi-square type test on the count rates of each detector in a block. In order to check the resolution and its uniformity and also the placement of the data in the PET scans, a cylindrical lucite phantom was fabricated which has a set of axial holes placed at 2-cm intervals. A computer program has been developed to analyze the images produced by this phantom and determine deviations from the normal resolution and correct placement of the data. A third procedure examines the reproducibility of the system by scanning a cylinder filled with a solution of Ge-68 several times and comparing the count rates in a small region of interest. >

Numeric Databases for Chemical Analysis.

Databases for use with analytical chemistry instrumental techniques are surveyed, with attention to existing databases and collection efforts now underway, as well as needs for new data-bases. Collections of spectra for use in NMR, infrared spectroscopy, and mass spectroscopy are described. Using mass spectral databases as an example, a critique is presented of automated quality control procedures used to evaluate individual spectra in large collections; the kinds of problems which have been en-countered in using these procedures are discussed. Finally, a brief critical review is presented covering the application of computers to the identification of unknown compounds using spectral data-bases; again, algorithms used with mass spectrometry are taken as the example. Ongoing work at NIST with the NIST/EPA/MSDC Mass Spectral Database is concerned with many of these problems; recent developments are described.