Data visualizations to detect systematic errors in laboratory assay results

Lötsch, Jörn

doi:10.1002/prp2.369

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…The analysis of biomedical data by machine learning requires data that have been cleaned of analytical laboratory errors 8 , 9 and are adequately transformed and preferably free of missing values, anomalies, 10 or values below the limit of quantification (LOQ). 2 , 5 Although likelihood‐based models have been shown to be particularly suitable for handling values below LOQ in pharmacokinetics mixed‐effects models, 2 , 3 , 4 , 5 , 6 many proposed solutions to this problem in the area of pharmacological data science are data set specific 10 , 11 and must be tailored to analyses that use machine‐learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)

Malkusch

Hahnefeld

Lötsch

2021

CPT Pharmacom & Syst Pharma

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The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R‐based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine‐learning‐based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k‐nearest‐neighbors‐based imputation followed by k‐means clustering and density‐based spatial clustering of applications with noise. The R package provides a Shiny‐based web interface designed to be easy to use for non–data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r-project.org/web/packages/pguIMP/index.html).

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Section: Introductionmentioning

confidence: 99%

Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)

Malkusch

Hahnefeld

Lötsch

2021

CPT Pharmacom & Syst Pharma

Self Cite

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“…Because the number of QC samples available is often limited by budgetary constraints, many of the methods we use rely on visualization and conservative action (i.e., removing chemicals from our dataset or qualifying their interpretation unless there is evidence that the analytical method was accurate and precise) rather than on statistical methods. Whether statistical methods are incorporated or not, tabulating, visualizing, and communicating about QA/QC for environmental exposure measurements is important in order to reveal systematic error in the laboratory [20] or in the field and support future use of the data [6].…”

Section: Introductionmentioning

confidence: 99%

“…Examining results by batch or even by sample run order can reveal trends in QC samples over time, identifying systematic laboratory errors that may be missed by summary statistics or visualizations [20]. Shifts in method performance over time may require batch-specific corrections or dropping or flagging data from certain batches.…”

Section: Introductionmentioning

confidence: 99%

Wrangling environmental exposure data: guidance for getting the best information from your laboratory measurements

et al. 2019

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BackgroundEnvironmental health and exposure researchers can improve the quality and interpretation of their chemical measurement data, avoid spurious results, and improve analytical protocols for new chemicals by closely examining lab and field quality control (QC) data. Reporting QC data along with chemical measurements in biological and environmental samples allows readers to evaluate data quality and appropriate uses of the data (e.g., for comparison to other exposure studies, association with health outcomes, use in regulatory decision-making). However many studies do not adequately describe or interpret QC assessments in publications, leaving readers uncertain about the level of confidence in the reported data. One potential barrier to both QC implementation and reporting is that guidance on how to integrate and interpret QC assessments is often fragmented and difficult to find, with no centralized repository or summary. In addition, existing documents are typically written for regulatory scientists rather than environmental health researchers, who may have little or no experience in analytical chemistry.ObjectivesWe discuss approaches for implementing quality assurance/quality control (QA/QC) in environmental exposure measurement projects and describe our process for interpreting QC results and drawing conclusions about data validity.DiscussionOur methods build upon existing guidance and years of practical experience collecting exposure data and analyzing it in collaboration with contract and university laboratories, as well as the Centers for Disease Control and Prevention. With real examples from our data, we demonstrate problems that would not have come to light had we not engaged with our QC data and incorporated field QC samples in our study design. Our approach focuses on descriptive analyses and data visualizations that have been compatible with diverse exposure studies with sample sizes ranging from tens to hundreds of samples. Future work could incorporate additional statistically grounded methods for larger datasets with more QC samples.ConclusionsThis guidance, along with example table shells, graphics, and some sample R code, provides a useful set of tools for getting the best information from valuable environmental exposure datasets and enabling valid comparison and synthesis of exposure data across studies.

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Data visualizations to detect systematic errors in laboratory assay results

Cited by 2 publications

References 2 publications

Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)

Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP)

Wrangling environmental exposure data: guidance for getting the best information from your laboratory measurements

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