MSstatsQC 2.0: R/Bioconductor Package for Statistical Quality Control of Mass Spectrometry-Based Proteomics Experiments.

MSstatsQC is an R/Bioconductor package for statistical monitoring of longitudinal system suitability and quality control in mass spectrometry-based proteomics. MSstatsQC was initially designed for targeted selected reaction monitoring experiments. This paper presents an extension, MSstatsQC 2.0, that supports experiments with global data-dependent and data-independent acquisition. The extension implements data processing and analyses that are specific to these acquisition types. It relies on state-of-the-art methods of statistical process control to detect deviations from optimal performance of various metrics (such as intensity and retention time of chromatographic peaks) and to summarize the results across multiple metrics and analytes. Additionally, the web-based graphical user interface MSstatsQCgui, implemented as a separate R/Bioconductor package, provides a user-friendly way to visualize and report the results from MSstatsQC 2.0.

MSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experiments.

Selected Reaction Monitoring (SRM) is a powerful tool for targeted detection and quantification of peptides in complex matrices. An important objective of SRM is to obtain peptide quantifications that are (1) suitable for the investigation, and (2) reproducible across laboratories and runs. The first objective is achieved by system suitability tests (SST), which verify that mass spectrometric instrumentation performs as specified. The second objective is achieved by quality control (QC), which provides in-process quality assurance of the sample profile. A common aspect of SST and QC is the longitudinal nature of the data. Although SST and QC have received a lot of attention in the proteomic community, the currently used statistical methods are limited. This manuscript improves upon the statistical methodology for SST and QC that is currently used in proteomics. It adapts the modern methods of longitudinal statistical process control, such as simultaneous and time weighted control charts and change point analysis, to SST and QC of SRM experiments, discusses their advantages, and provides practical guidelines. Evaluations on simulated data sets, and on data sets from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium, demonstrated that these methods substantially improve our ability of real time monitoring, early detection and prevention of chromatographic and instrumental problems. We implemented the methods in an open-source R-based software package MSstatsQC and its web-based graphical user interface. They are available for use stand-alone, or for integration with automated pipelines. Although the examples focus on targeted proteomics, the statistical methods in this manuscript apply more generally to quantitative proteomics.

Predicting Prolonged Wound Drainage after Hemiarthroplasty for Hip Fractures: A Stacked Machine Learning Study.

Background: Prolonged wound drainage (PWD) is one of the most important reasons that increase the risk of early periprosthetic joint infection after arthroplasty. It is very important to evaluate the risk factors for PWD in the surgical field after arthroplasty surgery. This can be accomplished using machine learning or artificial intelligence methods. Our aim in this study was to compare machine learning methods in predicting possible PWD. Methods: The study was carried out on clinical, laboratory, and radiological data of 313 patients who underwent hemiarthroplasty (HA) for proximal femur fractures. We preprocessed the dataset and trained and tested machine learning methods using cross validation. We compared various machine learning algorithms (linear discriminant analysis, decision tree, k-nearest neighbors, gradient boosting machine, and logistic regression [LR]) based on performance measures. We also combined the most successful algorithms with a metaclassifier. To help understand the relationship between risk factors, we provided a risk factor severity ranking. Results: To estimate the risk of PWD, classification was performed with first-level classifiers and then integrated as a LR-based meta-learner stacking method. More performance improvements were achieved with the stacking method. Conclusions: We found that the stacking method was superior to other methods in PWD classification. We determined that the volume of fluid collected from the drain, morbid obesity class, blood transfusion, and body mass index score were the four most important risk factors according to stacking.

Development of the postural habits and awareness scale: a reliability and validity study.

Objectives. The aim of this study was to develop a scale that assesses postural awareness and habits, as well as to establish the validity and reliability thereof. Methods. The 19-item postural habits and awareness scale (PHAS) was developed. The scale has a score range of 0-95, with a higher score indicating good posture and awareness. A total of 278 healthy adults with an age range of 18-65 years were included in the study. The sociodemographic form, short form 36 health survey (SF-36) and body awareness questionnaire (BAQ) were used to test the validity and reliability of this newly developed scale. Results. From factor analyses, it was observed that the items clustered into four factors, which explained 55.99% of the variance. Cronbach's α for each factor of the scale varied between 0.619 and 0.832. A high correlation was observed regarding test-retest reliability of the scale (r = 0.905). Conclusion. This newly developed self-reported scale allows for the comprehensive determination of both postural habits and awareness together. The PHAS is a valid and reliable scale that can be used by professionals who are interested in posture.