Automated quality control tool for high-content imaging data by building 2D prediction intervals on reference biosignatures.

Recent advances in automated microscopy and image analysis enables quantitative profiling of cellular phenotypes (Cell Painting). It paves the way for studying the broad effects of chemical perturbations on biological systems at large scale during lead optimization. Comparison of perturbation biosignatures with biosignatures of annotated compounds can inform on both on- and off-target effects. When building databases with phenotypic profiles of thousands of compounds, it is vital to control the quality of Cell Painting assays over time. A tool for this to our knowledge does not yet exist within the imaging community. In this paper, we introduce an automated tool to assess the quality of Cell Painting assays by quantifying the reproducibility of biosignatures of annotated reference compounds. The tool learns the biosignature of those treatments from a historical dataset, and subsequently, it builds a two-dimensional probabilistic quality control (QC) limit. The limit will then be used to detect aberrations in new Cell Painting experiments. The tool is illustrated using simulated data and further demonstrated on Cell Painting data of the A549 cell line. In general, the tool provides a sensitive, detailed and easy-to-interpret mechanism to validate the quality of Cell Painting assays.

Correction to: On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments.

An amendment to this paper has been published and can be accessed via the original article.

SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data.

SUMMARY: SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects. AVAILABILITY AND IMPLEMENTATION: The R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data.

BACKGROUND: Long non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets. RESULTS: Gene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines exhibit inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and benchmark RNA-seq datasets. The substandard performance of DE tools for lncRNAs applies also to low-abundance mRNAs. No single tool uniformly outperformed the others. Variability, number of samples, and fraction of DE genes markedly influenced DE tool performance. CONCLUSIONS: Overall, linear modeling with empirical Bayes moderation (limma) and a non-parametric approach (SAMSeq) showed good control of the false discovery rate and reasonable sensitivity. Of note, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in realistic settings such as in clinical cancer research. About half of the methods showed a substantial excess of false discoveries, making these methods unreliable for DE analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, giving guidance on selection of the optimal DE tool ( http://statapps.ugent.be/tools/AppDGE/ ).

Blood pressure control status and associated factors among adult hypertensive patients on outpatient follow-up at University of Gondar Referral Hospital, northwest Ethiopia: a retrospective follow-up study.

BACKGROUND: Large segments of the hypertensive population in the world are either untreated or inadequately treated. The incidence of heart failure and mortality from cardiovascular complications of hypertension is high among patients with uncontrolled blood pressure (BP). But BP control status of hypertensive patients has not been investigated in the study area. The study aimed to assess BP control status and determinant factors among adult hypertensive patients on antihypertensive medication attending outpatient follow-up at University of Gondar Referral Hospital, northwest Ethiopia. METHODS: An institution-based retrospective follow-up study was conducted from September 2015 to April 2016. Data were collected using a structured and pretested questionnaire adopted from the World Health Organization STEPwise approach. BP records of 6 months were used, and patients were classified as having controlled BP if their BP readings were <140/90 mmHg for all adults ≥18 years of age and <150/90 mmHg for adults aged ≥60 years. A generalized estimating equation was fitted, and the odds ratio with a 95% confidence level was used to determine the effect of covariates on BP control status. RESULTS: Among 395 participants, 50.4% (95% CI: 45-55) of them controlled their BP in the last 6 months of the survey. Physical activity (adjusted odds ratio [AOR]=1.95, 95% CI: 1.41-2.68), duration on antihypertensive drugs of 2-4 years (AOR=1.70, 95% CI: 1.13-2.56) and 5 years or more (AOR=1.96, 95% CI: 1.32-2.92), and high adherence (AOR=2.18, 95% CI: 1.14-4.15) to antihypertensive drugs were positively associated with BP control, while salt intake (AOR=0.67, 95% CI: 0.49-0.93), overweight (AOR=0.50, 95% CI: 0.36-0.68), and obesity (AOR=0.56, 95% CI: 0.36-0.87) were inversely associated with BP control. CONCLUSION: In this study, only half of the hypertensive patients controlled their BP. Thus, health care providers need to be made aware about the importance of counseling hypertensive patients on drug adherence, moderate physical activity, and salt restriction to improve BP control.