Project description:Alzheimer's disease (AD) is a major public health priority with a large socioeconomic burden and complex etiology. The Alzheimer Disease Metabolomics Consortium (ADMC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) aim to gain new biological insights in the disease etiology. We report here an untargeted lipidomics of serum specimens of 806 subjects within the ADNI1 cohort (188 AD, 392 mild cognitive impairment and 226 cognitively normal subjects) along with 83 quality control samples. Lipids were detected and measured using an ultra-high-performance liquid chromatography quadruple/time-of-flight mass spectrometry (UHPLC-QTOF MS) instrument operated in both negative and positive electrospray ionization modes. The dataset includes a total 513 unique lipid species out of which 341 are known lipids. For over 95% of the detected lipids, a relative standard deviation of better than 20% was achieved in the quality control samples, indicating high technical reproducibility. Association modeling of this dataset and available clinical, metabolomics and drug-use data will provide novel insights into the AD etiology. These datasets are available at the ADNI repository at http://adni.loni.usc.edu/.

| S-EPMC6244184 | biostudies-literature

massPix: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics.

Project description:INTRODUCTION:Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools. OBJECTIVES:We have developed massPix-an R package for analysing and interpreting data from MSI of lipids in tissue. METHODS:massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries. RESULTS:Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering. CONCLUSION:massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.

| S-EPMC5608769 | biostudies-literature

Using control genes to correct for unwanted variation in microarray data.

Project description:Microarray expression studies suffer from the problem of batch effects and other unwanted variation. Many methods have been proposed to adjust microarray data to mitigate the problems of unwanted variation. Several of these methods rely on factor analysis to infer the unwanted variation from the data. A central problem with this approach is the difficulty in discerning the unwanted variation from the biological variation that is of interest to the researcher. We present a new method, intended for use in differential expression studies, that attempts to overcome this problem by restricting the factor analysis to negative control genes. Negative control genes are genes known a priori not to be differentially expressed with respect to the biological factor of interest. Variation in the expression levels of these genes can therefore be assumed to be unwanted variation. We name this method "Remove Unwanted Variation, 2-step" (RUV-2). We discuss various techniques for assessing the performance of an adjustment method and compare the performance of RUV-2 with that of other commonly used adjustment methods such as Combat and Surrogate Variable Analysis (SVA). We present several example studies, each concerning genes differentially expressed with respect to gender in the brain and find that RUV-2 performs as well or better than other methods. Finally, we discuss the possibility of adapting RUV-2 for use in studies not concerned with differential expression and conclude that there may be promise but substantial challenges remain.

| S-EPMC3577104 | biostudies-literature

Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies.

Project description:BackgroundHealthcare-associated infections (HAIs) represent a major Public Health issue. Hospital-based prevalence studies are a common tool of HAI surveillance, but data quality problems and non-representativeness can undermine their reliability.MethodsThis study proposes three algorithms that, given a convenience sample and variables relevant for the outcome of the study, select a subsample with specific distributional characteristics, boosting either representativeness (Probability and Distance procedures) or risk factors' balance (Uniformity procedure). A "Quality Score" (QS) was also developed to grade sampled units according to data completeness and reliability. The methodologies were evaluated through bootstrapping on a convenience sample of 135 hospitals collected during the 2016 Italian Point Prevalence Survey (PPS) on HAIs.ResultsThe QS highlighted wide variations in data quality among hospitals (median QS 52.9 points, range 7.98-628, lower meaning better quality), with most problems ascribable to ward and hospital-related data reporting. Both Distance and Probability procedures produced subsamples with lower distributional bias (Log-likelihood score increased from 7.3 to 29 points). The Uniformity procedure increased the homogeneity of the sample characteristics (e.g., - 58.4% in geographical variability). The procedures selected hospitals with higher data quality, especially the Probability procedure (lower QS in 100% of bootstrap simulations). The Distance procedure produced lower HAI prevalence estimates (6.98% compared to 7.44% in the convenience sample), more in line with the European median.ConclusionsThe QS and the subsampling procedures proposed in this study could represent effective tools to improve the quality of prevalence studies, decreasing the biases that can arise due to non-probabilistic sample collection.

| S-EPMC8088017 | biostudies-literature

VIRULIGN: fast codon-correct alignment and annotation of viral genomes.

Project description:SummaryVirus sequence data are an essential resource for reconstructing spatiotemporal dynamics of viral spread as well as to inform treatment and prevention strategies. However, the potential benefit of these applications critically depends on accurate and correctly annotated alignments of genetically heterogeneous data. VIRULIGN was built for fast codon-correct alignments of large datasets, with standardized and formalized genome annotation and various alignment export formats.Availability and implementationVIRULIGN is freely available at https://github.com/rega-cev/virulign as an open source software project.Supplementary informationSupplementary data is available at Bioinformatics online.

| S-EPMC6513156 | biostudies-literature

Race adjustments in clinical algorithms can help correct for racial disparities in data quality.

Project description:Despite ethical and historical arguments for removing race from clinical algorithms, the consequences of removal remain unclear. Here, we highlight a largely undiscussed consideration in this debate: varying data quality of input features across race groups. For example, family history of cancer is an essential predictor in cancer risk prediction algorithms but is less reliably documented for Black participants and may therefore be less predictive of cancer outcomes. Using data from the Southern Community Cohort Study, we assessed whether race adjustments could allow risk prediction models to capture varying data quality by race, focusing on colorectal cancer risk prediction. We analyzed 77,836 adults with no history of colorectal cancer at baseline. The predictive value of self-reported family history was greater for White participants than for Black participants. We compared two cancer risk prediction algorithms-a race-blind algorithm which included standard colorectal cancer risk factors but not race, and a race-adjusted algorithm which additionally included race. Relative to the race-blind algorithm, the race-adjusted algorithm improved predictive performance, as measured by goodness of fit in a likelihood ratio test (P-value: <0.001) and area under the receiving operating characteristic curve among Black participants (P-value: 0.006). Because the race-blind algorithm underpredicted risk for Black participants, the race-adjusted algorithm increased the fraction of Black participants among the predicted high-risk group, potentially increasing access to screening. More broadly, this study shows that race adjustments may be beneficial when the data quality of key predictors in clinical algorithms differs by race group.

| S-EPMC11348319 | biostudies-literature

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns.

Project description:Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

| S-EPMC7536087 | biostudies-literature

Reasons to not correct for leaching in TBI; Reply to Lind et al. (2022).

Project description:We believe that correcting for leaching in (terrestrial) litterbags studies such as the Tea Bag Index will result in more uncertainties than it resolves. This is mainly because leaching occurs in pulses upon changes in the environment and because leached material can still be mineralized after leaching. Furthermore, amount of material that potentially leaches from tea is comparable to other litter types. When correcting for leaching, it is key to be specific about the employed method, just like being specific about the study specific definition of decomposition.

| S-EPMC10262069 | biostudies-literature

Reply to "Structural requirements for RNA degradation by HIV-1 reverse transcriptase".

Project description: Not available

| S-EPMC4207619 | biostudies-literature