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122 results for “confounding”
Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis
<p>Contents</p> <ol> <li><strong>moccasin_paper_env.yaml</strong>: conda environment file with R and Python packages and modules needed to reproduce analyses.</li> <li><strong>FigureReproduction.zip</strong>: data and code to reproduce main and supplemental figures.</li> <li><strong>MOCCASIN_ExampleDataset.zip</strong>: A small subset of the simulated data with example code to run MOCCASIN.</li> <li><strong>encode_corrected.zip</strong>: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI).</li> </ol> <p> </p> <p> </p> <p>(1) <strong>moccasin_paper_env.yaml</strong></p> <p>Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced.</p> <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> <p><br> The only Python packages not included in this environment are MAJIQ & VOILA. Please see majiq.biocipers.org for installation instructions.</p> <p> </p> <p> </p> <p>(2) <strong>FigureReproduction.zip</strong></p> <p>Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures.</p> <p> </p> <p> </p> <p>(3) <strong>MOCCASIN_ExampleDataset.zip</strong></p> <p>Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an "explore unknown residuals" analysis as described in the detailed methods in the supplemental of the paper. </p> <p> </p> <p> </p> <p>(4) <strong>encode_corrected.zip</strong></p> <p>Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix "BeforeMOCCASIN" or "AfterMOCCASIN" and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment. </p>
Library size confounds biology in spatial transcriptomics data
<p>This dataset contains annotated sub-cellular localised spatial measurements from the Visium, Xenium and CosMx platforms. Specifically, it includes datasets analysed in the publication Bhuva et. al, 2023 titled "Library size confounds biology in spatial transcriptomics data". Raw transcript detections are presented. Data is best accessed through the accompanying <em>SubcellularSpatialData</em> R/Bioconductor package. Region files used to annotate individual transcript detections are presented in the form of <a href="https://geojson.org/">GeoJSON</a> files. </p>
Supporting data: Reporting phenotypes in model organisms when considering body size as a potential confounder.
<p>This directory contains the data and associated scripts used to generate the figures in the manuscript "Reporting phenotypes in model organisms when considering body size as a potential confounder." submitted to the Journal of Biomedical Semantics</p>
Spatial confounding in Bayesian species distribution modeling
<ol> <li>Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for species geographical distribution. However, unobserved environmental conditions and ecological processes may confound the model estimates if they have a direct impact on the species and, at the same time, they are correlated with the observed environmental covariates. This, so-called spatial confounding, is a general property of spatial models but it has not been studied in the context of SDMs before.</li> <li>Here we examine how the estimation accuracy of SDMs depends on the type of spatial confounding. We construct two simulation studies where we alter spatial structures of the observed and unobserved covariates and the level of dependence between them. We fit generalized linear models with and without spatial random effects applying Bayesian inference and record the bias induced to model estimates by spatial confounding. After this, we examine spatial confounding also with real vegetation data from northern Norway.</li> <li>Our results show that model estimates for coarse-scale covariates, such as climate covariates, are likely to be biased if a species distribution depends also on an unobserved covariate operating on a finer spatial scale. Pushing higher probability for a relatively weak and spatially smoothly varying spatial random effect compared to the observed covariates improved estimation accuracy. The improvement was independent of the actual spatial structure of the unobserved covariate.</li> <li>Our study addresses the major factors of spatial confounding in SDMs and provides a list of recommendations for pre-inference assessment of spatial confounding and for inference-based methods to decrease the chance of biased model estimates.</li> </ol>
Figure 2 in Confounding factors affecting faecal egg count reduction as a measure of anthelmintic efficacy
Figure 2. An illustrative example of the potential effect of seasonal shifts in nematode species composition on observed faecal egg count (FEC) reduction, based on typical epidemiological patterns in sheep in temperate areas. FEC composition indicates the proportion of eggs belonging to each species, where eggs of Trichostrongylus spp., Teladorsagia circumcincta and Haemonchus contortus are not easily distinguished from each other. Months are calendar months in the northern hemisphere, with Nematodirus battus and then Teladorsagia dominating in spring and early summer, Trichostrongylus in late summer and autumn, and Haemonchus transiently dominant following favourable climatic conditions [91]. In scenario 1, only Haemonchus is resistant to treatment, with FECR of 80%; in scenario 2, only Teladorsagia is resistant (80% FECR); FEC of other species reduce by 98% following treatment. A FECRT would have different results in different months, detecting resistance (<95% FECR) only in months (% FECR in bold) in which the resistant species contributes sufficiently to total faecal egg output, and returning false-negative results for AR in other months. The simulation does not account for differences in fecundity between species, which further amplify seasonal variation in FECR. Here, FECRT conducted at different times of year produce differing results even if anthelmintic efficacy is stable within species over that period.
Figure 1 in Confounding factors affecting faecal egg count reduction as a measure of anthelmintic efficacy
Figure 1. Schematic showing the range of confounders potentially influencing faecal egg count reduction (FECR) following anthelmintic treatment, and hence classification of anthelmintic resistance (AR). These are divided into host, parasite and technical factors, which together affect actual reduction in faecal egg count (1). Technical considerations also influence the accuracy with which FECR is observed (2), and hence the detection of anthelmintic resistance (3). Technical refinements to the FECRT have very much focused on improving the accuracy with which actual FECR is measured and translated into AR classification (step 3), even though many factors other than AR can strongly influence actual FECR. These risk confounding the FECRT and should be borne in mind when designing, conducting and interpreting the test, whether in standardised form for detection of AR, or in modified forms to monitor anthelmintic effectiveness.
Axial diffusion of respired CO2 confounds stem respiration estimates during the dormant season
<p>Efflux-based estimates of stem respiration in oak trees during the dormant season were biased by axial diffusion of locally respired CO<sub>2</sub>. Light-induced axial CO<sub>2 </sub>diffusion along the stem due to woody tissue photosynthesis may lead to equivocal estimates of stem respiratory coefficients during the dormant season, which are generally used to estimate maintenance respiration throughout the year.</p>
Datasets for paper "Frequent Ancient Hybridization Shape the Confounding Phylogeny of Pandanales"
<p><span>The phylogeny of Pandanales, an order of monocots comprising five distinct families (Cyclanthaceae, Pandanaceae, Stemonaceae, Triuridaceae, and Velloziaceae), has long been contentious, particularly concerning the placement of Triuridaceae. Previous phylogenetic studies have produced conflicting results, partly due to the limited scope of molecular data. In this study, we leveraged large-scale transcriptome sequencing from 17 Pandanales species and three outgroup samples, combined with whole-genome data for Acanthochlamys bracteata, to conduct a comprehensive phylogenetic analysis. Using both concatenation and coalescent-based methods, we generated a well-supported phylogeny for the group, revealing significant gene tree-species tree discordance. Our hybridization analyses revealed that ancient hybridization played a crucial role in shaping the evolutionary history of the Pandanaceae and Triuridaceae, providing a compelling explanation for the conflicting phylogenetic results in earlier studies. Additionally, whole-genome duplication (WGD) analyses identified five distinct WGD events across the order, suggesting these events played a critical role in the ecological diversification of the families. Our findings resolve key phylogenetic conflicts in Pandanales and underscore the importance of integrating hybridization detection and WGD analysis in understanding plant evolutionary history.</span></p>
Data set for figure 2-4 from publication "Missed Evaporation from Atmospherically Relevant Inorganic Mixtures Confounds Experimental Aerosol Studies",
<p>Data set for figure 2-4 from publication "Missed Evaporation from Atmospherically Relevant Inorganic Mixtures Confounds Experimental Aerosol Studies".</p>
Spatial confounding in Bayesian species distribution modeling
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To what extent is research on infrahumanization confounded by intergroup preference?
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Weight loss, insulin resistance, and study design confound results in a meta-analysis of animal models of fatty liver
The classical drug development pipeline necessitates studies using animal models of human disease to gauge future efficacy in humans, however there is a low conversion rate from success in animals to humans. Non-alcoholic fatty liver disease (NAFLD) is a complex chronic disease without any established therapies and a major field of animal research. We performed a meta-analysis with meta-regression of 603 interventional rodent studies (10,364 animals) in NAFLD to assess which variables influenced treatment response. Weight loss and alleviation of insulin resistance were consistently associated with improvement in NAFLD. Multiple drug classes that do not affect weight in humans caused weight loss in animals. Other study design variables, such as age of animals and dietary composition, influenced the magnitude of treatment effect. Publication bias may have increased effect estimates by 37-79%. These findings help to explain the challenge of reproducibility and translation within the field of metabolism.
Experimental Data for the paper Training AI to Recognize Realizable Gauss diagrams: the Same Instances Confound AI and Human Mathematicians
<p>This upload contains supplementary materials for the paper <br> Training AI to Recognize Realizable Gauss diagrams: the Same Instances Confound AI and Human Mathematicians, by Abdullah Khan, Alexei Lisitsa and Alexei Vernitski, to appear in Proceedings of ICAART 2022 (14th International Conference on Agents and Artificial Intelligence, 3-5 February, 2022), SCITEPRESS. </p>
Candidate-species delimitation in Desmognathus salamanders reveals gene flow across lineage boundaries, confounding phylogenetic estimation and clarifying hybrid zones
Dusky Salamanders (genus Desmognathus) currently comprise only 22 described, extant species. However, recent mitochondrial and nuclear estimates indicate the presence of up to 49 candidate species based on ecogeographic sampling. Previous studies also suggest a complex history of hybridization between these lineages. Studies in other groups suggest that disregarding admixture may affect both phylogenetic inference and clustering-based species-delimitation. With a dataset comprising 233 Anchored Hybrid Enrichment (AHE) loci sequenced for 896 Desmognathus specimens from all 49 candidate species, we test three hypotheses regarding i) species-level diversity, ii) hybridization and admixture, and iii) misleading phylogenetic inference. Using phylogenetic and population-clustering analyses considering gene flow, we find support for at least 47 candidate species in the phylogenomic dataset, some of which are newly characterized here while others represent combinations of previously named lineages that are collapsed in the current dataset. Within these, we observe significant phylogeographic structure, with up to 64 total geographic genetic lineages, many of which hybridize either narrowly at contact zones or extensively across ecological gradients. We find strong support for both recent admixture between terminal lineages and ancient hybridization across internal branches. This signal appears to distort concatenated phylogenetic inference, wherein more heavily admixed terminal specimens occupy apparently artifactual early diverging topological positions, occasionally to the extent of forming false clades of intermediate hybrids. Additional geographic and genetic sampling and more robust computational approaches will be needed to clarify taxonomy, and to reconstruct a network topology to display evolutionary relationships in a manner that is consistent with their complex history of reticulation. --
Result of LAVA-Knock: Genetic correlation via knockoffs removes confounding due to cross-trait assortative mating
<p>Results of application to GWAS summary statistics for 36 traits. 394,060 windows are used to perform LAVA-Bonf. 64,833 windows are used to conduct LAVA-Knock. At target FDR 0.1, LAVA-Knock detects 8,421 significant windows for 4,401 locus-phenotype pairs. LAVA-Bonf with threshold 0.05/394,060 identifies 10,802 windows for 6,260 locus-phenotype pairs.</p>
Data for "A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies"
<p>Data for the manuscript: A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies (see also https://doi.org/10.1101/2022.05.09.491139)</p>
Beyond Confounders: Addressing Source of Measurement Variability and Error in Shear Wave Elastography
ClinicalTrials.gov study NCT03342560. IPD Sharing: NO. Countries: 1. Publications: 30.
Data from: Ultra-slow oscillations in fMRI and resting-state connectivity: Neuronal and vascular contributions and technical confounds
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Widespread admixture blurs population structure and confounds Lake Trout (Salvelinus namaycush) conservation even in the genomic era
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Candidate-species delimitation in Desmognathus salamanders reveals gene flow across lineage boundaries, confounding phylogenetic estimation and clarifying hybrid zones
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.