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14 results for “Generalized linear models”
Minimal dataset for "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models"
<p>This repository contains a minimal data set to reproduce all results that don't compromise the privacy concerns for the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> <br> The repository contains the following data:</p> <ul> <li>adaptscore_acute.csv <ul> <li>A csv file that contains the estimated adaptation scores for the acute data set with HLA I model.</li> </ul> </li> <li>adaptscore_leftout.csv <ul> <li>A csv file that contains the estimated adaptation scores for the leftout data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training.csv <ul> <li>A csv file that contains the estimated adaptation scores for the traininig data set with the joint HLA I and HLA II model</li> </ul> </li> <li>adaptscore_training_hla1_without_clin.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the HLA I model (via cross-validation)</li> </ul> </li> <li>adaptscore_training_seed2.csv <ul> <li>A csv file that contains the estimated adaptation scores for the training data set with the joint HLA I and HLA II model via cross-validation with another seed</li> </ul> </li> </ul>
Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil
<p>Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics).</p> <p>The excel file includes Aphanomyces root rot disease visual scores (<em>Root_Rating</em>), unique identifier for each lentil accession/line (<em>Lentil_ID</em>), unique identifier for each experiment (<em>Experiment</em>), and unique identifier for each image (<em>Lab_ID</em>).</p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
<p>Diet analysis integrates a wide variety of visual, chemical and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, e.g., highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package <i>mvtweedie</i> that extends capabilities of widely used packages in R (<i>mgcv</i> and <i>ggplot2</i>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted puffins (<i>Fratercula cirrhata</i>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (<i>Canis lupus ligoni</i>) in Southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. </p>
Consensus nucleotide sequences for env and gag for paper: Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models
<p>This is the consensus sequence repository to the manuscript "Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models".<br> It contains the 10% consensus nucleotide sequences of the env and gag (only p24) protein of HIV-1 used for the training and leftout data set. The NGS sequences are available under BioProject ID PRJNA810303 and the corresponding BioSample Accession IDs are SAMN26241863:26242168 and SAMN28728524:SAMN28728529</p> <ul> <li>env_leftout.fasta <ul> <li>A fasta file that contains the consensus nucleotide sequences for the env protein for the leftout data set</li> </ul> </li> <li>env_nt_274.fasta <ul> <li>A fasta file that contains the consensus nucleotide sequences for the env protein for the training data set</li> </ul> </li> <li>gag_leftout.fasta <ul> <li>A fasta file that contains the consensus nucleotide sequences for the gag protein for the leftout data set</li> </ul> </li> <li>gag_nt_274.fasta <ul> <li>A fasta file that contains the consensus nucleotide sequences for the gag protein for the training data set</li> </ul> </li> </ul>
Figure 5 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 5. Relative abundances of each type of cnidocyst for each species. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza.
Figure 1 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 1. Cnidome of Hydra viridissima. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza. Scale bar: 3 μm.
Figure 4 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 4. Different morphotypes of holotrichous isorhiza. (A) Hydra viridissima, (B) Hydra vulgaris pedunculata, C and (D) Hydra vulgaris. Scale bar: 2.45 μm.
Figure 8 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 8. GLM adjustment graphs used for comparison between species. (A) scatter plot, (B) Q-Q Plots.
Figure 3 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 3. Cnidome of Hydra vulgaris pedunculata. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza. Scale bar: 2.7 μm.
Figure 2 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 2. Cnidome of Hydra vulgaris. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza. Scale bar: 2.85 μm.
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
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Data from: miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs
<p>These datasets can be used to reproduce all analyses from the publication "miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs" in conjunction with codes found at https://github.com/mccall-group/miRglmm_paper. </p> <p>"Monocyte_data_subset.rda", "monocyte_exact_subset_filtered2.rda" and "sims_N100_m2_s1_rtruncnorm.rda" can be used to reproduce the simulation analysis. </p> <p>"panel_B_SE.rda" and "ERCC_filtered.rda" can be used to reproduce the ERCC synthetic data analysis with known ground truth.</p> <p>"study89_data_subset.rda" and "study89_data_subset_filtered2.rda" can be used to reproduce the immune cell-type analysis. </p> <p>"bladder_testes_data_subset.rda" and "bladder_testes_data_subset_filtered2.rda" can be used to reproduce the bladder vs testes tissue analysis.</p>
Dataset to develop the Generalized Linear Models in "THE CRITICAL ROLE OF HYDROLOGICAL DISTANCE IN SHAPING NUTRIENT DYNAMICS ALONG THE WATERSHED-LAKE CONTINUUM"
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Data from: Generalized linear mixed models for mapping multiple quantitative trait loci
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