Showing 4 of total 4 results (show query)
bioc
scMET:Bayesian modelling of cell-to-cell DNA methylation heterogeneity
High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.
Maintained by Andreas C. Kapourani. Last updated 5 months ago.
immunooncologydnamethylationdifferentialmethylationdifferentialexpressiongeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionbayesiansequencingcoveragesinglecellbayesian-inferencegeneralised-linear-modelsheterogeneityhierarchical-modelsmethylation-analysissingle-cellcpp
22 stars 6.27 score 42 scripts
tbalan
frailtyEM:Fitting Frailty Models with the EM Algorithm
Contains functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation-Maximization algorithm. Supported data formats include clustered failures with left truncation and recurrent events in gap-time or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable and the Power Variance Family are supported.
Maintained by Theodor Adrian Balan. Last updated 6 years ago.
frailtyheterogeneitysurvivalcpp
3 stars 4.69 score 33 scripts
magosil86
getmstatistic:Quantifying Systematic Heterogeneity in Meta-Analysis
Quantifying systematic heterogeneity in meta-analysis using R. The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical statistical theory, documentation and examples.
Maintained by Lerato E Magosi. Last updated 4 years ago.
getmstatisticgwasheartgenes214heterogeneitymeta-analysismstatisticoutlier-studiesstatasystematic-heterogeneity
3 stars 4.41 score 17 scriptsmagosil86
bumblebee:Quantify Disease Transmission Within and Between Population Groups
A simple tool to quantify the amount of transmission of an infectious disease of interest occurring within and between population groups. 'bumblebee' uses counts of observed directed transmission pairs, identified phylogenetically from deep-sequence data or from epidemiological contacts, to quantify transmission flows within and between population groups accounting for sampling heterogeneity. Population groups might include: geographical areas (e.g. communities, regions), demographic groups (e.g. age, gender) or arms of a randomized clinical trial. See the 'bumblebee' website for statistical theory, documentation and examples <https://magosil86.github.io/bumblebee/>.
Maintained by Lerato E Magosi. Last updated 3 years ago.
epidemiologygeneticsheterogeneityphylogeneticstransmission
3.88 score 15 scripts