Name Mode Size
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Damsel-package.Rd 100644 1 kb
annotatePeaksGenes.Rd 100644 2 kb
collateGenes.Rd 100644 2 kb
countBamInGATC.Rd 100644 2 kb
dros_counts.Rd 100644 2 kb
geom_dm.Rd 100644 1 kb
geom_gatc.Rd 100644 2 kb
geom_genes_tx.Rd 100644 2 kb
geom_peak.Rd 100644 2 kb
getGatcRegions.Rd 100644 1 kb
identifyPeaks.Rd 100644 2 kb
makeDGE.Rd 100644 3 kb
pipe.Rd 100644 0 kb
plotCorrHeatmap.Rd 100644 1 kb
plotCounts.Rd 100644 2 kb
plotCountsDistribution.Rd 100644 1 kb
plotCountsInPeaks.Rd 100644 1 kb
plotGeneOntology.Rd 100644 1 kb
plotWrap.Rd 100644 3 kb
random_counts.Rd 100644 0 kb
random_edgeR_results.Rd 100644 0 kb
random_regions.Rd 100644 0 kb
testDmRegions.Rd 100644 2 kb
testGeneOntology.Rd 100644 2 kb
README.md
# Damsel <!-- badges: start --> <!-- badges: end --> The goal of Damsel is to conduct an end-to-end analysis for DamID. It takes an input of BAM files and a GATC region file and generates counts, identifies methylated regions, identifies peaks, associating peaks with genes, conducts gene ontology testing, and provides a variety of adjustable ggplot2 style visualisations. ## Installation You can install the development version of Damsel like so: ``` r BiocManager::install("Oshlack/Damsel") ``` ## Example Here we generate example results from the differential methylation results, and plot layers of raw counts and the logFC. ``` r library(Damsel) set.seed(123) example_regions <- random_regions() example_counts <- random_counts() example_dm <- random_edgeR_results() head(example_dm) plotCounts(example_counts, "chr2L", 1, 5000) + geom_dm(example_dm) ```