create_tabix_file()
.get_cgi_*()
to get CpG islands annotation for mm10, GRCm39, hg19, and hg38.span
argument in favour of smoothing_window
which is defaulted to 2000 bases.smoothing_window
argument which represents the window size in bases from which data is used for smoothing around each point. This serves the same purpose as the dynamic calculation done previously, but is set more explicitly and should be more intuitive for users. The default is always 2000, and can be increased to increase smoothness and decreased to decrease smoothness.plot_gene()
plots, previously the isoform annotation would be restricted to only the gene of interest. It is now changed to follow the same behaviour as plot_region()
whereby all isoforms in the region are plotted.plot_region()
, plot_gene()
and plot_grange()
to plot heatmap by default.query_methy(simplify = FALSE)
, it will now return a list that is the same length as the number of regions queried, where it previously returned nothing if a particular sequence was missing from the tabix.gene_anno
argument to plot_region()
and plot_granges()
to control whether gene annotation is plotted.query_methy()
to always return same length output as the query when simplify = FALSE.plot_region_heatmap()
producing the wrong plot when a factor is used for the chromosome.samples()
setter for NanoMethResults.plot_agg_genes()
function as a shorthand for plot_agg_regions(x, exons_to_genes(exons(x)))
. methy_to_bsseq()
calls.filter_methy()
. If NanoMethResult is used as input, then NanoMethResult is invisibly returned as output.line_size
argument to plot_gene()
, plot_region()
and plot_granges()
plots for adjusting line size.subsample
argument to heatmap plots, default 50. This reduces the number of rows shown the plot to the specified amount.get_exons_mm10()
, get_exons_hg19()
, and get_exons_hg38()
as replacements for get_exons_mus_musculus()
and get_exons_homo_sapiens()
.heatmap
argument to plot_gene()
, plot_region()
and plot_granges()
. This adds a read-heatmap to the plot.cluster_regions()
function to perform k-means clustering on a table of genomic regions based on methylation profile.plot_gene()
, plot_region()
and plot_granges()
. This can be changed using the new avg_method
argument, default is mean
.filter_methy()
function to create a filtered methylation file.region_methy_stats()
to obtain average methylation fractions of specific regions.methy_to_edger()
direct conversion wrapper around methy_to_bsseq()
and bsseq_to_edger()
.palette
argument to plot_gene()
, plot_region()
and plot_granges()
to allow custom colour palettes.bsseq_to_edger()
failing when regions argument was used.plot_agg_regions()
.plot_agg_regions()
and plot_agg_regions_sample_grouped()
merged into one interface.plot_regions()
default window proportion to 0.theme_bw()
to theme_tufte()
.methy
, samples
and exons
.plot_gene()
, plot_region()
and plot_agg_regions()
.bsseq_to_edger()
to calculate aggregate counts over features rather than per site.exons_to_genes()
function to convert exon annotation to gene annotationplot_granges_heatmap()
function to use GRanges for plotting heatmapsplot_agg_regions()
plot_region_heatmap()
StatLM()
plot_region_heatmap()
as analogue to plot_region()
.plot_agg_regions_sample_grouped()
to use group
column of NanoMethViz::samples(x)
rather than haplotype
.plot_gene_heatmap()
.gene_anno()
in plot_gene()
for argument so FALSE actually turns off gene annotation.query_methy_gene()
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