NEWS.md
NEW FEATURES
coverage_norm(), ensuring that
regions used to normalise coverage have the same seqlevels
as the inputted junctions.NEW FEATURES
gene_name/symbol using EnsDb
inputted into junction_annot().NEW FEATURES
plot_sashimi() and enable the
visualization of raw junction counts.NEW FEATURES
NEW FEATURES
outlier_detect() to using basilisk
for interfacing into python replacing reticulate.NEW FEATURES
dasper into a Bioconductor-friendly format
using biocthis.junction_load(), which loads raw junction data
from RNA-sequencing into an RangedSummarizedExperiment
object. Includes an option to allow download of user-specified control
junctions.junction_annot(), which uses information from
reference annotation and the strand of a junction to classify junctions
as “annotated”, “novel_acceptor”, “novel_donor”, “novel_exon_skip”,
“novel_combo”, “ambig_gene” and “unannotated”.junction_filter(), which filters junctions by
their count, width, annotation or if they overlap a set of user-defined
regions.junction_norm(), which normalises raw junction
counts (into a proportion-spliced-in) by dividing the counts of each
junction by the total number of counts in it’s associated cluster.junction_process(), a wrapper function for all
“junction_” prefixed functions except junction_load().junction_score(), which scores patient junctions
based on the extent their counts deviate from a control count
distribution.coverage_norm(), which will load and normalise
coverage for exonic/intronic regions corresponding to each
junction.coverage_score(), which scores coverage
associated with each junction based on it’s deviation from control
coverage distributions.coverage_process(), a wrapper function for all
“coverage_” prefixed functions.outlier_detect(), which uses the junction scores
and coverage scores as input into an unsupervised outlier detection
algorithm to find the most outlier-looking junctions in each
sample.outlier_aggregate(), which aggregates the
junction-level outlier data to a cluster-level.outlier_process(), a wrapper function for all
“outlier_” prefixed functions.plot_sashimi(), which enables the visualisation
of junction data across genes/transcripts or regions of interest.