## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
## ---- include=FALSE-----------------------------------------------------------
library(rats)
## -----------------------------------------------------------------------------
# Simulate some data.
simdat <- sim_boot_data(clean=TRUE)
# For convenience let's assign the contents of the list to separate variables
mycond_A <- simdat[[2]] # Simulated bootstrapped data for one condition.
mycond_B <- simdat[[3]] # Simulated bootstrapped data for other condition.
myannot <- simdat[[1]] # Transcript and gene IDs for the above data.
# Call DTU
mydtu <- call_DTU(annot= myannot, verbose= FALSE,
boot_data_A= mycond_A, boot_data_B= mycond_B,
dprop_thresh=0.1, qboot=TRUE, rboot=FALSE)
## -----------------------------------------------------------------------------
# Grouping by condition (DEFAULT):
plot_gene(mydtu, "MIX", style="bycondition")
## -----------------------------------------------------------------------------
# Grouping by isoform:
plot_gene(mydtu, "MIX", style="byisoform")
## ----eval=FALSE---------------------------------------------------------------
# models <- annot2models('/my/annotation/file.gtf')
# library(ggbio)
# # This will plot the structure of all isoforms for the given gene ID.
# autoplot(models[['mygeneID']])
## ---- eval=FALSE--------------------------------------------------------------
# # Proportion change VS transcript-level significance. Each point is a transcript
# plot_overview(mydtu, type="tvolcano")
#
# # This can also be plotted for genes, by using the largest isoform effect size as proxy.
# plot_overview(mydtu, type="gvolcano")
## ---- eval=FALSE--------------------------------------------------------------
# # Distribution of proportion change.
# plot_overview(mydtu, type="dprop")
#
# # Distribution of largest isoform proportion change per gene.
# plot_overview(mydtu, type="maxdprop")
## ---- eval=FALSE--------------------------------------------------------------
# # Proportion change VS transcript-level significance. Each point is a transcript
# plot_overview(mydtu, type="fcvolcano")
#
# # This can also be plotted for genes, by using the largest isoform effect size as proxy.
# plot_overview(mydtu, type="fcVSdprop")
## -----------------------------------------------------------------------------
# Pairwise Pearson's correlations among samples.
plot_diagnostics(mydtu, type='cormat') # Default type.
## ---- eval=FALSE--------------------------------------------------------------
# # Start the interactive volcano plot.
# plot_shiny_volcano(mydtu)
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