Most of the plots here related to exploring aspects of a DLP run to help learn how well it may have worked or not!
A good resource of exploring aspects of DLP are these confluence pages:
Chip Layout Plots
The first thing we usually want to check, is how the chip is laid out:
# just some example data from a recent run with lots of experimental conditions
ex_mets <- vroom::vroom(
"data/ex_metrics.tsv.gz",
show_col_types = FALSE
)
dlptools::chip_plot(ex_mets, "experimental_condition")
Then often we want to look for spatial effects across the chip.
Such as, quality:
dlptools::chip_plot(ex_mets, "quality")
or perhaps total reads:
dlptools::chip_plot(ex_mets, "total_reads")
GC Correction Plots
Another plot that can be useful is to look at the GC correction for a cell, to see if the values really fall on integers for CNs, or if maybe something seems off about the multiplier HMMcopy chose for the cell.
# standard reads table, really anything with GC, modal_curve, multiplier, and
# cell_id columns should work.
ex_reads <- vroom::vroom(
"data/example_full_reads.tsv.gz",
show_col_types = FALSE
)
dlptools::gc_plot(
ex_reads,
cellid = "AT21352-A144173A-R03-C32",
# plot_choice = "both" # the default option
)
Or you can just plot one:
dlptools::gc_plot(
ex_reads,
cellid = "AT21352-A144173A-R03-C32",
plot_choice = "corrected" # or plot_choice = "raw"
)
Cell Copy Profiles
Sometimes you want to show a plot of a cell, or handful of cells, with their copy values and the overlaid state calls.
The Signals package has plotCNprofile which has lots of features.
But if you want a quick and dirt dlptools version, you can do:
dlptools::cell_cn_profile(
ex_reads,
cell_ids = unique(ex_reads$cell_id)[1:2],
# high copy numbers can obscure the plot, so you can set:
# pseduo_log_y = TRUE
# and that might help. Or see function help for more ideas
)
If needed, can also change what the y-axis is to other bin-based options:
dlptools::cell_cn_profile(
ex_reads,
cell_ids = unique(ex_reads$cell_id)[1:2],
yaxis = "state"
)