Nothing
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
knitr::opts_chunk$set(fig.width=7.2, fig.height=6)
## ----setup,warning=FALSE, message=FALSE---------------------------------------
library(trace)
library(dplyr)
library(ggplot2)
## ----fsa_import---------------------------------------------------------------
fsa_list <- lapply(cell_line_fsa_list, function(x) x$clone())
## ----ladders,warning=FALSE, message=FALSE-------------------------------------
find_ladders(
fsa_list,
show_progress_bar = FALSE
)
## ----find_fragments, warning=FALSE--------------------------------------------
fragments_list <- find_fragments(
fsa_list,
min_bp_size = 300
)
## ----add_metadata-------------------------------------------------------------
add_metadata(
fragments_list = fragments_list,
metadata_data.frame = metadata,
unique_id = "unique_id",
metrics_group_id = "metrics_group_id",
metrics_baseline_control = "metrics_baseline_control",
batch_run_id = "batch_run_id",
batch_sample_id = "batch_sample_id"
)
## ----find_alleles_and_repeats, warning=FALSE, message=FALSE-------------------
find_alleles(
fragments_list = fragments_list
)
call_repeats(
fragments_list = fragments_list
)
## -----------------------------------------------------------------------------
plot_traces(fragments_list[1], xlim = c(100, 150), show_peaks = FALSE)
## -----------------------------------------------------------------------------
extract_trace_table(fragments_list[1]) |>
filter(between(calculated_repeats, 100, 150)) |>
ggplot(aes(calculated_repeats, signal)) +
geom_line()
## -----------------------------------------------------------------------------
traces_df <- extract_trace_table(fragments_list)
## -----------------------------------------------------------------------------
samples_for_plotting <- metadata[which(metadata$metrics_group_id == "CC6"), "unique_id"]
traces_for_plotting_df <- extract_trace_table(fragments_list[samples_for_plotting])
## -----------------------------------------------------------------------------
traces_for_plotting_with_metadata_df <- dplyr::left_join(
traces_for_plotting_df,
metadata,
by = join_by(unique_id)
)
## -----------------------------------------------------------------------------
traces_for_plotting_with_metadata_df |>
dplyr::filter(between(calculated_repeats, 100, 150)) |>
ggplot(aes(x = calculated_repeats,
y = signal,
colour = as.factor(treatment))) +
geom_line(aes(group = unique_id)) +
facet_wrap(vars(paste("Day", day, treatment, "nM Branaplam")), ncol = 1)
## -----------------------------------------------------------------------------
alleles_df <- extract_alleles(fragments_list[samples_for_plotting])
sample_to_exclude <- alleles_df[which(is.na(alleles_df$allele_repeat)), "unique_id"]
traces_for_plotting_with_metadata_df <- traces_for_plotting_with_metadata_df |>
dplyr::filter(between(calculated_repeats, 100, 150),
!unique_id %in% sample_to_exclude) |>
group_by(unique_id) |>
mutate(relative_signal = signal / max(signal),
day_treatment = paste("Day", day, treatment, "nM Branaplam")) |>
ungroup()
traces_for_plotting_with_metadata_df |>
ggplot(aes(x = calculated_repeats,
relative_signal,
colour = as.factor(treatment))) +
geom_line(aes(group = unique_id)) +
facet_wrap(vars(day_treatment), ncol = 1)
## -----------------------------------------------------------------------------
d0_trace <- traces_for_plotting_with_metadata_df |>
filter(day == 0) |>
filter(unique_id == unique(unique_id)[1]) |>
select(-day_treatment)
traces_for_plotting_with_metadata_df |>
group_by(day, treatment) |>
filter(unique_id == unique(unique_id)[1]) |>
ggplot(aes(x = calculated_repeats,
y = relative_signal,
colour = paste("Day", day, treatment, "nM Branaplam"))) +
geom_line(data = d0_trace ,
aes(group = unique_id),
colour = "gray40") +
geom_line(aes(group = unique_id)) +
facet_wrap(vars(day_treatment), ncol = 1) +
labs(colour = "") +
theme_minimal()
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