R/1_flux-figures.R
In oldhamlab/Copeland.2021.hypoxia.flux: What the Package Does (One Line, Title Case)
Defines functions
clean_dna_count_hypoxia
plot_cells_per_dna
clean_viability
plot_low_fluxes
plot_high_fluxes
plot_growth_rates
annot_fluxes_main
plot_growth_curve
annot_pairwise
# flux-figures.R
annot_pairwise <- function(fluxes) {
fluxes %>%
dplyr::filter(experiment %in% c("02", "05", "bay")) %>%
dplyr::group_by(experiment, cell_type, metabolite, abbreviation) %>%
tidyr::nest() %>%
dplyr::mutate(
ttest = purrr::map_dbl(data, ~t.test(
flux ~ interaction(oxygen, treatment),
data = .x,
paired = TRUE
)$p.value),
pval = annot_p(ttest),
y_pos = Inf,
vjust = 1.5
)
}
plot_growth_curve <- function(
df,
cell = c("lf", "df", "pasmc"),
exper = c("02", "05", "bay")
) {
df %>%
dplyr::filter(
cell_type %in% cell &
experiment %in% exper &
metabolite == "cells" &
time < 96
) %>%
dplyr::group_by(
date,
group,
time
) %>%
dplyr::summarize(count = mean(conc, na.rm = TRUE)) %>%
plot_time_lines(
y = count / 1000,
ylab = expression(paste("Cell count (x", 10^3, ")")),
clr = "group"
) +
ggplot2::coord_cartesian(
ylim = c(0, NA),
clip = "off"
)
}
annot_fluxes_main <- function(x, formula) {
x <-
lmerTest::lmer(
formula,
data = x
) %>%
emmeans::emmeans(~ group) %>%
pairs() %>%
broom::tidy() %>%
dplyr::select(contrast, tidyselect::contains("p.value")) %>%
dplyr::rename_with(~ "pval", .cols = tidyselect::contains("p.value")) %>%
dplyr::mutate(
group = dplyr::case_when(
contrast == "21% - 0.5%" ~ "0.5%",
contrast == "21% - 0.2%" ~ "0.2%",
contrast == "DMSO - BAY" ~ "BAY",
contrast == "0.5% - BAY" ~ "BAY"
),
group = factor(group, levels = c("21%", "0.5%", "0.2%", "DMSO", "BAY")),
label = dplyr::case_when(
pval < 0.05 & contrast == "21% - 0.5%" ~ "*",
pval < 0.05 & contrast == "21% - 0.2%" ~ "*",
pval < 0.05 & contrast == "DMSO - BAY" ~ "†",
pval < 0.05 & contrast == "0.5% - BAY" ~ "‡"
),
vjust = dplyr::case_when(
label == "*" ~ 1.5,
label == "†" ~ 1.5,
label == "‡" ~ 3
),
y = Inf
) %>%
dplyr::filter(!is.na(label))
y <-
setdiff(c("21%", "0.5%", "0.2%", "DMSO", "BAY"), x$group) %>%
factor(levels = c("21%", "0.5%", "0.2%", "DMSO", "BAY")) %>%
list() %>%
rlang::set_names("group")
dplyr::bind_rows(x, y)
}
plot_growth_rates <- function(
df,
cell = c("lf", "pasmc"),
exper = c("02", "05", "bay"),
annot = NULL
) {
x <-
df %>%
dplyr::filter(cell_type %in% cell & experiment %in% exper)
annot <-
annot_fluxes_main(x, mu ~ group + (1 | date)) %>%
dplyr::filter(group %in% x$group)
ggplot2::ggplot(x) +
ggplot2::aes(
x = group,
y = mu
) +
ggplot2::stat_summary(
ggplot2::aes(fill = group),
geom = "col",
fun = "mean",
width = 0.6,
show.legend = FALSE
) +
ggplot2::stat_summary(
geom = "errorbar",
fun.data = "mean_se",
width = 0.2,
size = 0.25,
show.legend = FALSE
) +
ggplot2::geom_text(
data = annot,
ggplot2::aes(
label = label,
y = y,
vjust = vjust
),
family = "Calibri",
size = 6/ggplot2::.pt
) +
ggplot2::labs(
x = "Treatment",
y = "Growth rate (/h)"
) +
ggplot2::scale_fill_manual(values = clrs, limits = force) +
theme_plots() +
ggplot2::coord_cartesian(
ylim = c(0, 0.03),
clip = "off"
) +
NULL
}
plot_high_fluxes <- function(
df,
cell = c("lf", "pasmc"),
exper = c("02", "05", "bay")
) {
x <-
df %>%
dplyr::filter(
cell_type %in% cell &
experiment %in% exper &
metabolite %in% c("lactate", "glucose")
)
annot <-
x %>%
dplyr::group_by(abbreviation) %>%
tidyr::nest() %>%
dplyr::mutate(annot = purrr::map(data, annot_fluxes_main, formula = flux ~ group + (1 | date))) %>%
tidyr::unnest(c(annot)) %>%
dplyr::filter(group %in% x$group)
ggplot2::ggplot(x) +
ggplot2::aes(
x = reorder(toupper(abbreviation), flux),
y = flux
) +
ggplot2::stat_summary(
ggplot2::aes(fill = group),
geom = "col",
fun = "mean",
position = ggplot2::position_dodge(),
width = 0.6,
show.legend = TRUE
) +
ggplot2::geom_hline(
yintercept = 0,
color = "black",
lwd = 0.25
) +
ggplot2::stat_summary(
ggplot2::aes(group = group),
geom = "errorbar",
fun.data = "mean_se",
position = ggplot2::position_dodge(width = 0.6),
width = 0.2,
size = 0.25,
show.legend = FALSE
) +
ggplot2::geom_text(
data = annot,
ggplot2::aes(
x = abbreviation,
y = y,
vjust = vjust,
label = label,
group = group
),
position = ggplot2::position_dodge(width = 0.6),
hjust = 0.5,
family = "Calibri",
size = 6/ggplot2::.pt
) +
ggplot2::labs(
x = "Metabolite",
y = "Flux (fmol/cell/h)",
fill = NULL
) +
ggplot2::scale_fill_manual(values = clrs, limits = force) +
ggplot2::scale_y_continuous(
expand = ggplot2::expansion(mult = 0.2),
breaks = scales::extended_breaks(n = 7)
) +
theme_plots() +
ggplot2::theme(
legend.key.width = ggplot2::unit(0.5, "lines"),
legend.key.height = ggplot2::unit(0.5, "lines"),
legend.position = "bottom",
legend.box.margin = ggplot2::margin(t = -10)
)
}
plot_low_fluxes <- function(
df,
cell = c("lf", "pasmc"),
exper = c("02", "05", "bay")
) {
x <-
df %>%
dplyr::filter(
cell_type %in% cell &
experiment %in% exper &
metabolite %nin% c("lactate", "glucose")
)
annot <-
x %>%
dplyr::group_by(abbreviation) %>%
tidyr::nest() %>%
dplyr::mutate(annot = purrr::map(data, annot_fluxes_main, formula = flux ~ group + (1 | date))) %>%
tidyr::unnest(c(annot)) %>%
dplyr::filter(group %in% x$group)
ggplot2::ggplot(x) +
ggplot2::aes(
x = reorder(toupper(abbreviation), flux),
y = flux
) +
ggplot2::stat_summary(
ggplot2::aes(fill = group),
geom = "col",
fun = "mean",
position = ggplot2::position_dodge(width = 0.6),
width = 0.6,
show.legend = TRUE
) +
ggplot2::geom_hline(
yintercept = 0,
color = "black",
lwd = 0.25
) +
ggplot2::stat_summary(
ggplot2::aes(group = group),
geom = "errorbar",
fun.data = "mean_se",
position = ggplot2::position_dodge(width = 0.6),
width = 0.2,
size = 0.25,
show.legend = FALSE
) +
ggplot2::geom_text(
data = annot,
ggplot2::aes(
x = abbreviation,
y = y,
vjust = vjust,
label = label,
group = group
),
position = ggplot2::position_dodge(width = 0.6),
family = "Calibri",
size = 6/ggplot2::.pt
) +
ggplot2::labs(
x = "Metabolite",
y = "Flux (fmol/cell/h)",
fill = NULL
) +
ggplot2::scale_fill_manual(values = clrs, limits = force) +
ggplot2::scale_y_continuous(
trans = ggallin::pseudolog10_trans,
breaks = c(-100, -10, 0, 10, 100),
limits = c(-250, 250)
) +
theme_plots() +
ggplot2::theme(
panel.grid.major.x = ggplot2::element_line(color = "gray90"),
legend.key.width = ggplot2::unit(0.5, "lines"),
legend.key.height = ggplot2::unit(0.5, "lines"),
legend.position = "bottom",
legend.box.margin = ggplot2::margin(t = -10)
)
}
clean_viability <- function(viability_file) {
readr::read_csv(viability_file) %>%
dplyr::mutate(
viability = 100 * live / (dead + live),
oxygen = factor(oxygen, levels = c("21%", "0.5%"))
) %>%
dplyr::group_by(time, oxygen) %>%
wmo::remove_nested_outliers(viability, remove = TRUE)
}
plot_cells_per_dna <- function(dna_per_cell_clean) {
dna_per_cell_clean %>%
dplyr::filter(.data$volume == 200 & cells < 400000) %>%
ggplot2::ggplot() +
ggplot2::facet_wrap(~cell_type, labeller = ggplot2::as_labeller(toupper)) +
ggplot2::aes(
x = cells/1000,
y = conc
) +
ggplot2::geom_smooth(
formula = y ~ 0 + x,
method = "lm",
color = clrs[[2]],
size = 0.5,
se = FALSE
) +
ggplot2::stat_summary(
geom = "linerange",
fun.data = "mean_se",
size = 0.5,
show.legend = FALSE
) +
ggplot2::stat_summary(
geom = "point",
fun = "mean",
pch = 21,
color = "white",
fill = "black",
size = 2,
show.legend = FALSE
) +
ggplot2::labs(
x = expression(paste("Cell count (×", 10^3, ")")),
y = "DNA (ng)"
) +
theme_plots() +
ggplot2::coord_cartesian(xlim = c(0, NA), clip = "off") +
NULL
}
clean_dna_count_hypoxia <- function(dna_count_hypoxia_file) {
df <-
readr::read_csv(dna_count_hypoxia_file) %>%
dplyr::mutate(oxygen = factor(oxygen, levels = c("21%", "0.5%"))) %>%
clean_technical_replicates()
std <-
df %>%
dplyr::filter(!is.na(conc)) %>%
lm(value ~ conc, data = .)
df %>%
dplyr::filter(is.na(conc)) %>%
dplyr::mutate(conc = interpolate(., std)) %>%
dplyr::select(-value)
}
plot_dna_count_hypoxia <- function(dna_count_hypoxia) {
ggplot2::ggplot(dna_count_hypoxia) +
ggplot2::aes(
x = count / 1000,
y = conc,
color = oxygen,
fill = oxygen
) +
ggplot2::geom_smooth(
method = "lm",
size = 0.5,
formula = y ~ x,
se = FALSE,
show.legend = FALSE
) +
ggplot2::geom_point(
pch = 21,
color = "white",
# alpha = 0.3,
size = 2,
show.legend = FALSE) +
ggplot2::labs(
x = expression(paste("Cell count (×", 10^3, ")")),
y = "DNA (ng)"
) +
ggplot2::scale_x_continuous(breaks = seq(0, 300, 100)) +
ggplot2::scale_color_manual(values = clrs) +
ggplot2::scale_fill_manual(values = clrs) +
theme_plots() +
ggplot2::coord_cartesian(
ylim = c(0, NA),
xlim = c(0, 300),
clip = "off"
)
}
plot_k <- function(degradation_rates, k) {
annot <-
k %>%
dplyr::select(-k) %>%
dplyr::mutate(
label = "*",
ypos = Inf,
vjust = 1.5
)
degradation_rates %>%
dplyr::mutate(
group = dplyr::case_when(
oxygen == "21%" & treatment == "None" ~ "21%",
oxygen == "0.5%" ~ "0.5%",
treatment == "DMSO" ~ "DMSO"
),
group = factor(group, levels = c("21%", "0.5%", "DMSO"))
) %>%
dplyr::left_join(annot, by = c("metabolite", "oxygen", "treatment")) %>%
ggplot2::ggplot() +
ggplot2::aes(
x = reorder(toupper(abbreviation), k),
y = k
) +
ggplot2::stat_summary(
ggplot2::aes(fill = group),
geom = "col",
fun = "mean",
position = ggplot2::position_dodge(width = 0.6),
width = 0.6,
show.legend = TRUE
) +
ggplot2::geom_hline(
yintercept = 0,
color = "black",
lwd = 0.25
) +
ggplot2::stat_summary(
ggplot2::aes(group = group),
geom = "errorbar",
fun.data = "mean_se",
position = ggplot2::position_dodge(width = 0.6),
width = 0.2,
size = 0.25,
show.legend = FALSE
) +
ggplot2::geom_text(
ggplot2::aes(
color = group,
label = label,
y = ypos,
vjust = vjust
),
family = "Calibri",
size = 6/ggplot2::.pt,
position = ggplot2::position_dodge(width = 0.6),
show.legend = FALSE
) +
ggplot2::labs(
x = "Metabolite",
y = "Rate constant (/h)",
fill = NULL,
color = NULL
) +
ggplot2::scale_color_manual(
values = clrs,
limits = force
) +
ggplot2::scale_fill_manual(
values = clrs,
limits = force
) +
ggplot2::scale_y_continuous(expand = ggplot2::expansion(mult = 0.2)) +
theme_plots() +
ggplot2::theme(
panel.grid.major.x = ggplot2::element_line(color = "gray90"),
legend.key.width = ggplot2::unit(0.5, "lines"),
legend.key.height = ggplot2::unit(0.5, "lines"),
legend.position = "bottom",
legend.box.margin = ggplot2::margin(t = -10)
)
}
plot_evap_data <- function(evap_clean) {
evap_clean %>%
dplyr::filter(experiment == "05" & cell_type == "lf") %>%
dplyr::mutate(
oxygen = factor(oxygen, levels = c("21%", "0.5%"))
) %>%
ggplot2::ggplot() +
ggplot2::aes(
x = time,
y = volume,
color = oxygen,
fill = oxygen
) +
ggplot2::geom_smooth(
method = "lm",
formula = y ~ x,
se = FALSE,
size = 0.5,
show.legend = FALSE
) +
ggplot2::stat_summary(
geom = "linerange",
fun.data = "mean_se",
size = 0.5,
show.legend = FALSE
) +
ggplot2::stat_summary(
geom = "point",
fun = "mean",
pch = 21,
color = "white",
size = 2,
show.legend = FALSE
) +
ggplot2::labs(
x = "Time (h)",
y = "Volume (mL)"
) +
ggplot2::scale_x_continuous(breaks = seq(0, 72, 24)) +
ggplot2::scale_color_manual(values = clrs) +
ggplot2::scale_fill_manual(values = clrs) +
theme_plots()
}
oldhamlab/Copeland.2021.hypoxia.flux documentation built on Feb. 5, 2022, 8:31 p.m.
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Defines functions clean_dna_count_hypoxia plot_cells_per_dna clean_viability plot_low_fluxes plot_high_fluxes plot_growth_rates annot_fluxes_main plot_growth_curve annot_pairwise
# flux-figures.R
annot_pairwise <- function(fluxes) {
fluxes %>%
dplyr::filter(experiment %in% c("02", "05", "bay")) %>%
dplyr::group_by(experiment, cell_type, metabolite, abbreviation) %>%
tidyr::nest() %>%
dplyr::mutate(
ttest = purrr::map_dbl(data, ~t.test(
flux ~ interaction(oxygen, treatment),
data = .x,
paired = TRUE
)$p.value),
pval = annot_p(ttest),
y_pos = Inf,
vjust = 1.5
)
}
plot_growth_curve <- function(
df,
cell = c("lf", "df", "pasmc"),
exper = c("02", "05", "bay")
) {
df %>%
dplyr::filter(
cell_type %in% cell &
experiment %in% exper &
metabolite == "cells" &
time < 96
) %>%
dplyr::group_by(
date,
group,
time
) %>%
dplyr::summarize(count = mean(conc, na.rm = TRUE)) %>%
plot_time_lines(
y = count / 1000,
ylab = expression(paste("Cell count (x", 10^3, ")")),
clr = "group"
) +
ggplot2::coord_cartesian(
ylim = c(0, NA),
clip = "off"
)
}
annot_fluxes_main <- function(x, formula) {
x <-
lmerTest::lmer(
formula,
data = x
) %>%
emmeans::emmeans(~ group) %>%
pairs() %>%
broom::tidy() %>%
dplyr::select(contrast, tidyselect::contains("p.value")) %>%
dplyr::rename_with(~ "pval", .cols = tidyselect::contains("p.value")) %>%
dplyr::mutate(
group = dplyr::case_when(
contrast == "21% - 0.5%" ~ "0.5%",
contrast == "21% - 0.2%" ~ "0.2%",
contrast == "DMSO - BAY" ~ "BAY",
contrast == "0.5% - BAY" ~ "BAY"
),
group = factor(group, levels = c("21%", "0.5%", "0.2%", "DMSO", "BAY")),
label = dplyr::case_when(
pval < 0.05 & contrast == "21% - 0.5%" ~ "*",
pval < 0.05 & contrast == "21% - 0.2%" ~ "*",
pval < 0.05 & contrast == "DMSO - BAY" ~ "†",
pval < 0.05 & contrast == "0.5% - BAY" ~ "‡"
),
vjust = dplyr::case_when(
label == "*" ~ 1.5,
label == "†" ~ 1.5,
label == "‡" ~ 3
),
y = Inf
) %>%
dplyr::filter(!is.na(label))
y <-
setdiff(c("21%", "0.5%", "0.2%", "DMSO", "BAY"), x$group) %>%
factor(levels = c("21%", "0.5%", "0.2%", "DMSO", "BAY")) %>%
list() %>%
rlang::set_names("group")
dplyr::bind_rows(x, y)
}
plot_growth_rates <- function(
df,
cell = c("lf", "pasmc"),
exper = c("02", "05", "bay"),
annot = NULL
) {
x <-
df %>%
dplyr::filter(cell_type %in% cell & experiment %in% exper)
annot <-
annot_fluxes_main(x, mu ~ group + (1 | date)) %>%
dplyr::filter(group %in% x$group)
ggplot2::ggplot(x) +
ggplot2::aes(
x = group,
y = mu
) +
ggplot2::stat_summary(
ggplot2::aes(fill = group),
geom = "col",
fun = "mean",
width = 0.6,
show.legend = FALSE
) +
ggplot2::stat_summary(
geom = "errorbar",
fun.data = "mean_se",
width = 0.2,
size = 0.25,
show.legend = FALSE
) +
ggplot2::geom_text(
data = annot,
ggplot2::aes(
label = label,
y = y,
vjust = vjust
),
family = "Calibri",
size = 6/ggplot2::.pt
) +
ggplot2::labs(
x = "Treatment",
y = "Growth rate (/h)"
) +
ggplot2::scale_fill_manual(values = clrs, limits = force) +
theme_plots() +
ggplot2::coord_cartesian(
ylim = c(0, 0.03),
clip = "off"
) +
NULL
}
plot_high_fluxes <- function(
df,
cell = c("lf", "pasmc"),
exper = c("02", "05", "bay")
) {
x <-
df %>%
dplyr::filter(
cell_type %in% cell &
experiment %in% exper &
metabolite %in% c("lactate", "glucose")
)
annot <-
x %>%
dplyr::group_by(abbreviation) %>%
tidyr::nest() %>%
dplyr::mutate(annot = purrr::map(data, annot_fluxes_main, formula = flux ~ group + (1 | date))) %>%
tidyr::unnest(c(annot)) %>%
dplyr::filter(group %in% x$group)
ggplot2::ggplot(x) +
ggplot2::aes(
x = reorder(toupper(abbreviation), flux),
y = flux
) +
ggplot2::stat_summary(
ggplot2::aes(fill = group),
geom = "col",
fun = "mean",
position = ggplot2::position_dodge(),
width = 0.6,
show.legend = TRUE
) +
ggplot2::geom_hline(
yintercept = 0,
color = "black",
lwd = 0.25
) +
ggplot2::stat_summary(
ggplot2::aes(group = group),
geom = "errorbar",
fun.data = "mean_se",
position = ggplot2::position_dodge(width = 0.6),
width = 0.2,
size = 0.25,
show.legend = FALSE
) +
ggplot2::geom_text(
data = annot,
ggplot2::aes(
x = abbreviation,
y = y,
vjust = vjust,
label = label,
group = group
),
position = ggplot2::position_dodge(width = 0.6),
hjust = 0.5,
family = "Calibri",
size = 6/ggplot2::.pt
) +
ggplot2::labs(
x = "Metabolite",
y = "Flux (fmol/cell/h)",
fill = NULL
) +
ggplot2::scale_fill_manual(values = clrs, limits = force) +
ggplot2::scale_y_continuous(
expand = ggplot2::expansion(mult = 0.2),
breaks = scales::extended_breaks(n = 7)
) +
theme_plots() +
ggplot2::theme(
legend.key.width = ggplot2::unit(0.5, "lines"),
legend.key.height = ggplot2::unit(0.5, "lines"),
legend.position = "bottom",
legend.box.margin = ggplot2::margin(t = -10)
)
}
plot_low_fluxes <- function(
df,
cell = c("lf", "pasmc"),
exper = c("02", "05", "bay")
) {
x <-
df %>%
dplyr::filter(
cell_type %in% cell &
experiment %in% exper &
metabolite %nin% c("lactate", "glucose")
)
annot <-
x %>%
dplyr::group_by(abbreviation) %>%
tidyr::nest() %>%
dplyr::mutate(annot = purrr::map(data, annot_fluxes_main, formula = flux ~ group + (1 | date))) %>%
tidyr::unnest(c(annot)) %>%
dplyr::filter(group %in% x$group)
ggplot2::ggplot(x) +
ggplot2::aes(
x = reorder(toupper(abbreviation), flux),
y = flux
) +
ggplot2::stat_summary(
ggplot2::aes(fill = group),
geom = "col",
fun = "mean",
position = ggplot2::position_dodge(width = 0.6),
width = 0.6,
show.legend = TRUE
) +
ggplot2::geom_hline(
yintercept = 0,
color = "black",
lwd = 0.25
) +
ggplot2::stat_summary(
ggplot2::aes(group = group),
geom = "errorbar",
fun.data = "mean_se",
position = ggplot2::position_dodge(width = 0.6),
width = 0.2,
size = 0.25,
show.legend = FALSE
) +
ggplot2::geom_text(
data = annot,
ggplot2::aes(
x = abbreviation,
y = y,
vjust = vjust,
label = label,
group = group
),
position = ggplot2::position_dodge(width = 0.6),
family = "Calibri",
size = 6/ggplot2::.pt
) +
ggplot2::labs(
x = "Metabolite",
y = "Flux (fmol/cell/h)",
fill = NULL
) +
ggplot2::scale_fill_manual(values = clrs, limits = force) +
ggplot2::scale_y_continuous(
trans = ggallin::pseudolog10_trans,
breaks = c(-100, -10, 0, 10, 100),
limits = c(-250, 250)
) +
theme_plots() +
ggplot2::theme(
panel.grid.major.x = ggplot2::element_line(color = "gray90"),
legend.key.width = ggplot2::unit(0.5, "lines"),
legend.key.height = ggplot2::unit(0.5, "lines"),
legend.position = "bottom",
legend.box.margin = ggplot2::margin(t = -10)
)
}
clean_viability <- function(viability_file) {
readr::read_csv(viability_file) %>%
dplyr::mutate(
viability = 100 * live / (dead + live),
oxygen = factor(oxygen, levels = c("21%", "0.5%"))
) %>%
dplyr::group_by(time, oxygen) %>%
wmo::remove_nested_outliers(viability, remove = TRUE)
}
plot_cells_per_dna <- function(dna_per_cell_clean) {
dna_per_cell_clean %>%
dplyr::filter(.data$volume == 200 & cells < 400000) %>%
ggplot2::ggplot() +
ggplot2::facet_wrap(~cell_type, labeller = ggplot2::as_labeller(toupper)) +
ggplot2::aes(
x = cells/1000,
y = conc
) +
ggplot2::geom_smooth(
formula = y ~ 0 + x,
method = "lm",
color = clrs[[2]],
size = 0.5,
se = FALSE
) +
ggplot2::stat_summary(
geom = "linerange",
fun.data = "mean_se",
size = 0.5,
show.legend = FALSE
) +
ggplot2::stat_summary(
geom = "point",
fun = "mean",
pch = 21,
color = "white",
fill = "black",
size = 2,
show.legend = FALSE
) +
ggplot2::labs(
x = expression(paste("Cell count (×", 10^3, ")")),
y = "DNA (ng)"
) +
theme_plots() +
ggplot2::coord_cartesian(xlim = c(0, NA), clip = "off") +
NULL
}
clean_dna_count_hypoxia <- function(dna_count_hypoxia_file) {
df <-
readr::read_csv(dna_count_hypoxia_file) %>%
dplyr::mutate(oxygen = factor(oxygen, levels = c("21%", "0.5%"))) %>%
clean_technical_replicates()
std <-
df %>%
dplyr::filter(!is.na(conc)) %>%
lm(value ~ conc, data = .)
df %>%
dplyr::filter(is.na(conc)) %>%
dplyr::mutate(conc = interpolate(., std)) %>%
dplyr::select(-value)
}
plot_dna_count_hypoxia <- function(dna_count_hypoxia) {
ggplot2::ggplot(dna_count_hypoxia) +
ggplot2::aes(
x = count / 1000,
y = conc,
color = oxygen,
fill = oxygen
) +
ggplot2::geom_smooth(
method = "lm",
size = 0.5,
formula = y ~ x,
se = FALSE,
show.legend = FALSE
) +
ggplot2::geom_point(
pch = 21,
color = "white",
# alpha = 0.3,
size = 2,
show.legend = FALSE) +
ggplot2::labs(
x = expression(paste("Cell count (×", 10^3, ")")),
y = "DNA (ng)"
) +
ggplot2::scale_x_continuous(breaks = seq(0, 300, 100)) +
ggplot2::scale_color_manual(values = clrs) +
ggplot2::scale_fill_manual(values = clrs) +
theme_plots() +
ggplot2::coord_cartesian(
ylim = c(0, NA),
xlim = c(0, 300),
clip = "off"
)
}
plot_k <- function(degradation_rates, k) {
annot <-
k %>%
dplyr::select(-k) %>%
dplyr::mutate(
label = "*",
ypos = Inf,
vjust = 1.5
)
degradation_rates %>%
dplyr::mutate(
group = dplyr::case_when(
oxygen == "21%" & treatment == "None" ~ "21%",
oxygen == "0.5%" ~ "0.5%",
treatment == "DMSO" ~ "DMSO"
),
group = factor(group, levels = c("21%", "0.5%", "DMSO"))
) %>%
dplyr::left_join(annot, by = c("metabolite", "oxygen", "treatment")) %>%
ggplot2::ggplot() +
ggplot2::aes(
x = reorder(toupper(abbreviation), k),
y = k
) +
ggplot2::stat_summary(
ggplot2::aes(fill = group),
geom = "col",
fun = "mean",
position = ggplot2::position_dodge(width = 0.6),
width = 0.6,
show.legend = TRUE
) +
ggplot2::geom_hline(
yintercept = 0,
color = "black",
lwd = 0.25
) +
ggplot2::stat_summary(
ggplot2::aes(group = group),
geom = "errorbar",
fun.data = "mean_se",
position = ggplot2::position_dodge(width = 0.6),
width = 0.2,
size = 0.25,
show.legend = FALSE
) +
ggplot2::geom_text(
ggplot2::aes(
color = group,
label = label,
y = ypos,
vjust = vjust
),
family = "Calibri",
size = 6/ggplot2::.pt,
position = ggplot2::position_dodge(width = 0.6),
show.legend = FALSE
) +
ggplot2::labs(
x = "Metabolite",
y = "Rate constant (/h)",
fill = NULL,
color = NULL
) +
ggplot2::scale_color_manual(
values = clrs,
limits = force
) +
ggplot2::scale_fill_manual(
values = clrs,
limits = force
) +
ggplot2::scale_y_continuous(expand = ggplot2::expansion(mult = 0.2)) +
theme_plots() +
ggplot2::theme(
panel.grid.major.x = ggplot2::element_line(color = "gray90"),
legend.key.width = ggplot2::unit(0.5, "lines"),
legend.key.height = ggplot2::unit(0.5, "lines"),
legend.position = "bottom",
legend.box.margin = ggplot2::margin(t = -10)
)
}
plot_evap_data <- function(evap_clean) {
evap_clean %>%
dplyr::filter(experiment == "05" & cell_type == "lf") %>%
dplyr::mutate(
oxygen = factor(oxygen, levels = c("21%", "0.5%"))
) %>%
ggplot2::ggplot() +
ggplot2::aes(
x = time,
y = volume,
color = oxygen,
fill = oxygen
) +
ggplot2::geom_smooth(
method = "lm",
formula = y ~ x,
se = FALSE,
size = 0.5,
show.legend = FALSE
) +
ggplot2::stat_summary(
geom = "linerange",
fun.data = "mean_se",
size = 0.5,
show.legend = FALSE
) +
ggplot2::stat_summary(
geom = "point",
fun = "mean",
pch = 21,
color = "white",
size = 2,
show.legend = FALSE
) +
ggplot2::labs(
x = "Time (h)",
y = "Volume (mL)"
) +
ggplot2::scale_x_continuous(breaks = seq(0, 72, 24)) +
ggplot2::scale_color_manual(values = clrs) +
ggplot2::scale_fill_manual(values = clrs) +
theme_plots()
}
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