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paper_figures.R
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## curves for paper
# Load required packages and utility scripts
source("packages.R")
source("utils.R")
source("plot_functions.R")
source("parameters.R")
source("lfa_test_data.R")
set.seed(2020)
traj <- readRDS(file.info(list.files("results/", full.names = T)) %>%
as.data.frame() %>%
rownames_to_column()%>%
filter(str_detect(rowname,"traj_")) %>%
slice_max(mtime) %>%
pull(rowname))
trajectories_to_plot <- traj %>%
#filter(type=="symptomatic") %>%
group_by(type) %>%
sample_n(4) %>%
#ungroup() %>%
mutate(pred = map(.x = m,
~data.frame(x = seq(0, 25, length.out = 101)) %>%
mutate(ct = .x(x)))) %>%
unnest(pred) %>%
mutate(LFT = stats::predict(innova_mod,
newdata =
data.frame(ct = ct),
type = "response"
),
culture=stats::predict(culture_mod,
newdata =
data.frame(ct = ct),
type = "response"
),
PCR=ifelse(ct<35,1,0)) %>%
arrange(type)
traj_a <- trajectories_to_plot %>%
#pivot_longer(cols=c(ct,culture,LFT)) %>%
ggplot()+
geom_line(aes(x=x,
y=ct,
group=interaction(sim,idx),
colour="Cycle threshold",
size=(factor(type))))+
scale_color_manual(values="black",name="",guide=F)+
scale_x_continuous(name="Days since exposure",breaks = breaks_width(7))+
scale_size_manual(name="",labels=capitalize, values = c("symptomatic" = 1, 'asymptomatic' = 0.5))+
scale_y_reverse(# Features of the first axis
limits=c(39.99,NA),
name = "Cycle\nthreshold")+
plotting_theme+
theme( strip.background = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
strip.text.x = element_blank(),
#axis.title.x = element_blank()
)+
facet_wrap(~fct_rev(type)+interaction(sim,idx),nrow = 1)
traj_b <- trajectories_to_plot %>%
#pivot_longer(cols=c(ct,culture,LFT)) %>%
ggplot()+
geom_line(aes(x=x,
y=culture,
group=interaction(sim,idx),
colour="Culture",
size=fct_rev(type)))+
geom_line(aes(x=x,
y=LFT,
group=interaction(sim,idx),
colour="LFT",
size=fct_rev(type)))+
geom_line(aes(x=x,
y=PCR,
group=interaction(sim,idx),
colour="PCR",
size=fct_rev(type)))+
scale_color_manual(values=traj_pal,name="")+
scale_size_manual(name="",labels=capitalize, guide = F,
values = c("symptomatic" = 1, 'asymptomatic' = 0.5))+
scale_x_continuous(name="Days since exposure",breaks = breaks_width(7))+
scale_y_continuous(# Features of the first axis
name = "Probability\nof detection")+
plotting_theme+
theme( strip.background = element_blank(),
strip.text.x = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank()) +
facet_wrap(~fct_rev(type)+interaction(sim,idx),nrow = 1) +
guides(color = guide_legend(override.aes = list(size = 2) ) )
pickering_plot <- pickering %>%
rename("LFT"=Innova,"Culture"=Culture) %>%
pivot_longer(cols = c(`SureScreen F`,`LFT`,Encode,`Culture`)) %>%
filter(name%in%c("Culture","LFT")) %>%
# bind_rows(innova_liv_sim %>% select(ct,AG) %>% rename("value"=AG) %>% mutate(name="Liverpool")) %>%
drop_na(value) %>%
ggplot(aes(x=ct,y=value,colour=name))+
#geom_jitter(alpha=0.25,width = 0, height =0.05)+
geom_point(alpha = 0.25, pch = "|", size = 4) +
geom_smooth(method = "glm",se=F,method.args=list(family="binomial"),size=0.5)+
# geom_line(data=infectivity %>% select(ct,value) %>%
# mutate(name="Lee et al. infectivity"),
# aes(x=ct,y=value,colour=name),size=1)+
geom_line(data=tribble(~ct,~value,
10,1,
35 ,1,
35.0001,0,
40,0) %>%
mutate(name="PCR"),
aes(x=ct,y=value,colour=name),
size=0.5) +
scale_color_manual(values=traj_pal,name="",guide=F)+
plotting_theme +
theme(panel.border = element_rect(fill=NA),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank())+
scale_x_reverse(
#breaks=scales::breaks_width(5)
)+
#scale_x_continuous(breaks=seq(0,9),labels=c("0\n37.1","1\n33.8","2\n30.5", "3\n27.2", "4\n23.9", "5\n20.6", "6\n17.35", "7\n14.1", "8\n10.8", "9\n7.5" ))+
labs(x="Cycle threshold",
y="Probability\nof detection",
colour="")+
facet_wrap(~name)
trajectories_plot <- traj_a/pickering_plot/traj_b+
plot_layout(ncol=1,heights = c(1,1.5,1),guides="collect")&
plot_annotation(tag_levels = "A")&
theme(legend.position = "bottom",strip.placement = NULL)
save_plot(plot = trajectories_plot, dpi=400,
device="png",prefix = "trajectories", height = 150,width=210)
trajectories_to_plot %>%
ggplot()+
geom_line(aes(x=x,
y=ct,
group=interaction(sim,idx)),
colour=traj_pal[1],show.legend = F)+
# geom_line(aes(x=x,
# y=culture*40,
# group=interaction(sim,idx),
# colour="Culture"))+
# geom_line(aes(x=x,
# y=LFT*40,
# group=interaction(sim,idx),
# colour="LFT"))+
scale_colour_viridis_d(name="",guide=F)+
scale_x_continuous(name="Days since exposure")+
scale_y_continuous(name="CT")+
theme_half_open()
save_plot(dpi=400,
device="png",prefix = "trajectories_ct", height = 100,width=100)
trajectories_to_plot %>%
ggplot()+
geom_line(aes(x=x,
y=culture,
group=interaction(sim,idx)),
colour=traj_pal[2],show.legend = F)+
# geom_line(aes(x=x,
# y=LFT*40,
# group=interaction(sim,idx),
# colour="LFT"))+
scale_x_continuous(name="Days since exposure")+
scale_y_continuous(name="Culture probability",limits=c(0,1))+
theme_half_open()
save_plot(dpi=400,
device="png",prefix = "trajectories_culture", height = 100,width=100)
trajectories_to_plot %>%
ggplot()+
geom_line(aes(x=x,
y=LFT,
group=interaction(sim,idx)),
colour=traj_pal[3],show.legend = F)+
# geom_line(aes(x=x,
# y=LFT*40,
# group=interaction(sim,idx),
# colour="LFT"))+
scale_x_continuous(name="Days since exposure")+
scale_y_continuous(name="LFT probability",limits=c(0,1))+
theme_half_open()
save_plot(dpi=400,
device="png",prefix = "trajectories_lft", height = 100,width=100)
trajectories_to_plot %>%
ggplot()+
geom_line(aes(x=x,
y=PCR,
group=interaction(sim,idx)),
colour=traj_pal[4],show.legend = F)+
# geom_line(aes(x=x,
# y=LFT*40,
# group=interaction(sim,idx),
# colour="LFT"))+
scale_x_continuous(name="Days since exposure")+
scale_y_continuous(name="PCR probability",limits=c(0,1))+
theme_half_open()
save_plot(dpi=400,
device="png",prefix = "trajectories_pcr", height = 100,width=100)
# lft_plot <- trajectories_to_plot %>%
# ggplot(data = ., aes(x = x, y = y)) +
# # annotate(geom = "rect",
# # ymax = Inf, xmin = -Inf, xmax = Inf, ymin = 30,
# # color = NA,
# # fill = "black", alpha = 0.1) +
# geom_line(aes(group = idx,
# color = LFT,
# ),
# alpha = 0.1) +
# theme_bw() +
# facet_wrap(~value,labeller = labeller(value=capitalize),ncol=1)+
# #geom_hline(aes(yintercept=35,linetype="PCR detection threshold"))+
# #geom_hline(aes(yintercept=30,linetype="Infectivity threshold"))+
# scale_linetype_manual(name="",values=c("dotted","dashed"))+
# # geom_hline(data = mask,
# # aes(yintercept = max),
# # lty = 2) +
# scale_color_viridis_c(option="magma",begin=0.2,end=0.8,
# name = "Probability of detection by LFT",guide=guide_colorsteps(ticks=T,barwidth=unit(5,"cm"))) +
# ylab("Ct value") +
# xlab("Time since exposure (days)") +
# plotting_theme +
# theme(panel.spacing = unit(1,"cm")) +
# scale_y_reverse()+coord_cartesian(expand = F)
#
#
# culture_plot <- trajectories_to_plot %>%
# ggplot(data = ., aes(x = x, y = y)) +
# geom_line(aes(group = idx,
# color = culture,
# ),
# alpha = 0.1) +
# theme_bw() +
# facet_wrap(~value,labeller = labeller(value=capitalize),ncol=1)+
# scale_linetype_manual(name="",values=c("dotted","dashed"))+
# scale_color_viridis_c(option="viridis",begin=0.2,end=0.8,
# name = "Probability of viral culture",guide=guide_colorsteps(ticks=T,barwidth=unit(5,"cm"))) +
# ylab("Ct value") +
# xlab("Time since exposure (days)") +
# plotting_theme +
# theme(panel.spacing = unit(1,"cm")) +
# scale_y_reverse()+coord_cartesian(expand = F)
#
# lft_plot+culture_plot
#
# save_plot(dpi=400,
# device="png",prefix = "trajectories", height = 200,width=210)
#
#
# trajectories_to_plot %>%
# ggplot(data = ., aes(x = x, y = culture)) +
# geom_line(aes(group = idx,
# #color = culture,
# ),
# alpha = 0.1) +
# stat_summary(fun="mean",geom="line",colour="red",linetype="dashed")+
# stat_summary(fun="median",geom="line",colour="red",linetype="solid")+
# theme_bw() +
# facet_wrap(~value,labeller = labeller(value=capitalize),ncol=1)+
# scale_linetype_manual(name="",values=c("dotted","dashed"))+
# scale_color_viridis_c(option="viridis",begin=0.2,end=0.8,
# name = "Probability of viral culture",guide=guide_colorsteps(ticks=T,barwidth=unit(5,"cm"))) +
# ylab("Probability of culture") +
# xlab("Time since exposure (days)") +
# plotting_theme +
# theme(panel.spacing = unit(1,"cm")) +
# #scale_y_reverse()+
# coord_cartesian(expand = F)
#