scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE))
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1, alpha = 0.9) +
geom_point(size = 5, alpha = 0.9) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(1.0, 1.0, 0.01, 0.01),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.00, 0.00),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.00),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.0, 0.00),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.00),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.9),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.9),guide=F)
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.0, 0.0),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.0),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.0),guide=F)
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.0),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.9),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.0),guide=F)
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.0, 0.0),guide=F)
p2
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.0),guide=F)
p2 <- ggplot(
scores %>% filter(
Memory > 0
) %>%  # filter(Method %in% c("CONSULT-II v0.4.0", "KRANK v0.3.2")) %>%
mutate(
Distance_to_closest = cut(
Distance_to_closest,
include.lowest = TRUE,
breaks = c(0.25, 0.1, 0.01, 0)
)
) %>%
group_by(Method, Memory, Distance_to_closest) %>%
summarise(Recall = mean(Recall), F1 = mean(F1), Precision = mean(Precision)) %>% filter(!is.na(Distance_to_closest))) +
aes(y = F1, x = Memory, shape=reorder(Method, F1), color=reorder(Method,F1), alpha = Method) +
facet_wrap(~factor(Distance_to_closest, levels=c('(0.1,0.25]','(0.01,0.1]', '[0,0.01]')), scales = "free_y", nrow = 3) +
geom_line(aes(group=Method, alpha = Method),size = 1) +
geom_point(size = 5) +
labs(shape = "Tool", colour = "Tool", x = "Memory (Gb)", y = "F1", title = "Short-read classification in WoL-v1") +
scale_color_manual(values=c("#e31a1c", "#ff7f00", "#33a02c","#1f78b4")) +
scale_shape_manual(values=c(18, 17,15,16)) +
theme_cowplot(font_size = 17) +
theme(   legend.position = "bottom",
legend.justification = "center",
legend.direction = "vertical",
legend.title = element_blank(),
legend.margin=margin(0,0,0,0)
) + guides(color=guide_legend(nrow=1,byrow=TRUE)) +
scale_x_continuous(trans = "log", breaks = c(4, 16, 64)) +
guides(shape=guide_legend(nrow=2,byrow=TRUE)) +
guides(color=guide_legend(nrow=2,byrow=TRUE)) +
scale_alpha_manual(values=c(0.9, 0.9, 0.9, 0.0),guide=F)
p2
