Chapter 14 Comparing metabolic capacity of positively and negatively associated bacteria
14.1 Load MG data
load("data/data_mg.Rdata")
trends_bw <-
read_tsv("data/hmsc_bw_trend.tsv") %>%
mutate(trend_07 = case_when(bw_support > 0.85 ~ 'positive',
bw_support < 0.15 ~ 'negative',
TRUE ~ 'non-significant')) %>%
mutate(trend_09 = case_when(bw_support > 0.95 ~ 'positive',
bw_support < 0.05 ~ 'negative',
TRUE ~ 'non-significant'))
gifts <-
gifts_elements %>%
as.data.frame() %>%
select(where(~ sum(.) > 0))
gifts_funct <-
gifts_functions %>%
as.data.frame() %>%
select(where(~ sum(.) > 0))
avg_mci <- rowMeans(gifts)
gifts_funct$avg_mci <- avg_mci
avg_mci_bio <- rowMeans(gifts[,grepl("B",names(gifts))])
gifts_funct$avg_mci_bio <- avg_mci_bio
avg_mci_deg <- rowMeans(gifts[,grepl("D",names(gifts))])
gifts_funct$avg_mci_deg <- avg_mci_deg
gift_colors <-
read_tsv("data/gift_colors.tsv") %>%
mutate(legend = str_c(Code_function, " - ", Function))14.2 Taxonomy of positively and negatively associated MAGs
14.2.1 Positive group
All MAGs with positive association belong to RF39
trends_bw %>%
filter(trend_09 == "positive") %>%
select(genome) %>%
left_join(genome_taxonomy, by = 'genome') %>%
select(order) %>%
table()order
RF39
10 14.2.2 Negative group
MAGs with negative association belong to various orders, mainly to Lachnospirales and Oscillospirales
trends_bw %>%
filter(trend_09 == "negative") %>%
select(genome) %>%
left_join(genome_taxonomy, by = 'genome') %>%
select(order) %>%
table() %>%
sort()order
Actinomycetales Campylobacterales Opitutales RF39 Saccharimonadales UBA1381
1 1 1 1 1 1
RF32 TANB77 Coriobacteriales Bacteroidales Erysipelotrichales Lactobacillales
4 4 5 8 8 12
Christensenellales Oscillospirales Lachnospirales
27 35 81 14.3 Compute MCIs of BW positive and negative associated communities
gifts_funct_bw_pos <-
gifts_funct %>%
mutate(avg_mci_bio = gifts_funct$avg_mci_bio,
avg_mci_deg = gifts_funct$avg_mci_deg,
bw_effect = trends_bw$trend_09) %>%
filter(bw_effect == "positive") %>%
select(-bw_effect)
gifts_funct_bw_neu <-
gifts_funct %>%
mutate(avg_mci_bio = gifts_funct$avg_mci_bio,
avg_mci_deg = gifts_funct$avg_mci_deg,
bw_effect = trends_bw$trend_09) %>%
filter(bw_effect == "non-significant") %>%
select(-bw_effect)
gifts_funct_bw_neg <-
gifts_funct %>%
mutate(avg_mci_bio = gifts_funct$avg_mci_bio,
avg_mci_deg = gifts_funct$avg_mci_deg,
bw_effect = trends_bw$trend_09) %>%
filter(bw_effect == "negative") %>%
select(-bw_effect)mean_func <- function(data, indices) {
return(mean(data[indices]))
}
boot_pos_df <- data.frame(matrix(nrow = ncol(gifts_funct_bw_pos), ncol = 4))
colnames(boot_pos_df) <- c("function_id", "mean", "ci_05", "ci_95")
boot_neu_df <- data.frame(matrix(nrow = ncol(gifts_funct_bw_neu), ncol = 4))
colnames(boot_neu_df) <- c("function_id", "mean", "ci_05", "ci_95")
boot_neg_df <- data.frame(matrix(nrow = ncol(gifts_funct_bw_neg), ncol = 4))
colnames(boot_neg_df) <- c("function_id", "mean", "ci_05", "ci_95")
for(i in 1:ncol(gifts_funct_bw_pos)){
boot_pos_df[i, "function_id"] <- colnames(gifts_funct_bw_pos)[i]
if(sum(gifts_funct_bw_pos[, i]) == 0){
boot_pos_df[i, "mean"] <- 0
boot_pos_df[i, "ci_05"] <- 0
boot_pos_df[i, "ci_95"] <- 0
}
else{
boot_temp_pos <- boot(gifts_funct_bw_pos[, i], statistic = mean_func, R = 10000)
boot_temp_pos_ci <- boot.ci(boot_temp_pos, type = "bca")
boot_pos_df[i, "mean"] <- boot_temp_pos_ci$t0
boot_pos_df[i, "ci_05"] <- boot_temp_pos_ci$bca[4]
boot_pos_df[i, "ci_95"] <- boot_temp_pos_ci$bca[5]
}
boot_neu_df[i, "function_id"] <- colnames(gifts_funct_bw_neu)[i]
boot_temp_neu <- boot(gifts_funct_bw_neu[, i], statistic = mean_func, R = 10000)
boot_temp_neu_ci <- boot.ci(boot_temp_neu, type = "bca")
boot_neu_df[i, "mean"] <- boot_temp_neu_ci$t0
boot_neu_df[i, "ci_05"] <- boot_temp_neu_ci$bca[4]
boot_neu_df[i, "ci_95"] <- boot_temp_neu_ci$bca[5]
boot_neg_df[i, "function_id"] <- colnames(gifts_funct_bw_neg)[i]
boot_temp_neg <- boot(gifts_funct_bw_neg[, i], statistic = mean_func, R = 10000)
boot_temp_neg_ci <- boot.ci(boot_temp_neg, type = "bca")
boot_neg_df[i, "mean"] <- boot_temp_neg_ci$t0
boot_neg_df[i, "ci_05"] <- boot_temp_neg_ci$bca[4]
boot_neg_df[i, "ci_95"] <- boot_temp_neg_ci$bca[5]
}
boot_df <- data.frame(rbind(boot_pos_df %>% mutate(bw_association = "postive"),
boot_neu_df %>% mutate(bw_association = "non-significant"),
boot_neg_df %>% mutate(bw_association = "negative")))
gifts_funct_df <- data.frame(rbind(gifts_funct_bw_pos %>% mutate(bw_association = "postive"),
gifts_funct_bw_neu %>% mutate(bw_association = "non-significant"),
gifts_funct_bw_neg %>% mutate(bw_association = "negative")))14.3.1 MCI comparisons of biosynthesis pathways
## B01
p_B01_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B01, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B01, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "B01"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## B02
p_B02_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B02, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B02, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "B02"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## B03
p_B03_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B03, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B03, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data=boot_df %>% filter(function_id=="B03"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association,color=bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35))+
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal()+
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## B04
p_B04_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B04, color = bw_association),
alpha = 0.5, width = 0.25)+
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B04, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "B04"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## B06
p_B06_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B06, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B06, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "B06"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## B07
p_B07_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B07, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B07, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "B07"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## B08
p_B08_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B08, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B08, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "B08"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## B09
p_B09_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B09, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B09, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "B09"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## B10
p_B10_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = B10, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = B10, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "B10"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## Avg. MCI Biosynthesis
p_Mci_bio_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = avg_mci_bio, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = avg_mci_bio, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "avg_mci_bio"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
ylab("Avg. bio. MCI") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## Avg. MCI
p_Mci_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = avg_mci, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = avg_mci, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "avg_mci"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
ylab("Avg. MCI") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
14.3.3 All
grid.arrange(p_Mci_bio_mg, p_B01_mg, p_B02_mg,
p_B03_mg, p_B04_mg, p_B06_mg,
p_B07_mg, p_B08_mg, p_B09_mg,
p_B10_mg, ncol = 3)
14.3.3.1 MCI comparisons of degradation pathways
## D01
p_D01_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = D01, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = D01, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "D01"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## D02
p_D02_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = D02, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = D02, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "D02"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## D03
p_D03_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = D03, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = D03, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "D03"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size =14))
## D05
p_D05_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = D05, color = bw_association),
alpha = 0.5,width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = D05, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "D05"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## D06
p_D06_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = D06, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = D06, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "D06"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## D07
p_D07_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = D07, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = D07, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "D07"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## D08
p_D08_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = D08, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = D08, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "D08"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## D09
p_D09_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = D09, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = D09, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "D09"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
## Avg. MCI Degradation
p_Mci_deg_mg <-
ggplot() +
geom_jitter(data = gifts_funct_df, aes(x = bw_association, y = avg_mci_deg, color = bw_association),
alpha = 0.5, width = 0.25) +
geom_boxplot(data = gifts_funct_df, aes(x = bw_association, y = avg_mci_deg, fill = bw_association),
alpha = 0.1, outlier.shape = NA, width = 0.5) +
geom_linerange(data = boot_df %>% filter(function_id == "avg_mci_deg"),
aes(ymin = ci_05, ymax = ci_95, x = bw_association, color = bw_association),
linewidth = 4, size = 1, position = position_nudge(x = 0.35)) +
scale_color_manual(values = c("red3","grey50","blue3")) +
scale_fill_manual(values = c("red3","grey50","blue3")) +
xlab("Body-weight association") +
ylab("Avg. deg. MCI") +
theme_minimal() +
theme(legend.position = 'none',
axis.title.x = element_text(size = 14),
axis.text.x = element_text(size = 8),
axis.text.y = element_text(size = 12),
axis.title.y = element_text(size = 14))
14.4 Heatmap of MCI at element level
Not included in Supplementary.
gifts_bw_pos <-
gifts %>%
mutate(bw_effect = trends_bw$trend_09) %>%
filter(bw_effect == "positive") %>%
select(-bw_effect)
gifts_bw_neu <-
gifts %>%
mutate(bw_effect = trends_bw$trend_09) %>%
filter(bw_effect == "non-significant") %>%
select(-bw_effect)
n_neg_sp <- 50
gifts_bw_neg <-
gifts %>%
mutate(bw_effect = trends_bw$trend_09, bw_param = trends_bw$parameter) %>%
filter(bw_effect == "negative") %>%
arrange(bw_param) %>%
slice(1:n_neg_sp) %>%
select(-c(bw_effect, bw_param))
gifts_bw_df_long <-
data.frame(rbind(gifts_bw_pos, gifts_bw_neg)) %>%
mutate(bw_association = c(rep("positive", nrow(gifts_bw_pos)), rep("negative", nrow(gifts_bw_neg)))) %>%
select(-matches("S0")) %>%
rownames_to_column(var = "genome") %>%
pivot_longer(!c(genome, bw_association), names_to = "Element", values_to = "mci") %>%
mutate(Function = substr(Element, start = 1, stop = 3)) %>%
relocate(Function, .after = Element)
# Heatmap
gifts_bw_df_long %>%
rename(Code_element = "Element") %>%
rename(Code_function = "Function") %>%
left_join(gift_colors, by = "Code_function") %>%
group_by(Code_element) %>%
filter(sum(mci) > 0) %>%
ungroup() %>%
ggplot(., aes(x = genome, y = Code_element, fill = bw_association, group = legend, alpha = mci)) +
geom_tile(color = "#ffffff") +
scale_y_discrete(guide = guide_axis(check.overlap = TRUE)) +
scale_x_discrete(guide = guide_axis(check.overlap = TRUE)) +
scale_fill_manual(values = c("red3",
"blue3"),
na.value = "#f4f4f4") +
facet_grid(legend ~ bw_association, scales = "free", space = "free") +
theme_void(base_size = 8) +
theme(axis.text.y = element_blank(),
strip.text.y = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6),
legend.position = "none")