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acidovoraxPangenome.Rmd
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---
title: "Acidovorax Pangenome study"
author: "Siani Roberto"
date: "28.01.2021"
output:
html_document:
theme: cosmo
highlight: monochrome
toc: true
toc_float:
collapsed: false
smooth_scroll: false
---
### SETUP
```{r, include = F}
## load required libraries
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse,
ggpubr,
FactoMineR,
factoextra,
caret,
openxlsx,
EnhancedVolcano,
doParallel,
patchwork,
hrbrthemes,
superheat,
ggtree)
## choose palette for visualization
my_palette = get_palette(c("#50b47b", # green for commensal
"#7e60b7", # purple for free-living
"#c06846"), # red for pathogen
3)
## coerce variables to binary distribution
binaryTransform = function(x){
if (x >= 1) {return(1)}
else{return(x)}
}
## check if all the variables are normally distributed
checkNormality = function(x){
all(lapply(x, shapiro.test) %>%
sapply(`[`, "p.value") < 0.05)
}
## list of the groups for stat_compare_means()
my_comparison = list(c("commensal", "free-living"),
c("free-living", "pathogen"),
c("pathogen", "commensal"))
```
### BIOASSAYS
```{r}
## load results from L.japonicus bioassays
IN = read.xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "growth_assay",
sep.names = " ") %>%
select(c(bacteria,
`plant part`,
`Length`,
`Fresh Weight`,
`Dry Weight`,
`Total Fresh Weight`,
`Total Dry Weight`,
`Total Length`)) %>%
mutate(coloring = ifelse(bacteria %in% "no bacteria", "Control", "Bacteria"))
## convert to tidy frame
DF = IN %>%
pivot_longer(cols = 3:8,
names_to = "metric") %>%
na.omit() %>%
group_by(bacteria, metric, `plant part`)
## fix categorical variables values
DF$`plant part`[DF$metric %in% "Total Length" | DF$metric %in% "Total Fresh Weight" | DF$metric %in% "Total Dry Weight"] = ""
rm(IN)
```
```{r}
## explore all the metrics for significance
p1a = ggplot(DF[DF$metric %in% "Total Fresh Weight", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(0, 0.65)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Total Fresh Weight" &
DF$coloring %in% "Control", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Total Fresh Weight (g)")
p1a +
plot_layout()
p1b = ggplot(DF[DF$metric %in% "Total Length", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(100, 350)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Total Length" &
DF$coloring %in% "Control", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Total Length (mm)")
p1b +
plot_layout()
p1c = ggplot(DF[DF$metric %in% "Length" &
DF$`plant part` %in% "Shoot", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(0, 150)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Length" &
DF$coloring %in% "Control" &
DF$`plant part` %in% "Shoot", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Shoot Length (mm)")
p1d = ggplot(DF[DF$metric %in% "Length" &
DF$`plant part` %in% "Root", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(0, 250)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Length" &
DF$coloring %in% "Control" &
DF$`plant part` %in% "Root", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Root Length (mm)")
p1c / p1d
p1e = ggplot(DF[DF$metric %in% "Fresh Weight" &
DF$`plant part` %in% "Shoot", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(0, 0.3)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Fresh Weight" &
DF$coloring %in% "Control" &
DF$`plant part` %in% "Shoot", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Shoot Fresh Weight (g)")
p1f = ggplot(DF[DF$metric %in% "Fresh Weight" &
DF$`plant part` %in% "Root", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(0, 0.4)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Fresh Weight" &
DF$coloring %in% "Control" &
DF$`plant part` %in% "Root", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Root Fresh Weight (g)")
p1e / p1f
p1g = ggplot(DF[DF$metric %in% "Total Dry Weight", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(0, 0.100)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Total Dry Weight" &
DF$coloring %in% "Control", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Total Dry Weight (g)")
p1g +
plot_layout()
p1h = ggplot(DF[DF$metric %in% "Dry Weight" &
DF$`plant part` %in% "Shoot", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(0, 0.1)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Dry Weight" &
DF$coloring %in% "Control" &
DF$`plant part` %in% "Shoot", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Shoot Dry Weight (g)")
p1i = ggplot(DF[DF$metric %in% "Dry Weight" &
DF$`plant part` %in% "Root", ],
aes(x = reorder(bacteria, value, FUN = median),
y = value,
fill = coloring,
alpha = 0.9)) +
geom_boxplot(outlier.shape = NA) +
theme_ipsum_rc(plot_margin = margin(1, 1, 1, 1),
grid = "XY") +
ylim(c(0, 0.05)) +
scale_fill_manual(values = c("#008080",
"#800000")) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
hjust = 0.5,
vjust = 0.75,
size = 10,
face = "bold"),
axis.title.x = element_blank()) +
stat_compare_means(ref.group = "no bacteria",
label = "p.signif",
hide.ns = T,
method.args = list(p.adjust.methods = "holm"),
method = "t.test") +
geom_hline(yintercept = median(DF[DF$metric %in% "Dry Weight" &
DF$coloring %in% "Control" &
DF$`plant part` %in% "Root", ]$value),
color = "#800000",
linetype = "dotted",
size = 1.5,
alpha = 0.6) +
ylab("Root Dry Weight (g)")
p1h / p1i
rm(p1a,
p1b,
p1c,
p1d,
p1e,
p1f)
```
```{r}
## final plot for the bioassays
DF_WIDE = DF %>%
summarise(mean = mean(value)) %>%
pivot_wider(names_from = c(`plant part`, metric),
values_from = mean,
names_sep = " ")
tDF = DF_WIDE[, 2:10] %>%
t() %>%
data.frame()
colnames(tDF) = DF_WIDE$bacteria
## load summarized results from the tests
effects = read.xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "growth_effects",
sep.names = " ") %>%
select(bacteria, effect)
tDF = tDF %>%
apply(MARGIN = 2,
FUN = function(x)
(((x - tDF$`no bacteria`)
/ tDF$`no bacteria`) * 100)) %>%
t %>%
as.data.frame() %>%
mutate(bacteria = DF_WIDE$bacteria) %>%
filter(!bacteria %in% "no bacteria") %>%
merge(effects,
by.y = "bacteria")
rownames(tDF) = tDF$bacteria
## plot heatmap
superheat(tDF[, 2:10],
scale = F,
legend.text.size = 15,
smooth.heat = T,
heat.pal = get_palette("npg", 3),
membership.rows = tDF$effect,
left.label.text.alignment = "left",
left.label.col = "white",
bottom.label.col = "white",
grid.hline.col = "black",
grid.vline = F,
padding = 0.1,
legend = T,
left.label = "variable",
bottom.label.size = 0.05,
legend.width = 3,
bottom.label.text.size = 6,
left.label.size = 0.05)
rm(DF_WIDE,
tDF,
effects,
DF)
```
```{r}
## load panMatrix for L.japonicus isolates only
IN = read.xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "panMatrix_LJR",
colNames = T,
rowNames = T) %>%
t
## conver to tidy frame
DF = data.frame(IN,
row.names = c(rownames(IN))) %>%
mutate(effect = ifelse(rownames(.) %in% c("LjRoot120", "LjRoot124", "LjRoot176", "LjRoot75", "LjRoot87", "LjRoot88", "LjRoot103"), "robust", "neutral")) %>%
t %>%
as.data.frame()
## write table to submit to Galaxy LEfSE
write.table(DF,
file = "forLefsa.tsv",
sep = "\t",
col.names = F,
quote = F)
## load summirized results
res.lda = read.xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "res.lefse",
colNames = T,
rowNames = F,
sep.names = " ") %>%
group_by(Outcome)
## plot results
ggbarplot(res.lda,
x = "Pfam",
y = "Logarithmic LDA score",
fill = "Family",
palette = get_palette("npg", 4),
sort.val = "asc",
width = 0.8,
alpha = 0.75,
ylab = "Logarithmic LDA score",
lab.col = "associated_group") +
theme_ipsum(grid = "XY",
plot_margin = margin(1, 1, 1, 1),
base_size = 20,
axis_title_size = 20) +
theme(axis.title.y = element_blank()) +
coord_flip()
```
### PANGENOME
```{r}
## load pyANI results for the strains
ANI = readxl::read_xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "ANI") %>%
column_to_rownames("key")
## load metadata for the plotting
classes = readxl::read_xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "metadata")
## hierarchical clustering (default distance, euclidean)
res_hc = hclust(dist(ANI),
method = "ward.D2")
## plotting the tree
a = ggtree(res_hc,
layout = "fan",
open.angle = 120,
root.position = 10) %<+% classes +
geom_tiplab(aes(color = factor(phenotype),
size = 15)) +
scale_color_manual(values = my_palette) +
theme(legend.position = "none",
plot.margin = margin(1, 1, 1, 1))
## loading pangenome structure
pangenome_info = readxl::read_xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "splits") %>%
select(c(Bin, Clusters)) %>%
mutate(Bin = fct_relevel(Bin,
"Core",
"Shell",
"Cloud",
"Singleton",
"Total"))
## plotting the pangenome structure
b = ggplot(pangenome_info,
aes(x = Clusters,
y = Bin,
fill = Bin)) +
xlim(c(0, 40000)) +
geom_bar(position = "stack",
stat = "identity",
width = 1,
alpha = 0.8) +
geom_text(aes(label = Clusters),
hjust = -0.05,
size = 9,
alpha = 0.8) +
scale_fill_manual(values = c("#264653","#2a9d8f","#e9c46a","#f4a261","#e76f51")) +
theme_ipsum(
plot_margin = margin(0, 0, 0, 0),
axis_text_size = 20,
axis_title_size = 20,
grid = F) +
xlab("N. of Gene Clusters") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())
## plotting all together
c = a +
inset_element(b,
left = 0.377,
bottom = 0,
right = 1.2,
top = 0.45,
align_to = "plot")
c +
plot_layout()
```
### GENOME ARCHITECTURE
```{r}
## load summarized info for the genomes
DF0 = read.xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "summary",
rowNames = F,
colNames = T,
sep.names = " ") %>%
mutate(logTotalLength = log10(total_length)) %>%
group_by(phenotype)
## plot a dot-chart of the genome length
A = ggdotchart(DF0,
x = "layers",
y = "logTotalLength",
color = "phenotype",
palette = my_palette,
group = "phenotype",
remove = "LjRoot141",
x.text.col = T,
ylab = "Logarithmic Genome Lenght",
ggtheme = theme_ipsum(grid = "X",
plot_margin = margin(0, 0, 1, 1),
axis_title_size = 20,
base_size = 15)) +
theme(legend.position = "none",
axis.title.x = element_blank())
## plot a box-plot of genome length by group
B = ggboxplot(DF0,
"phenotype",
"logTotalLength",
group = "phenotype",
fill = "phenotype",
palette = my_palette[c(2, 1, 3)],
order = c("free-living", "commensal", "pathogen"),
xlab = "",
ylab = "Logarithmic Genome Lenght",
ggtheme = theme_ipsum(
grid = "Y",
plot_margin = margin(0, 0, 0, 0),
axis_title_size = 20,
base_size = 20)) +
stat_compare_means(comparisons = my_comparison,
label = "p.signif") +
theme(legend.position = "none")
## plot a box-plot of number of genes per kb by group
C = ggboxplot(DF0,
"phenotype",
"num_genes_per_kb",
group = "phenotype",
fill = "phenotype",
palette = my_palette[c(2, 1, 3)],
order = c("free-living", "commensal", "pathogen"),
xlab = "",
ylab = "Number of genes per kb",
ggtheme = theme_ipsum(
grid = "Y",
plot_margin = margin(0, 0, 0, 0),
axis_title_size = 20,
base_size = 20)) +
stat_compare_means(comparisons = my_comparison,
label = "p.signif") +
theme(legend.position = "none")
## assemble plots
A /
(B + C) +
plot_layout(heights = c(1, 2)) +
plot_annotation(tag_levels = "a",
theme = theme_ipsum(plot_margin = margin(0, 0, 0, 0)))
## extract the observed phenotype and source for later
phenotype = DF0 %>%
dplyr::select(layers, phenotype) %>%
as.data.frame() %>%
mutate(phenotype = as.factor(phenotype),
ID = layers) %>%
select(!layers)
source = DF0 %>%
dplyr::select(layers, source) %>%
as.data.frame() %>%
mutate(source = as.factor(source),
ID = layers) %>%
select(!layers)
## clean environment
rm(DF0,
A,
B,
C)
```
### PRE-PROCESSING
```{r}
## source data as a table, transpose and convert in binary matrix (presence/absence)
TAB = read.xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "panMatrix",
colNames = T,
rowNames = T,
sep.names = " ") %>%
t() %>%
as.matrix() %>%
apply(MARGIN = c(1,2),
FUN = function(x) binaryTransform(x))
## preprocess by removing near-zero and zero variance column (I am not sure if corr is working ...)
DF = TAB %>%
preProcess(method = c("nzv",
"zv",
"corr"),
verbose = T) %>%
predict(TAB) %>%
as.data.frame()
rownames(DF) = phenotype$ID
## check normality for every variable (TRUE if all the variable are normal. Else FALSE: adjust preProcess arguments)
checkNormality(DF)
## clean-up environment
rm(TAB)
```
### PRINCIPAL COMPONENT ANALYSIS
```{r}
## PCA from FactoMineR, keeping 3 dimensions
res.pca = PCA(DF,
scale.unit = F,
graph = F,
ncp = 3)
## extract results for individuals
ind = get_pca_ind(res.pca)$coord %>%
as.data.frame() %>%
mutate(ID = rownames(.)) %>%
merge(source, by = "ID")
checkNormality(ind %>%
select(c(Dim.1, Dim.2, Dim.3)))
## test significance of distance between clusters
res.adonis = vegan::adonis(data = ind %>%
select(phenotype),
formula = ind %>%
select(Dim.1,
Dim.2,
Dim.3) ~ phenotype,
method = "eu",
permutations = 1000)
## ordination plot
A = ind %>%
ggplot(aes(x = Dim.1,
y = Dim.2,
group = phenotype,
color = phenotype,
label = ID,
shape = phenotype)) +
geom_point(size = 3,
alpha = 0.8) +
stat_ellipse(linetype = 0,
geom = "polygon",
type = "norm",
alpha = 0.25,
aes(fill = phenotype)) +
theme_ipsum(plot_margin = margin(0, 0, 0, 0),
base_size = 20,
axis_title_size = 20) +
scale_color_manual(values = my_palette) +
scale_fill_manual(values = my_palette) +
theme(legend.position = "none") +
labs(x = "Dim.1 (24.3%)",
y = "Dim.2 (12.4%)")
B = ind %>%
ggplot(aes(x = Dim.1,
y = Dim.3,
group = phenotype,
color = phenotype,
label = ID,
shape = phenotype)) +
geom_point(size = 3,
alpha = 0.8) +
stat_ellipse(linetype = 0,
geom = "polygon",
type = "norm",
alpha = 0.25,
aes(fill = phenotype)) +
theme_ipsum(plot_margin = margin(0, 0, 0, 0),
base_size = 20,
axis_title_size = 20) +
scale_color_manual(values = my_palette) +
scale_fill_manual(values = my_palette) +
labs(x = "Dim.1 (24.3%)",
y = "Dim.3 (10.3%)") +
theme(legend.title = element_blank(),
legend.text = element_text(size = 20))
## get contributions for the variables
cntrb = rbind(get_pca_var(res.pca)$contrib %>%
as.data.frame() %>%
slice_max(Dim.1),
get_pca_var(res.pca)$contrib %>%
as.data.frame() %>%
slice_max(Dim.2),
get_pca_var(res.pca)$contrib %>%
as.data.frame() %>%
slice_max(Dim.3)) %>%
mutate(Pfam = rownames(.)) %>%
pivot_longer(!Pfam,
names_to = "Dimension",
values_to = "Contribution") %>%
arrange(Dimension)
C = ggbarplot(cntrb,
x = "Pfam",
y = "Contribution",
fill = "Dimension",
position = position_dodge(),
width = 0.9,
alpha = 0.8,
palette = "Blues") +
theme_ipsum(plot_margin = margin(0, 0, 0, 0),
base_size = 15,
axis_title_size = 20) +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,
vjust = 1,
hjust = 1),
legend.text = element_text(size = 20),
legend.title = element_blank())
(A | B) / C +
plot_layout(heights = c(2, 1)) +
plot_annotation(tag_levels = "a",
theme = theme_ipsum(plot_margin = margin(0, 0, 0, 0)))
## cleaning environment
rm(cntrb,
ind,
res.pca,
A,
B,
C)
```
### GENE CLUSTERS FUNCTIONS ENRICHMENT ANALYSIS
```{r}
## load enriched functions data
DF3 = read.xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "enrichedFunctions",
sep.names = " ") %>%
filter(!associated_groups %in% "free-living",
!associated_groups %in% "pathogen, free-living",
!associated_groups %in% "commensal, free-living",
!associated_groups %in% "commensal, pathogen") %>%
na.exclude() %>%
mutate(log2fold = log2(enrichment_score))
## reverse enrichment score for one of the groups
DF3$log2fold[DF3$associated_groups %in% "commensal"] = 0 - DF3$log2fold[DF3$associated_groups %in% "commensal"]
## load enriched functions of interest data
DF4 = read.xlsx("~/Desktop/AvxPGC/IN/SupplementalMaterial.xlsx",
sheet = "curatedList") %>%
mutate(log2fold = log2(enrichment_score))
## reverse enrichment score for one of the groups
DF4$log2fold[DF4$associated_groups %in% "commensal"] = 0 - DF4$log2fold[DF4$associated_groups %in% "commensal"]
## volcano plot with enrichment score x adjusted q value
A = EnhancedVolcano(DF3,
lab = DF3$Pfam,
selectLab = DF4$Pfam,
x = "log2fold",
y = "adjusted_q_value",
cutoffLineType = "dotted",
pointSize = 3,
FCcutoff = NA,
ylim = c(0, 20),
xlim = c(-10, 15),
colAlpha = 0.33,
labSize = 0,
pCutoff = 0.05,
col = c("#2a5b6f",
"#000000",
"#cc6092")) +
labs(y = bquote(~-Log[10]~ "q-value")) +
theme_ipsum(grid = "XY",
plot_margin = margin(0, 0, 0, 0),
axis_title_size = 20) +
theme(legend.position = "none",
plot.title = element_blank(),
plot.subtitle = element_blank(),
plot.caption = element_blank())
## dot-chart of enrichment score for all the functions of interest
B = ggdotchart(DF4,
x = "Pfam",
y = "log2fold",
color = "Family",
sorting = "asc",
palette = get_palette("npg", 4),
dot.size = 2.5,
y.text.col = T,
ggtheme = theme_ipsum(grid = "X",
plot_margin = margin(0, 0, 0, 0),
base_size = 20,
axis_title_size = 20),
font.tickslab = c(12),
ticks = F,
ylab = "Enrichment Score") +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 80,
hjust = 1,
vjust = 1),
legend.title = element_blank())
## combined graph
(A / B) +
plot_layout(heights = c(2, 1)) +
plot_annotation(tag_levels = "a",
theme = theme_ipsum(plot_margin = margin(0, 1, 0, 0)))
## clean environment
rm(DF3,
DF4,
A,
B)
```