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Microbiome_Liver_Transplant_Manuscript.Rmd
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---
title: "Microbiome in Liver Transplant Manuscript"
author: "Nicholas P. Dylla"
date: "`r Sys.Date()`"
output:
html_document:
theme: yeti
highlight: pygments
toc: true
toc_float: true
toc_depth: 3
code_folding: hide
df_print: paged
editor_options:
chunk_output_type: console
header-includes:
- |
```{=latex}
\usepackage{fvextra}
\DefineVerbatimEnvironment{Highlighting}{Verbatim}{
showspaces = false,
showtabs = false,
breaklines,
commandchars=\\\{\}
}
```
---
# {.tabset}
## Package Loading and Data Importing
### Load Packages and Custom Functions
```{r, package-loading, message=FALSE, warning=FALSE}
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
if (!require("pacman", quietly = TRUE))
install.packages("pacman")
if (!require("gtsummary", quietly = TRUE))
remotes::install_github("ddsjoberg/gtsummary")
if (!require("mixOmics", quietly = TRUE))
BiocManager::install("mixOmicsTeam/mixOmics")
if (!require("yingtools2", quietly = TRUE))
remotes::install_github("ying14/yingtools2")
if (!require("ggpirate", quietly = TRUE))
remotes::install_github("mikabr/ggpirate")
if (!require("paletteer", quietly = TRUE))
remotes::install_github("EmilHvitfeldt/paletteer")
if (!require("microbiomeMarker", quietly = TRUE))
BiocManager::install("microbiomeMarker")
if (!require("magick", quietly = TRUE))
install.packages("magick")
if (!require("mltest", quietly = TRUE))
install.packages("mltest")
if (!require("biostatUtil", quietly = TRUE))
remotes::install_github("talhouklab/biostatUtil")
library(pacman)
p_load(
factoextra, # Dimension reduction
FactoMineR, # Dimension reduction
stabs, # Stability variable selection
mboost, # Gradient boosting for model building
ggrepel, # Visualization, repels labels on plots
bestglm, # Logistic regression
knitr, # To change R markdown PDF options
caret, # To calculate model paramters
cutpointr, # Calculate cutpoints
RPostgreSQL, # Connect to PostgreSQL server
phyloseq, # Phyloseq data wrangling
# microbiomeMarker, # LEfSe analysis
ComplexHeatmap, # Heatmap generator
paletteer, # Extensive color palette
tidyverse, # Data wrangling and visualization
circlize, # Color ramp builder
rstatix, # Tidyverse statistics
EnhancedVolcano, # Volcano plot analysis
umap, # Uniform Manifold Approximation Projection
ggpubr, # Simple ggplots with stats
ggpirate, # ggplot version of Pirate plot
mixOmics, # PLS-DA
conflicted, # Forces conflicted packages to be used by preferred package
gridExtra, # Arrange multiple grobs
reticulate, # Run python in R
tableone, # Additional clinical tables support
lubridate, # Manipulate date objects
yingtools2, # Custom plotting functions
cowplot, # Combine plots together
epiR, # Obtain model performance measures
pROC, # Produce ROC curves
plotrix, # Add table to base R plot
glmnet, # LASSO regression
forcats, # Factor reordering
ggpmisc, # Miscellaneous ggplot helper functions
doParallel, # Parallelize functions (for optimizing cutpoints)
PRISMAstatement, # Flow table generator
DiagrammeRsvg, # Flow table visualizer
rsvg, # Flow table visualizer
scales, # Scale functions for visualizations
devtools, # To load packages from GitHub
# datscience, # Bibliography manager
formatR, # Markdown code formatter
openxlsx, # Excel worksheet manipulator
biostatUtil, # Mainly used for multi-level confusion matrix confidence intervals
irr, # Dependency package
rJava, # Dependency package
install = FALSE
)
library("gtsummary") # Clinical tables, pacman had trouble loading this in, so had to go the manual route
{conflict_prefer("select", "dplyr")
conflict_prefer("mutate", "dplyr")
conflict_prefer("filter", "dplyr")
conflict_prefer("rename", "dplyr")
conflict_prefer("slice", "dplyr")
conflict_prefer("between", "dplyr")
conflict_prefer("annotate", "ggplot2")}
devtools::source_url("https://github.com/yingeddi2008/DFIutility/blob/master/getRdpPal.R?raw=TRUE")
# ggplot theme shortcuts
et <- element_text
eb <- element_blank
er <- element_rect
el <- element_line
opts_chunk$set(tidy.opts = list(width.cutoff = 60), tidy = TRUE)
`%!in%` <- negate(`%in%`)
# Running python in R
use_condaenv("base", required = TRUE)
```
## Import Data
### Load in all Metagenomic, Metabolomic and Clinical Data
```{r, load-in-data, message=FALSE, warning=FALSE}
# 1) Load R image
load('./Data/LT_Modeling.RData')
# # OR #
#
# # 2) Individually Load R Objects
#
# # Sample lookup
# sample_lookup <- readRDS("./Data/sample_lookup.rds")
#
# # First samples from all patients
# first_samps <- readRDS("./Data/first_samps_anon.rds")
#
# # Simplified dataframe containing infection information and relative abundance of targeted taxa
# peri_matrix_all <- readRDS("./Data/peri_matrix_clin_all.rds")
#
# # Distinct detailed infection data
# peri_criteria_best <- readRDS("./Data/peri_criteria_best_anon.rds")
#
# # All detailed infection data
# peri_criteria_all <- readRDS("./Data/peri_criteria_all_anon.rds")
#
# # NCBI taxonomy lookup
# tax_lookup <- readRDS("./Data/tax_lookup.rds")
#
# # Complete metaphlan dataframe for all patients and healthy donors
# metaphlan_df <- readRDS("./Data/metaphlan_anon.rds")
#
# # Metaphlan dataframe of patient samples
# metaphlan_peri_anon <- readRDS("./Data/metaphlan_peri_anon.rds")
#
# # Custom color palette
# metaphlan_pal <- getRdpPal(metaphlan_df)
#
# # Qualitative metabolomics
# metab_qual_anon <- readRDS("./Data/metab_qual_anonym.rds")
#
# # Quantitative metabolomics
# metab_quant_anon <- readRDS("./Data/metab_quant_anonym.rds")
#
# Antibiotics data
#
# abx <- readRDS("./Data/abx_anon.rds")
# abx <- readRDS("./Data/original_cohort_abx.rds")
#
# Demographics data
# demo <- readRDS("./Data/demo_anon.rds")
#
# # Bile acid gene data
# ba_genes <- readRDS("./Data/bile_acid_genes.rds")
#
# # CARD genes
# card_dict <- readRDS("./Data/card_dict.rds")
#
# card_dict <- card_dict %>%
# filter(dbsource == "card") %>%
# mutate(
# AMRGeneFamily = ifelse(
# AMRGeneFamily == "glycopeptide resistance gene cluster;van ligase",
# "glycopeptide resistance gene cluster;vanA",
# AMRGeneFamily
# ),
# AMRGeneFamily = gsub(
# pattern = ";",
# replacement = "\n",
# x = AMRGeneFamily
# )
# )
#
# CARD data
# card2 <- readRDS("./Data/card.rds")
```
## Figures
### Optimal Cutpoint Analysis: Relative Abundance of Enterococcus and Enterobacerales
```{r, optimal-cutpoint-analysis, fig.dim = c(16, 12), message=FALSE, warning=FALSE}
# Custom function to avoid any errors during map
safe_cutpointr <- possibly(.f = cutpointr, otherwise = "Error")
# Calculate the number of cores
no_cores <- detectCores() - 2
# create the cluster for caret to use
cl <- makePSOCKcluster(no_cores)
registerDoParallel(cl)
set.seed(123456)
test_abundance <-
peri_matrix_all %>%
mutate(sampleID = as.factor(sampleID)) %>%
select(starts_with(c("entero","esch","klebs","citro","rahn","proteus")), -ends_with("abs_abundance")) %>%
mutate_if(is.character, as.numeric) %>%
mutate(
enterococcus_infection = ifelse(`Enterococcus faecium` + `Enterococcus faecalis` + `Enterococcus avium` >= 1, 1, 0),
enterobacterales_infection = ifelse(`Escherichia coli`+ `Klebsiella pneumoniae`+ `Citrobacter freundii`+`Proteus mirabilis` >= 1, 1, 0)) %>%
select(-c(`Enterococcus faecium`, `Enterococcus faecalis`, `Enterococcus avium`,
`Escherichia coli`, `Klebsiella pneumoniae`, `Citrobacter freundii`, `Proteus mirabilis`)) %>%
pivot_longer(-c(enterococcus_infection, enterobacterales_infection), names_to = "variable", values_to = "value") %>%
pivot_longer(-c(variable, value), names_to = "infection_type", values_to = "infection") %>%
mutate(variable = paste0("Input: ", variable, "\nPredict: ", infection_type)) %>%
group_by(infection_type, variable) %>%
group_map(~ safe_cutpointr(., value, infection, variable,
method = maximize_metric,
metric = youden,
pos_class = 1,
boot_runs = 100,
allowParallel = TRUE,
na.rm = T), .keep = TRUE)
# to get cutpoint object
test_abundance[4][[1]] # ecoc rel abd, ecoc infection
test_abundance[1][[1]] # ebac rel abd, ebacc infection
test_abundance_unnest <-
test_abundance %>%
map_df(as_tibble)
# obtain threshold cutpoints for relative abundance
optimal_cutpoint_rel <-
test_abundance_unnest %>%
separate(subgroup, into = c("Input", "Predict"), sep = "\n", remove = F) %>%
filter(grepl("enterobacterales", Input) & grepl("enterobacterales", Predict)|
grepl("enterococcus", Input) & grepl("enterococcus", Predict)) %>%
group_by(pos_class) %>%
filter(grepl("rel", subgroup)) %>%
select(subgroup, optimal_cutpoint)
```
### Figure 5B: Enterococcus Infection and ROC
```{r, figure-5b, fig.dim = c(16, 12), message=FALSE, warning=FALSE}
# Expansions and Infections Stats
expan_infx_stats <-
peri_matrix_all %>%
left_join(peri_matrix_all %>% select(sampleID, ends_with("infection")) %>% ungroup()) %>%
mutate(ecoc_infx = enterococcus_infection,
ecoc_infx = ifelse(ecoc_infx >= 1, 1, 0),
ebac_infx = enterobacterales_infection,
ebac_infx = ifelse(ebac_infx >= 1, 1, 0)) %>%
select(enterococcus_rel_abundance, enterobacterales_rel_abundance, ecoc_infx, ebac_infx) %>%
summarise(ecoc_expan_above_cutpoint = sum(enterococcus_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[2]),
ecoc_infx_ecoc_below_cutpoint = sum(ecoc_infx == 1 & enterococcus_rel_abundance < optimal_cutpoint_rel$optimal_cutpoint[2], na.rm=T),
ecoc_infx_ecoc_above_cutpoint = sum(ecoc_infx == 1 & enterococcus_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[2], na.rm=T),
ecoc_noinfx_ecoc_below_cutpoint = sum(ecoc_infx == 0 & enterococcus_rel_abundance < optimal_cutpoint_rel$optimal_cutpoint[2], na.rm=T),
ecoc_noinfx_ecoc_above_cutpoint = sum(ecoc_infx == 0 & enterococcus_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[2], na.rm=T),
ebac_expan_above_cutpoint = sum(enterobacterales_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[1]),
ebac_infx_ebac_below_cutpoint = sum(ebac_infx == 1 & enterobacterales_rel_abundance < optimal_cutpoint_rel$optimal_cutpoint[1], na.rm=T),
ebac_infx_ebac_above_cutpoint = sum(ebac_infx == 1 & enterobacterales_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[1], na.rm=T),
ebac_noinfx_ebac_below_cutpoint = sum(ebac_infx == 0 & enterobacterales_rel_abundance < optimal_cutpoint_rel$optimal_cutpoint[1], na.rm=T),
ebac_noinfx_ebac_above_cutpoint = sum(ebac_infx == 0 & enterobacterales_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[1], na.rm=T)) %>%
rownames_to_column(var = "rowname") %>%
pivot_longer(-rowname, names_to = "metric", values_to = "count") %>%
mutate(percent = round((count / 107) * 100, 2)) %>%
select(-rowname)
ecoc_infx_confusion <- expan_infx_stats %>%
filter(metric %in% c(
"ecoc_infx_ecoc_below_cutpoint",
"ecoc_infx_ecoc_above_cutpoint",
"ecoc_noinfx_ecoc_below_cutpoint",
"ecoc_noinfx_ecoc_above_cutpoint"
)) %>%
select(metric, count) %>%
pivot_wider(names_from = metric, values_from = count)
ecoc_infx_confusion_cnfs <- data.frame("Enterococcus" = c("Infection", "No Infection"),
"Expansion" = c(ecoc_infx_confusion$ecoc_infx_ecoc_above_cutpoint,
ecoc_infx_confusion$ecoc_noinfx_ecoc_above_cutpoint),
"No Expansion" = c(ecoc_infx_confusion$ecoc_infx_ecoc_below_cutpoint,
ecoc_infx_confusion$ecoc_noinfx_ecoc_below_cutpoint))
ecoc_infx_confusion_cnfs <- ecoc_infx_confusion_cnfs %>%
mutate(
sensitivity = round(
ecoc_infx_confusion_cnfs[1, 2] / (ecoc_infx_confusion_cnfs[1, 2] + ecoc_infx_confusion_cnfs[1, 3]),
3
),
specificity = round(
ecoc_infx_confusion_cnfs[2, 3] / (ecoc_infx_confusion_cnfs[2, 3] + ecoc_infx_confusion_cnfs[2, 2]),
3
),
odds_ratio = round((ecoc_infx_confusion_cnfs[1, 2] / ecoc_infx_confusion_cnfs[1, 3]) /
(ecoc_infx_confusion_cnfs[2, 2] / ecoc_infx_confusion_cnfs[2, 3]),
3
)
)
fig_5b <-
test_abundance_unnest %>%
separate(subgroup, into = c("Input", "Predict"), sep = "\n", remove = F) %>%
filter(grepl("enterobacterales", Input) & grepl("enterobacterales", Predict)|
grepl("enterococcus", Input) & grepl("enterococcus", Predict)) %>%
group_by(pos_class) %>%
ungroup() %>%
unnest(roc_curve) %>%
arrange(desc(AUC)) %>%
select(-boot) %>%
mutate(
auc_label = paste0("AUC = ", formatC(round(AUC, 3), digits = 2, format = "f")),
auc_label = case_when(
grepl("enterococcus_rel_abundance", Input) ~ paste0(
auc_label,
" [",
formatC(pull(round(boot_ci(x = test_abundance[4][[1]], AUC, alpha = 0.05)[3][1, ], 2)), digits = 2, format = "f"),
", ",
formatC(pull(round(boot_ci(x = test_abundance[4][[1]], AUC, alpha = 0.05)[3][2, ], 2)), digits = 2, format = "f"),
"]"
),
grepl("enterobacterales_rel_abundance", Input) ~ paste0(
auc_label,
" [",
formatC(pull(round(boot_ci(x = test_abundance[1][[1]], AUC, alpha = 0.05)[3][1, ], 2)), digits = 2, format = "f"),
", ",
formatC(pull(round(boot_ci(x = test_abundance[1][[1]], AUC, alpha = 0.05)[3][2, ], 2)), digits = 2, format = "f"),
"]"
)
),
acc_label = paste0("Accuracy = ", formatC(round(acc, 3)*100, digits = 0, format = "f"), "%"),
acc_label = case_when(
grepl("enterococcus_rel_abundance", Input) ~ paste0(
acc_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], acc, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], acc, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
),
grepl("enterobacterales_rel_abundance", Input) ~ paste0(
acc_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], acc, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], acc, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
)
),
sens_label = paste0("Sensitivity = ", formatC(round(sensitivity, 3)*100, digits =0, format = "f"), "%"),
sens_label = case_when(
grepl("enterococcus_rel_abundance", Input) ~ paste0(
sens_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], sensitivity, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], sensitivity, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
),
grepl("enterobacterales_rel_abundance", Input) ~ paste0(
sens_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], sensitivity, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], sensitivity, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
)
),
spec_label = paste0("Specificity = ", formatC(round(specificity, 3)*100, digits = 0, format = "f"), "%"),
spec_label = case_when(
grepl("enterococcus_rel_abundance", Input) ~ paste0(
spec_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], specificity, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], specificity, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
),
grepl("enterobacterales_rel_abundance", Input) ~ paste0(
spec_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], specificity, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], specificity, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
)
)
) %>%
filter(grepl(pattern = "rel_abundance", x = Input)) %>%
mutate(subgroup = str_to_title(subgroup),
subgroup = gsub(pattern = "_", replacement = " ", x = subgroup),
subgroup = gsub(pattern = "rel abundance", replacement = "Relative Abundance (%)", x = subgroup),
subgroup2 = ifelse(grepl(x = subgroup, pattern = "enterococcus", ignore.case = TRUE), "Enterococcus", "Enterobacterales"),
subgroup2 = factor(subgroup2, levels = c("Enterococcus", "Enterobacterales")),
odds_label = paste0("OR = ", ecoc_infx_confusion_cnfs$odds_ratio[1])) %>%
filter(subgroup2 == "Enterococcus") %>%
ggplot(aes(x = fpr, y = tpr, color = subgroup2)) +
geom_line(size = 1.2) +
geom_text(aes(label = auc_label, x = 0.45, y = 0.16), show.legend = F, hjust = 0, size = 6) +
geom_text(aes(label = acc_label, x = 0.45, y = 0.12), show.legend = F, hjust = 0, size = 6) +
geom_text(aes(label = spec_label, x = 0.45, y = 0.08), show.legend = F, hjust = 0, size = 6) +
geom_text(aes(label = sens_label, x = 0.45, y = 0.04), show.legend = F, hjust = 0, size = 6) +
geom_text(aes(label = paste0("Cutpoint = ", round((optimal_cutpoint), digits = 3)*100, "%"), x = 0.45, y = 0.2), hjust = 0, size = 6, show.legend = F) +
geom_text(aes(label = odds_label, x = 0.45, y = 0), show.legend = F, hjust = 0, size = 6)+
theme_bw()+
theme(
axis.text = et(color = "black", size = 12),
axis.title = et(color = "black", size = 14),
legend.text = et(size = 12),
legend.title = et(size = 14),
legend.spacing.y = unit(0.5, 'cm'),
legend.position = "none",
panel.grid = eb(),
strip.text = et(size = 18, color = "#2dc46b", face = "bold"),
strip.background = eb(),
panel.border = eb(),
panel.spacing.y = unit(20, "mm"),
axis.line.x = el(color = "black")
) +
geom_vline(xintercept = -0.05) +
geom_hline(yintercept = -0.05) +
geom_table_npc(
data = ecoc_infx_confusion_cnfs %>%
column_to_rownames(var = "Enterococcus") %>%
select(Expansion, `No Expansion` = No.Expansion) %>%
mutate(Total = rowSums(.)),
label = list(ecoc_infx_confusion_cnfs %>%
column_to_rownames(var = "Enterococcus") %>%
select(Expansion, `No Expansion` = No.Expansion) %>%
mutate(Total = rowSums(.))),
npcx = 0.15,
npcy = 0.6,
hjust = 0,
vjust = 1,
table.rownames = TRUE,
table.theme = ttheme_minimal(
base_size = 16,
core = list(
bg_params = list(col = "#2dc46b"),
fg_params = list(col = "#2dc46b")
),
colhead = list(fg_params = list(
col = "#2dc46b", fontface = "bold"
)),
rowhead = list(fg_params = list(
col = "#2dc46b", fontface = "bold"
))
)
)+
scale_x_continuous(expand = expansion(add = c(0.001,0.05))) +
scale_y_continuous(expand = expansion(add = c(0.001,0.05))) +
facet_wrap(~subgroup2, ncol = 1, scales = "fixed") +
ylab("True Positive Rate\n") +
xlab("\nFalse Positive Rate") +
scale_color_manual(values = c("#2dc46b")) +
guides(color = guide_legend("Groups",
byrow = T,
override.aes = list(size = 5)))
fig_5b
```
### Figure 5D: Enterococcus Infection and ROC
```{r, figure-5d, fig.dim = c(16, 12), message=FALSE, warning=FALSE}
ebac_infx_confusion <- expan_infx_stats %>%
filter(metric %in% c(
"ebac_infx_ebac_below_cutpoint",
"ebac_infx_ebac_above_cutpoint",
"ebac_noinfx_ebac_below_cutpoint",
"ebac_noinfx_ebac_above_cutpoint"
)) %>%
select(metric, count) %>%
pivot_wider(names_from = metric, values_from = count)
ebac_infx_confusion_cnfs <- data.frame("Enterobacterales" = c("Infection", "No Infection"),
"Expansion" = c(ebac_infx_confusion$ebac_infx_ebac_above_cutpoint,
ebac_infx_confusion$ebac_noinfx_ebac_above_cutpoint),
"No Expansion" = c(ebac_infx_confusion$ebac_infx_ebac_below_cutpoint,
ebac_infx_confusion$ebac_noinfx_ebac_below_cutpoint))
ebac_infx_confusion_cnfs <- ebac_infx_confusion_cnfs %>%
mutate(sensitivity = round(ebac_infx_confusion_cnfs[1,2]/(ebac_infx_confusion_cnfs[1,2] + ebac_infx_confusion_cnfs[1,3]), 3),
specificity = round(ebac_infx_confusion_cnfs[2,3]/(ebac_infx_confusion_cnfs[2,3] + ebac_infx_confusion_cnfs[2,2]), 3),
odds_ratio = round((ebac_infx_confusion_cnfs[1,2]/ebac_infx_confusion_cnfs[1,3])/(ebac_infx_confusion_cnfs[2,2]/ebac_infx_confusion_cnfs[2,3]), 3))
fig_5d <-
test_abundance_unnest %>%
separate(subgroup, into = c("Input", "Predict"), sep = "\n", remove = F) %>%
filter(grepl("enterobacterales", Input) & grepl("enterobacterales", Predict)|
grepl("enterococcus", Input) & grepl("enterococcus", Predict)) %>%
group_by(pos_class) %>%
ungroup() %>%
unnest(roc_curve) %>%
arrange(desc(AUC)) %>%
select(-boot) %>%
mutate(
auc_label = paste0("AUC = ", formatC(round(AUC, 3), digits = 2, format = "f")),
auc_label = case_when(
grepl("enterococcus_rel_abundance", Input) ~ paste0(
auc_label,
" [",
formatC(pull(round(boot_ci(x = test_abundance[4][[1]], AUC, alpha = 0.05)[3][1, ], 2)), digits = 2, format = "f"),
", ",
formatC(pull(round(boot_ci(x = test_abundance[4][[1]], AUC, alpha = 0.05)[3][2, ], 2)), digits = 2, format = "f"),
"]"
),
grepl("enterobacterales_rel_abundance", Input) ~ paste0(
auc_label,
" [",
formatC(pull(round(boot_ci(x = test_abundance[1][[1]], AUC, alpha = 0.05)[3][1, ], 2)), digits = 2, format = "f"),
", ",
formatC(pull(round(boot_ci(x = test_abundance[1][[1]], AUC, alpha = 0.05)[3][2, ], 2)), digits = 2, format = "f"),
"]"
)
),
acc_label = paste0("Accuracy = ", formatC(round(acc, 3)*100, digits = 0, format = "f"), "%"),
acc_label = case_when(
grepl("enterococcus_rel_abundance", Input) ~ paste0(
acc_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], acc, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], acc, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
),
grepl("enterobacterales_rel_abundance", Input) ~ paste0(
acc_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], acc, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], acc, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
)
),
sens_label = paste0("Sensitivity = ", formatC(round(sensitivity, 3)*100, digits =0, format = "f"), "%"),
sens_label = case_when(
grepl("enterococcus_rel_abundance", Input) ~ paste0(
sens_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], sensitivity, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], sensitivity, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
),
grepl("enterobacterales_rel_abundance", Input) ~ paste0(
sens_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], sensitivity, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], sensitivity, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
)
),
spec_label = paste0("Specificity = ", formatC(round(specificity, 3)*100, digits = 0, format = "f"), "%"),
spec_label = case_when(
grepl("enterococcus_rel_abundance", Input) ~ paste0(
spec_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], specificity, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[4][[1]], specificity, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
),
grepl("enterobacterales_rel_abundance", Input) ~ paste0(
spec_label,
" [",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], specificity, alpha = 0.05)[3][1, ], 2))*100, digits = 0, format = "f"), "%"),
", ",
paste0(formatC(pull(round(boot_ci(x = test_abundance[1][[1]], specificity, alpha = 0.05)[3][2, ], 2))*100, digits = 0, format = "f"), "%"),
"]"
)
)
) %>%
filter(grepl(pattern = "rel_abundance", x = Input)) %>%
mutate(subgroup = str_to_title(subgroup),
subgroup = gsub(pattern = "_", replacement = " ", x = subgroup),
subgroup = gsub(pattern = "rel abundance", replacement = "Relative Abundance (%)", x = subgroup),
subgroup2 = ifelse(grepl(x = subgroup, pattern = "enterococcus", ignore.case = TRUE), "Enterococcus", "Enterobacterales"),
subgroup2 = factor(subgroup2, levels = c("Enterococcus", "Enterobacterales")),
odds_label = paste0("OR = ", ebac_infx_confusion_cnfs$odds_ratio[1])) %>%
filter(subgroup2 == "Enterobacterales") %>%
ggplot(aes(x = fpr, y = tpr, color = subgroup2)) +
geom_line(size = 1.2) +
geom_text(aes(label = auc_label, x = 0.45, y = 0.16), show.legend = F, hjust = 0, size = 6) +
geom_text(aes(label = acc_label, x = 0.45, y = 0.12), show.legend = F, hjust = 0, size = 6) +
geom_text(aes(label = spec_label, x = 0.45, y = 0.08), show.legend = F, hjust = 0, size = 6) +
geom_text(aes(label = sens_label, x = 0.45, y = 0.04), show.legend = F, hjust = 0, size = 6) +
geom_text(aes(label = paste0("Cutpoint = ", round((optimal_cutpoint), digits = 3)*100, "%"), x = 0.45, y = 0.20), hjust = 0, size = 6, show.legend = F) +
geom_text(aes(label = odds_label, x = 0.45, y = 0), show.legend = F, hjust = 0, size = 6)+
theme_bw()+
theme(
axis.text = et(color = "black", size = 12),
axis.title = et(color = "black", size = 14),
legend.text = et(size = 12),
legend.title = et(size = 14),
legend.spacing.y = unit(0.5, 'cm'),
legend.position = "none",
panel.grid = eb(),
strip.text = et(size = 18, color = "red", face = "bold"),
strip.background = eb(),
panel.border = eb(),
panel.spacing.y = unit(20, "mm"),
axis.line.x = el(color = "black")
) +
geom_vline(xintercept = -0.05) +
geom_hline(yintercept = -0.05) +
geom_table_npc(
data = ebac_infx_confusion_cnfs %>%
column_to_rownames(var = "Enterobacterales") %>%
select(Expansion, `No Expansion` = No.Expansion) %>%
mutate(Total = rowSums(.)),
label = list(ebac_infx_confusion_cnfs %>%
column_to_rownames(var = "Enterobacterales") %>%
select(Expansion, `No Expansion` = No.Expansion) %>%
mutate(Total = rowSums(.))),
npcx = 0.15,
npcy = 0.5,
hjust = 0,
vjust = 1,
table.rownames = TRUE,
table.theme = ttheme_minimal(
base_size = 16,
core = list(
bg_params = list(col = "red"),
fg_params = list(col = "red")
),
colhead = list(fg_params = list(
col = "red", fontface = "bold"
)),
rowhead = list(fg_params = list(
col = "red", fontface = "bold"
))
)
)+
scale_x_continuous(expand = expansion(add = c(0.001,0.05))) +
scale_y_continuous(expand = expansion(add = c(0.001,0.05))) +
facet_wrap(~subgroup2, ncol = 1, scales = "fixed") +
ylab("True Positive Rate\n") +
xlab("\nFalse Positive Rate") +
scale_color_manual(values = c("red")) +
guides(color = guide_legend("Groups",
byrow = T,
override.aes = list(size = 5)))
fig_5d
```
#### Enterococcus Expansion: Volcano Analysis
```{r, volcano-analysis-enterococcus-expansion, fig.dim = c(24, 8),warning=FALSE, message=FALSE}
# Heatmap compounds and their categories
heatmap_lookup <- read.csv("./Data/qual_heatmap_lookup.csv", stringsAsFactors = FALSE)
# Build heatmap compound list
heatmap_cmpds <- metab_qual_anon %>%
mutate(compound = str_to_title(compound),
compound = recode(compound,
Preq1 = "PreQ1")) %>%
filter(compound %in% heatmap_lookup$compound) %>%
distinct(compound) %>%
drop_na()
qual_log2fc_ecoc_expan <- metab_qual_anon %>%
mutate(compound = ifelse(compound == "isovaleric-acid", "isovalerate", compound),
compound = str_to_title(compound),
compound = recode(compound,
Preq1 = "PreQ1")) %>%
filter(compound %in% heatmap_cmpds$compound) %>%
left_join(peri_matrix_all %>% select(sampleID, enterococcus_rel_abundance)) %>%
drop_na() %>%
mutate(enterococcus_expansion = ifelse(enterococcus_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[2], 1, 0)) %>%
arrange(enterococcus_expansion, sampleID) %>%
group_by(compound) %>%
mutate(enterococcus_expansion_0 = length(mvalue[enterococcus_expansion == "0"]),
enterococcus_expansion_1 = length(mvalue[enterococcus_expansion == "1"])) %>%
filter(any(mvalue != 0)) %>%
summarise(log2fc_val = log((mean(mvalue[enterococcus_expansion == "0"], na.rm = T)/ mean(mvalue[enterococcus_expansion == "1"], na.rm = T)), base = 2)) # 0 = No Expansion, 1 = Expansion
qual_pval_ecoc_expan <- metab_qual_anon %>%
mutate(compound = ifelse(compound == "isovaleric-acid", "isovalerate", compound),
compound = str_to_title(compound),
compound = recode(compound,
Preq1 = "PreQ1")) %>%
filter(compound %in% heatmap_cmpds$compound) %>%
left_join(peri_matrix_all %>% select(sampleID, enterococcus_rel_abundance)) %>%
drop_na() %>%
mutate(enterococcus_expansion = ifelse(enterococcus_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[2], 1, 0)) %>%
arrange(enterococcus_expansion, sampleID) %>%
group_by(compound) %>%
mutate(enterococcus_expansion_0 = length(mvalue[enterococcus_expansion == "0"]),
enterococcus_expansion_1 = length(mvalue[enterococcus_expansion == "1"])) %>%
filter(any(mvalue != 0)) %>%
rstatix::wilcox_test(mvalue ~ enterococcus_expansion) %>%
rstatix::adjust_pvalue(method = "BH") %>%
rstatix::add_significance("p.adj")
qual_tot_ecoc_expan <- left_join(qual_log2fc_ecoc_expan, qual_pval_ecoc_expan) %>%
column_to_rownames(var = "compound")
# volcano label color
ecoc_expan_volcano_labcol <- qual_tot_ecoc_expan %>%
filter(p.adj <= 0.05 & abs(log2fc_val) >= 0.75) %>%
mutate(color = ifelse(log2fc_val < 0, "#389458", "gray47"))
# # Volcano Plot (adjusted)
# set.seed(456)
# volcano_adj_ecoc_expan <-
# EnhancedVolcano(qual_tot_ecoc_expan,
# lab = rownames(qual_tot_ecoc_expan),
# x = 'log2fc_val',
# y = 'p.adj',
# title = NULL,
# pCutoff = 0.05,
# FCcutoff = 0.75,
# pointSize = 6,
# labSize = 8,
# axisLabSize = 32,
# labCol = ecoc_expan_volcano_labcol$color,
# caption = NULL,
# colAlpha = 0.65,
# col = c("gray75", c("#D4CA15", "#912777", "#1238E3")),
# xlim = c(-2.5, 4),
# ylim = c(0, 8),
# legendPosition = "none",
# legendLabels = c(expression(p.adj > 0.05*";" ~ Log[2] ~ FC < "\u00B1"*0.75),
# expression(p.adj > 0.05*";" ~ Log[2] ~ FC >= "\u00B1"*0.75),
# expression(p.adj <= 0.05*";" ~ Log[2] ~ FC < "\u00B1"*0.75),
# expression(p.adj <= 0.05*";" ~ Log[2] ~ FC >= "\u00B1"*0.75)),
# legendLabSize = 14,
# boxedLabels = T,
# drawConnectors = T,
# widthConnectors = 0.2,
# arrowheads = F,
# gridlines.minor = F,
# gridlines.major = F,
# max.overlaps = Inf
# ) +
# theme(
# axis.text = et(color = "black"),
# legend.text = et(hjust = 0),
# plot.margin = unit(c(0, 0, 0, 0), "cm")
# ) +
# labs(subtitle = NULL) +
# annotate("segment", x = 0.8, xend = 3, y = 7.95, yend = 7.95,
# arrow = arrow(), size = 2, color = "gray67") +
# annotate("text", x = 1.65, y = 8.15, label = "No Expansion", size = 9, color = "gray67") +
# annotate("rect", xmin = 0.75, xmax = Inf, ymin = -log(0.05, base = 10), ymax = Inf,
# alpha = .1, fill = "gray87") +
# annotate("segment", x = -0.8, xend = -2.5, y = 7.95, yend = 7.95,
# arrow = arrow(), size = 2, color = "#389458") +
# annotate("text", x = -1.55, y = 8.15, label = "Expansion", size = 9, color = "#389458") +
# annotate("rect", xmin = -0.75, xmax = -Inf, ymin = -log(0.05, base = 10), ymax = Inf,
# alpha = .1, fill = "#389458") +
# guides(color = guide_legend(nrow = 4),
# shape = guide_legend(nrow = 4)) +
# scale_y_continuous(expand = expansion(add = c(0, 0.15)))
#
# volcano_adj_ecoc_expan
#
# ggsave(plot = volcano_adj_ecoc_expan, filename = "./Results/Figure_5A.pdf", width = 24, height = 11)
```
#### Enterococcus Expansion: Distribution
```{r, qual-npa-enterococcus-expansion-distribution, fig.dim = c(16, 12),warning=FALSE, message=FALSE}
# Using unadjusted p-values for down-selection of metabolites, show distribution of normalized peak area
boxplot_ecoc_expan <- metab_qual_anon %>%
mutate(compound = ifelse(compound == "isovaleric-acid", "isovalerate", compound),
compound = str_to_title(compound),
compound = recode(compound,
Preq1 = "PreQ1")) %>%
filter(compound %in% heatmap_cmpds$compound) %>%
left_join(peri_matrix_all %>% select(sampleID, enterococcus_rel_abundance)) %>%
drop_na() %>%
mutate(enterococcus_expansion = ifelse(enterococcus_rel_abundance >= optimal_cutpoint_rel$optimal_cutpoint[2], 1, 0),
enterococcus_expansion = ifelse(enterococcus_expansion == 1, "Expansion", "No Expansion")) %>%
arrange(enterococcus_expansion, sampleID) %>%
group_by(compound) %>%
mutate(enterococcus_expansion_0 = length(mvalue[enterococcus_expansion == "No Expansion"]),
enterococcus_expansion_1 = length(mvalue[enterococcus_expansion == "Expansion"])) %>%
filter(any(mvalue != 0)) %>%
right_join(qual_tot_ecoc_expan %>%
rownames_to_column(var = "compound") %>%
mutate(abs_log2fc_val = abs(log2fc_val)) %>%
filter(p <= 0.05, abs_log2fc_val >= 1))
if (nrow(boxplot_ecoc_expan) > 0){
print(ggpar(
ggboxplot(
boxplot_ecoc_expan,
x = "enterococcus_expansion",
y = "mvalue",
color = "enterococcus_expansion",
palette = c("#389458","gray87"),
ylab = "Normalized Peak Area",
xlab = "",
outlier.shape = NA
),
legend.title = "Enterococcus"
) +
stat_compare_means(label.y.npc = 0.75) +
facet_wrap(~compound, scales = "free_y") +
geom_point(data = boxplot_ecoc_expan, aes(x = enterococcus_expansion, y = mvalue, color = enterococcus_expansion), position = position_jitter(width = 0.2),
size = 2, alpha = 0.65))
ggsave(filename = "./Results/enterococcus_expansion_boxplot.pdf", width = 24, height = 18)
} else {
print("no significant observations")
}
```
#### taxUMAP Data Preparation
```{r, umap-pre-taxumap, warning=FALSE, message=FALSE, fig.dim = c(16, 12)}
# Custom colors
pirate_colors <- c("#1A49BE", "#3A001E")
pirate_colors2 <- c("#3A001E", "#3A001E", "#3A001E", "#1A49BE")
t_metaphlan <- metaphlan_df %>%
filter(sampleID %in% first_samps$sampleID| grepl(sampleID, pattern = "hd")) %>%
mutate(db = ifelse(grepl(sampleID, pattern = "lt"), "Liver Transplant", "Healthy Donor")) %>%
select(sampleID, taxid, db, pctseqs, Total) %>%
group_by(sampleID, taxid, pctseqs) %>%
slice(1) %>%
ungroup() %>%
filter(pctseqs >= 0.0001) %>%
group_by(sampleID) %>%
dplyr::add_count(taxid, name = "totalSp") %>%
mutate(sampleID_count = length(unique(sampleID)),
spPres = totalSp/sampleID_count) %>%
filter(spPres >= 0.10) %>%
select(-c(Total, sampleID_count, spPres, totalSp)) %>%
group_by(sampleID) %>%
mutate(pctseqs = pctseqs/sum(pctseqs))
t_metaphlan_mat <- t_metaphlan %>%
distinct() %>%
pivot_wider(names_from = "taxid", values_from = "pctseqs", values_fill = 0) %>%
column_to_rownames(var = "sampleID") %>%
select(-db)
# taxUMAP microbiota table
taxumap_microbiota <- t_metaphlan_mat %>%
rownames_to_column(var = "index_column") %>%
pivot_longer(-index_column, names_to = "taxid", values_to = "pctseq") %>%
mutate(taxid = paste0("taxID", taxid)) %>%
pivot_wider(names_from = "taxid", values_from = "pctseq")
write.csv(taxumap_microbiota, "./Results/microbiota_table.csv", row.names = FALSE)
# taxUMAP taxonomy table
taxumap_taxonomy <- t_metaphlan_mat %>%
rownames_to_column(var = "index_column") %>%
pivot_longer(-index_column, names_to = "taxid", values_to = "pctseq") %>%
left_join(tax_lookup %>% mutate(taxid = as.character(taxid))) %>%
mutate(taxid = paste0("taxID", taxid)) %>%
distinct(Kingdom, Phylum, Class, Order,
Family, Genus, Species, taxid) %>%
transmute(OTU = taxid,
Kingdom,
Phylum = if_else(Phylum == "", "unclassified", Phylum),
Class = if_else(Class == "", "unclassified", Class),
Order = if_else(Order == "", "unclassified", Order),
Family = if_else(Family == "", "unclassified", Family),
Genus = if_else(Genus == "", "unclassified", Genus),
Genus = gsub(" ","_", Genus),
Species = if_else(Species == "", "unclassified",Species),
Species = gsub(" ","_", Species)
)
write.csv(taxumap_taxonomy, "./Results/taxonomy.csv", row.names = FALSE)
```
#### TaxUMAP Analysis
```{python, warning=FALSE, message=FALSE}
import sys
print(sys.path)
from taxumap.taxumap_base import Taxumap
# From file
tu = Taxumap(taxonomy='./Results/taxonomy.csv', microbiota_data='./Results/microbiota_table.csv',random_state=456)
# Transform the data (an inplace function)
tu.transform_self(neigh=55)
# Raw embedding dataframe
embedding_df = tu.df_embedding
```
### Figure 1: Shotgun Metagenomics, Bile Acid Genes, taxUMAP and Diversity
```{r, figure-1-relative-abundance-umap-alpha-diversity, fig.dim = c(18, 14), warning=FALSE, message=FALSE}
#### Alpha Diversity ####
# Alpha diversity matrix: Inverse Simpson
alpha_invsim <- vegan::diversity(t_metaphlan_mat, index = "invsimpson") %>%
as.data.frame() %>%
rownames_to_column(var = "sampleID") %>%
dplyr::rename("InvSimpson" = ".")
# Alpha Diversity matrix: Shannon
alpha_shannon <- vegan::diversity(t_metaphlan_mat, index = "shannon") %>%
as.data.frame() %>%
rownames_to_column(var = "sampleID") %>%
dplyr::rename("Shannon" = ".")
# Alpha Diversity matrix: Observed ASVs
alpha_richness <- vegan::specnumber(t_metaphlan_mat) %>%
as.data.frame() %>%
rownames_to_column(var = "sampleID") %>%
dplyr::rename("Richness" = ".")
#### taxUMAP #####
# Find most abundant taxa per sample and plot just that
top_tax <- t_metaphlan %>%
group_by(sampleID) %>%
slice_max(pctseqs, n = 1) %>%
left_join(tax_lookup) %>%
mutate(across(everything(), ~ifelse(.=="", NA, as.character(.)))) %>%
replace_na(list(Species="unclassified",
Genus="unclassified",
Family="unclassified",
Order="unclassified",
Class="unclassified",
Phylum="unclassified")) %>%
mutate(Genus=ifelse(Genus=="unclassified",
paste(Family,Genus,sep="\n"),
as.character(Genus)),
pctseqs = as.numeric(pctseqs))
metaphlan_df2 <- t_metaphlan %>%
mutate(db = factor(db, levels = c("Liver Transplant", "Healthy Donor"))) %>%
left_join(tax_lookup) %>%
drop_na(taxid) %>%
arrange(Kingdom, Phylum, Class, Order, Family, Genus) %>%
mutate(Genus = paste0(Phylum,"-",Order,"-", Family, "-",Genus)) %>%
left_join(alpha_shannon) %>%
group_by(sampleID) %>%
arrange(Genus) %>%
mutate(cum.pct = cumsum(pctseqs),
y.text = (cum.pct + c(0, cum.pct[-length(cum.pct)]))/2) %>%
ungroup() %>%
dplyr::select(-cum.pct)
metaphlan_df_sumry <-
metaphlan_df2 %>%
group_by(sampleID) %>%
dplyr::slice(1) %>%
ungroup() %>%
mutate(healthy_min_shannon = min(Shannon[db == "Healthy Donor"]),
diversity_group = case_when(
db == "Healthy Donor" ~ "Healthy Donor",
Shannon >= healthy_min_shannon ~ "High Diversity",
TRUE ~ "TBD"
),
lt_med_shannon = median(Shannon[diversity_group == "TBD"], na.rm = TRUE),
diversity_group = case_when(
diversity_group %in% c("Healthy Donor", "High Diversity") ~ diversity_group,
Shannon >= lt_med_shannon ~ "Medium Diversity",
TRUE ~ "Low Diversity"
),
diversity_group = factor(
diversity_group,
levels = c(