Chapter 5 MetOrigin Analysis
Microbiome and its metabolites are closely associated with human health and diseases. However, it is challenging to understand the complex interplay between microbiome and metabolites. MetOrigin is a bioinformatics tool, aiming to identify which bacteria and how they participate in certain metabolic reactions, helping us to understand where metabolites come from: host, bacteria, or both?
The tutorial was from https://metorigin.met-bioinformatics.cn/home/. Here, we gave example for how to prepare inputs to MetOrigin website.
5.2 Importing data
The dataset is from the Zeybel-2022 published paper (Zeybel et al. 2022).
5.3 Differential Analysis
Fold change (group1 vs group2)
- RawData
VIP (Variable influence on projection & coefficient)
Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS)
Variable influence on projection (VIP) for projections to latent structures (PLS)
T-test
- significant differences between two groups (p value)
Merging result
Foldchange by Raw Data
VIP by Normalized Data
test Pvalue by Normalized Data
FoldChange <- function(
x,
group,
group_names) {
# dataseat
metadata <- SummarizedExperiment::colData(x) %>%
as.data.frame()
profile <- SummarizedExperiment::assay(x) %>%
as.data.frame()
colnames(metadata)[which(colnames(metadata) == group)] <- "CompVar"
phenotype <- metadata %>%
dplyr::filter(CompVar %in% group_names) %>%
dplyr::mutate(CompVar = as.character(CompVar)) %>%
dplyr::mutate(CompVar = factor(CompVar, levels = group_names))
sid <- intersect(rownames(phenotype), colnames(profile))
phen <- phenotype[pmatch(sid, rownames(phenotype)), , ]
prof <- profile %>%
dplyr::select(all_of(sid))
if (!all(colnames(prof) == rownames(phen))) {
stop("Wrong Order")
}
fc_res <- apply(prof, 1, function(x1, y1) {
dat <- data.frame(value = as.numeric(x1), group = y1)
mn <- tapply(dat$value, dat$group, function(x){
mean(x, na.rm = TRUE)
}) %>%
as.data.frame() %>%
stats::setNames("value") %>%
tibble::rownames_to_column("Group")
mn1 <- with(mn, mn[Group %in% group_names[1], "value"])
mn2 <- with(mn, mn[Group %in% group_names[2], "value"])
mnall <- mean(dat$value, na.rm = TRUE)
if (all(mn1 != 0, mn2 != 0)) {
fc <- mn1 / mn2
} else {
fc <- NA
}
logfc <- log2(fc)
res <- c(fc, logfc, mnall, mn1, mn2)
return(res)
}, phen$CompVar) %>%
base::t() %>% data.frame() %>%
tibble::rownames_to_column("Feature")
colnames(fc_res) <- c("FeatureID", "FoldChange",
"Log2FoldChange",
"Mean Abundance\n(All)",
paste0("Mean Abundance\n", c("former", "latter")))
# Number of Group
dat_status <- table(phen$CompVar)
dat_status_number <- as.numeric(dat_status)
dat_status_name <- names(dat_status)
fc_res$Block <- paste(paste(dat_status_number[1], dat_status_name[1], sep = "_"),
"vs",
paste(dat_status_number[2], dat_status_name[2], sep = "_"))
res <- fc_res %>%
dplyr::select(FeatureID, Block, everything())
return(res)
}
VIP_fun <- function(
x,
group,
group_names,
VIPtype = c("OPLS", "PLS"),
vip_cutoff = 1) {
metadata <- SummarizedExperiment::colData(x) %>%
as.data.frame()
profile <- SummarizedExperiment::assay(x) %>%
as.data.frame()
colnames(metadata)[which(colnames(metadata) == group)] <- "CompVar"
phenotype <- metadata %>%
dplyr::filter(CompVar %in% group_names) %>%
dplyr::mutate(CompVar = as.character(CompVar)) %>%
dplyr::mutate(CompVar = factor(CompVar, levels = group_names))
sid <- intersect(rownames(phenotype), colnames(profile))
phen <- phenotype[pmatch(sid, rownames(phenotype)), , ]
prof <- profile %>%
dplyr::select(all_of(sid))
if (!all(colnames(prof) == rownames(phen))) {
stop("Wrong Order")
}
dataMatrix <- prof %>% base::t() # row->sampleID; col->features
sampleMetadata <- phen # row->sampleID; col->features
comparsionVn <- sampleMetadata[, "CompVar"]
# corrlation between group and features
pvaVn <- apply(dataMatrix, 2,
function(feaVn) cor.test(as.numeric(comparsionVn), feaVn)[["p.value"]])
if (VIPtype == "OPLS") {
vipVn <- getVipVn(opls(dataMatrix,
comparsionVn,
predI = 1,
orthoI = NA,
fig.pdfC = "none"))
} else {
vipVn <- getVipVn(opls(dataMatrix,
comparsionVn,
predI = 1,
fig.pdfC = "none"))
}
quantVn <- qnorm(1 - pvaVn / 2)
rmsQuantN <- sqrt(mean(quantVn^2))
opar <- par(font = 2, font.axis = 2, font.lab = 2,
las = 1,
mar = c(5.1, 4.6, 4.1, 2.1),
lwd = 2, pch = 16)
plot(pvaVn,
vipVn,
col = "red",
pch = 16,
xlab = "p-value", ylab = "VIP", xaxs = "i", yaxs = "i")
box(lwd = 2)
curve(qnorm(1 - x / 2) / rmsQuantN, 0, 1, add = TRUE, col = "red", lwd = 3)
abline(h = 1, col = "blue")
abline(v = 0.05, col = "blue")
res_temp <- data.frame(
FeatureID = names(vipVn),
VIP = vipVn,
CorPvalue = pvaVn) %>%
dplyr::arrange(desc(VIP))
vip_select <- res_temp %>%
dplyr::filter(VIP > vip_cutoff)
pl <- ggplot(vip_select, aes(FeatureID, VIP)) +
geom_segment(aes(x = FeatureID, xend = FeatureID,
y = 0, yend = VIP)) +
geom_point(shape = 21, size = 5, color = '#008000' ,fill = '#008000') +
geom_point(aes(1,2.5), color = 'white') +
geom_hline(yintercept = 1, linetype = 'dashed') +
scale_y_continuous(expand = c(0, 0)) +
labs(x = '', y = 'VIP value') +
theme_bw() +
theme(legend.position = 'none',
legend.text = element_text(color = 'black',size = 12, family = 'Arial', face = 'plain'),
panel.background = element_blank(),
panel.grid = element_blank(),
axis.text = element_text(color = 'black',size = 15, family = 'Arial', face = 'plain'),
axis.text.x = element_text(angle = 90),
axis.title = element_text(color = 'black',size = 15, family = 'Arial', face = 'plain'),
axis.ticks = element_line(color = 'black'),
axis.ticks.x = element_blank())
# Number of Group
dat_status <- table(phen$CompVar)
dat_status_number <- as.numeric(dat_status)
dat_status_name <- names(dat_status)
res_temp$Block <- paste(paste(dat_status_number[1], dat_status_name[1], sep = "_"),
"vs",
paste(dat_status_number[2], dat_status_name[2], sep = "_"))
res_df <- res_temp %>%
dplyr::select(FeatureID, Block, everything())
res <- list(vip = res_df,
plot = pl)
return(res)
}
t_fun <- function(
x,
group,
group_names) {
# dataseat
metadata <- SummarizedExperiment::colData(x) %>%
as.data.frame()
profile <- SummarizedExperiment::assay(x) %>%
as.data.frame()
# rename variables
colnames(metadata)[which(colnames(metadata) == group)] <- "CompVar"
phenotype <- metadata %>%
dplyr::filter(CompVar %in% group_names) %>%
dplyr::mutate(CompVar = as.character(CompVar)) %>%
dplyr::mutate(CompVar = factor(CompVar, levels = group_names))
sid <- intersect(rownames(phenotype), colnames(profile))
phen <- phenotype[pmatch(sid, rownames(phenotype)), , ]
prof <- profile %>%
dplyr::select(all_of(sid))
if (!all(colnames(prof) == rownames(phen))) {
stop("Wrong Order")
}
t_res <- apply(prof, 1, function(x1, y1) {
dat <- data.frame(value = as.numeric(x1), group = y1)
rest <- t.test(data = dat, value ~ group)
res <- c(rest$statistic, rest$p.value)
return(res)
}, phen$CompVar) %>%
base::t() %>% data.frame() %>%
tibble::rownames_to_column("Feature")
colnames(t_res) <- c("FeatureID", "Statistic", "Pvalue")
t_res$AdjustedPvalue <- p.adjust(as.numeric(t_res$Pvalue), method = "BH")
# Number of Group
dat_status <- table(phen$CompVar)
dat_status_number <- as.numeric(dat_status)
dat_status_name <- names(dat_status)
t_res$Block <- paste(paste(dat_status_number[1], dat_status_name[1], sep = "_"),
"vs",
paste(dat_status_number[2], dat_status_name[2], sep = "_"))
res <- t_res %>%
dplyr::select(FeatureID, Block, everything())
return(res)
}
mergedResults <- function(
fc_result,
vip_result,
test_result,
group_names,
group_labels) {
if (is.null(vip_result)) {
mdat <- fc_result %>%
dplyr::mutate(Block2 = paste(group_labels, collapse = " vs ")) %>%
dplyr::mutate(FeatureID = make.names(FeatureID)) %>%
dplyr::select(-all_of(c("Mean Abundance\n(All)",
"Mean Abundance\nformer",
"Mean Abundance\nlatter"))) %>%
dplyr::inner_join(test_result %>%
dplyr::select(-Block) %>%
dplyr::mutate(FeatureID = make.names(FeatureID)),
by = "FeatureID")
res <- mdat %>%
dplyr::select(FeatureID, Block2, Block,
FoldChange, Log2FoldChange,
Statistic, Pvalue, AdjustedPvalue,
everything()) %>%
dplyr::arrange(AdjustedPvalue, Log2FoldChange)
} else {
mdat <- fc_result %>%
dplyr::mutate(Block2 = paste(group_labels, collapse = " vs ")) %>%
dplyr::mutate(FeatureID = make.names(FeatureID)) %>%
dplyr::select(-all_of(c("Mean Abundance\n(All)",
"Mean Abundance\nformer",
"Mean Abundance\nlatter"))) %>%
dplyr::inner_join(vip_result %>%
dplyr::select(-Block) %>%
dplyr::mutate(FeatureID = make.names(FeatureID)),
by = "FeatureID") %>%
dplyr::inner_join(test_result %>%
dplyr::select(-Block) %>%
dplyr::mutate(FeatureID = make.names(FeatureID)),
by = "FeatureID")
res <- mdat %>%
dplyr::select(FeatureID, Block2, Block,
FoldChange, Log2FoldChange,
VIP, CorPvalue,
Statistic, Pvalue, AdjustedPvalue,
everything()) %>%
dplyr::arrange(AdjustedPvalue, Log2FoldChange)
}
return(res)
}5.4 DE metabolites
fc_res <- FoldChange(
x = se_impute,
group = "group",
group_names = c("Mild", "Moderate"))
vip_res <- VIP_fun(
x = se_norm,
group = "group",
group_names = c("Mild", "Severe"),
VIPtype = "PLS",
vip_cutoff = 1)## PLS-DA
## 26 samples x 167 variables and 1 response
## standard scaling of predictors and response(s)
## R2X(cum) R2Y(cum) Q2(cum) RMSEE pre ort pR2Y pQ2
## Total 0.12 0.692 0.33 0.288 1 0 0.2 0.05
ttest_res <- t_fun(
x = se_norm,
group = "group",
group_names = c("Mild", "Severe"))
m_results <- mergedResults(
fc_result = fc_res,
vip_result = vip_res$vip,
test_result = ttest_res,
group_names = c("Mild", "Severe"),
group_labels = c("Mild", "Severe"))
head(m_results)## FeatureID Block2 Block FoldChange Log2FoldChange VIP CorPvalue Statistic Pvalue AdjustedPvalue
## 1 M_42449 Mild vs Severe 14_Mild vs 19_Moderate 0.6571801 -0.6056393 2.362694 0.002247897 -3.498896 0.001881585 0.2132448
## 2 M_52446 Mild vs Severe 14_Mild vs 19_Moderate 0.7040861 -0.5061763 2.059461 0.009476225 -2.892892 0.008125613 0.2132448
## 3 M_1566 Mild vs Severe 14_Mild vs 19_Moderate 0.7161609 -0.4816443 2.143655 0.006556886 -3.077688 0.005431005 0.2132448
## 4 M_19263 Mild vs Severe 14_Mild vs 19_Moderate 0.7165011 -0.4809592 2.128317 0.007023483 -3.073546 0.005774977 0.2132448
## 5 M_42448 Mild vs Severe 14_Mild vs 19_Moderate 0.7577644 -0.4001788 2.006246 0.011828403 -2.793941 0.010215320 0.2132448
## 6 M_43761 Mild vs Severe 14_Mild vs 19_Moderate 0.8685495 -0.2033201 2.043515 0.010136007 -2.822338 0.009428795 0.21324485.5 Obtain inputs for Metorigin
The metabolite table must contain at least one column of “HMDBID”, “KEGGID” or “Name”, and a column of 0/1 values indicating statistical significance (1-significant, 0-nonsignificant). If the “Diff” column is missing, all metabolites will be considered as differential metabolites.
get_metabolites <- function(
x = ExprSet,
dat,
group_names,
index_names = c("FoldChange", "Log2FoldChange", "VIP", "CorPvalue", "Pvalue", "AdjustedPvalue"),
index_cutoff = c(1, 1, 1, 0.05, 0.05, 0.2)) {
# x = ExprSet
# dat = m_results
# group_names = "Mild vs Severe"
# index_names = c("Log2FoldChange", "AdjustedPvalue")
# index_cutoff = c(0, 0.2)
feature <- Biobase::fData(x)
colnames(feature)[which(colnames(feature) == "SampleID HMDBID")] <- "HMDB"
colnames(feature)[which(colnames(feature) == "KEGG")] <- "cpd_ID"
colnames(feature)[which(colnames(feature) == "BIOCHEMICAL")] <- "Compounds"
feature$HMDB <- gsub(",\\S+", "", feature$HMDB)
temp_dat <- dat %>%
dplyr::filter(Block2 %in% group_names) %>%
dplyr::inner_join(feature %>%
tibble::rownames_to_column("FeatureID"),
by = "FeatureID") %>%
dplyr::filter(HMDB != "-")
colnames(temp_dat)[which(colnames(temp_dat) == index_names[1])] <- "DA_index1"
colnames(temp_dat)[which(colnames(temp_dat) == index_names[2])] <- "DA_index2"
temp_dat_diff <- temp_dat %>%
dplyr::filter(abs(DA_index1) > index_cutoff[1]) %>%
dplyr::filter(DA_index2 < index_cutoff[2]) %>%
dplyr::mutate(Diff = 1)
if (nrow(temp_dat_diff) == 0) {
stop("Beyond these thresholds, no significant metabolites were selected")
}
temp_dat_nodiff <- temp_dat %>%
dplyr::filter(!HMDB %in% temp_dat_diff$HMDB) %>%
dplyr::mutate(Diff = 0)
res <- rbind(temp_dat_diff, temp_dat_nodiff) %>%
dplyr::select(HMDB, cpd_ID, Compounds, DA_index1, DA_index2, Diff) %>%
dplyr::rename(HMDBID = HMDB,
KEGGID = cpd_ID,
Name = Compounds) %>%
dplyr::select(HMDBID, KEGGID, Name, Diff)
return(res)
}
pre_result <- get_metabolites(
x = ExprSet,
dat = m_results,
group_names = "Mild vs Severe",
index_names = c("Log2FoldChange", "AdjustedPvalue"),
index_cutoff = c(0, 1))
head(pre_result)## HMDBID KEGGID Name Diff
## 1 HMDB0005322 <NA> 1-palmitoyl-2-linoleoyl-GPE (16:0/18:2) 1
## 2 HMDB0008994 <NA> 1-stearoyl-2-linoleoyl-GPE (18:0/18:2)* 1
## 3 HMDB0002166 C05145 3-aminoisobutyrate 1
## 4 HMDB0005320 <NA> 1-palmitoyl-2-oleoyl-GPE (16:0/18:1) 1
## 5 HMDB0008993 <NA> 1-stearoyl-2-oleoyl-GPE (18:0/18:1) 1
## 6 HMDB0094649 <NA> 2-aminoheptanoate 15.7 MetOrigin User Tutorial
MetOrigin comprises five parts:
- Load data
Please select the analysis mode at first “Simple MetOrigin Analysis” or “Deep MetOrigin Analysis”. Then you can try “Load Example Data” or upload your own data. Host information needs to be confirmed before moving to the next step.
- Origin Analysis
This step is to identify where metabolites come from: host, bacteria, both, or unknown?
- Function Analysis
This step is to perform the metabolic pathway enrichment analysis according to different categories of metabolites: metabolites belonging to the host, bacteria, or both.
- Sankey Network
This step is to link all the possible bacteria that may participate in a specific metabolic reaction, helping you to identify important interplay between bacteria and metabolites.
- Download Results
All the analysis results can be downloaded for your further data exploration.
See more details please go to the below tutorial (MetOrigin website):
MetOrigin User Tutorial
5.8 Session info
## ─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.3 (2022-03-10)
## os macOS Monterey 12.2.1
## system x86_64, darwin17.0
## ui RStudio
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Shanghai
## date 2023-12-07
## rstudio 2023.09.0+463 Desert Sunflower (desktop)
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [2] CRAN (R 4.1.0)
## ade4 1.7-22 2023-02-06 [2] CRAN (R 4.1.2)
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## Biobase * 2.54.0 2021-10-26 [2] Bioconductor
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##
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## [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
##
## ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────References
Zeybel, Mujdat, Muhammad Arif, Xiangyu Li, Ozlem Altay, Hong Yang, Mengnan Shi, Murat Akyildiz, et al. 2022. “Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis.” Advanced Science 9 (11): 2104373.