trans_metab: Create 'trans_metab' object for metabolite analysis.
In microeco: Microbial Community Ecology Data Analysis
trans_metab R Documentation
Create trans_metab object for metabolite analysis.
Description
This class is a wrapper for a series of metabolite analysis, including origin inference and pathway enrichment.
Methods
Public methods
-
-
-
-
-
-
-
-
-
Method new()
Create the trans_metab object.
Usage
trans_metab$new(metab = NULL, microb = NULL)
Arguments
metabdefault NULL; metabolite data. A microtable object or data.frame object.
If the input is a data.frame object, the function can judge whether it is abundance table and preprocess the data.
microbdefault NULL; A microtable object.
Returns
data_metab and data_microb stored in the object.
Examples
\donttest{
data(soil_metab)
data(soil_microb)
t1 <- trans_metab$new(metab = soil_metab, microb = soil_microb)
}
Method cal_match()
Matching compound names against the database from TidyMass2 (DOI: 10.1038/s41467-026-68464-7) to facilitate the acquisition of standardized nomenclature.
An alternative way is to use MetaboAnalyst website (https://www.metaboanalyst.ca/faces/upload/ConvertView.xhtml).
Usage
trans_metab$cal_match(
database_path = "./metorigindb_split_202602",
method = "jw",
maxDist = 0.3,
...
)
Arguments
database_pathdefault "./metorigindb_split_202602"; directory path of the downloaded database.
Please download the pre-collated metorigindb database (RData format) from zenodo (https://zenodo.org/records/18618912) and extract the compressed archive.
methoddefault "jw"; method of approximate string matching. Default "jw" is Jaro-Winkler distance.
See the method parameter of amatch function in stringdist package.
maxDistdefault 0.3; See the maxDist parameter of amatch function in stringdist package.
...parameters passed to amatch function of stringdist package.
Returns
res_match table stored in the object.
Examples
\dontrun{
t1$cal_match()
}
Method cal_origin()
Metabolite origin inference based on the preprocessed database from TidyMass2 (DOI: 10.1038/s41467-026-68464-7)
Usage
trans_metab$cal_origin(
database_path = "./metorigindb_split_202602",
match_col = "names",
match_names_distance = 0,
bac_level = "Genus"
)
Arguments
database_pathdefault "./metorigindb_split_202602"; directory path of the downloaded database.
Please download the pre-collated metorigindb database (RData format) from zenodo (https://zenodo.org/records/18618912) and extract the compressed archive.
match_coldefault "names"; How to match to the data of metorigindb. Default "names" means using the input names of metabolites.
If the tax_table has other columns like "HMDB_ID" or "KEGG_ID", the user can provide more items, like c("names", "HMDB_ID").
The program will automatically identify the corresponding columns from the input data based on keywords such as HMDB or KEGG, and then match them with the database.
match_names_distancedefault 0; distance threshold used if the res_match_table is found in the object,
which is calculated from the cal_match function. Available for the "names" option in match_col parameter.
bac_leveldefault "Genus"; which bacteria level is used to parse the taxa in the data_microb of object.
The function can automatically match the taxa those found in the input data.
Returns
res_origin_rawtable table and res_origin_list list stored in the object.
res_origin_rawtable is the origin table extracted from the metorigindb database based on the match of name or other ID.
In res_origin_list, name is the metabolite; each element is the taxa that may produce the metabolite.
Examples
\dontrun{
t1$cal_origin()
t1$cal_origin(match_col = c("names", "HMDB_ID", "KEGG_ID"))
}
Method cal_origin_network()
Metabolite-bacteria network based on the res_origin_list data from the cal_origin function
Usage
trans_metab$cal_origin_network()
Returns
igraph format network.
Examples
\dontrun{
t1$cal_origin_network()
}
Method cal_pathway()
Map metabolites to pathways based on a database (KEGG, MetaCyc, Reactome, or custom).
Usage
trans_metab$cal_pathway(
db_type = c("kegg", "metacyc", "reactome", "custom")[1],
db_path = NULL,
id_type = NULL,
save_db = FALSE
)
Arguments
db_typedefault "kegg"; pathway database type, one of "kegg", "metacyc", "reactome", "custom".
db_pathdefault NULL; pathway database file path (RData format).
For KEGG: if NULL, will attempt to fetch from KEGG REST API.
For MetaCyc/Reactome/Custom: must provide local database file path.
id_typedefault NULL; column name in tax_table containing metabolite IDs.
If NULL, will be automatically set based on db_type: "KEGG_ID" for kegg, "METACYC_ID" for metacyc,
"REACTOME_ID" for reactome, "CUSTOM_ID" for custom.
save_dbdefault FALSE; whether save the fetched database (KEGG) to the local folder.
Returns
res_pathway_map (data.frame) stored in the object.
Contains columns: pathway_id, pathway_name, metab_id, metab_name.
Examples
\dontrun{
# KEGG pathway mapping (online)
t1$cal_pathway()
# KEGG pathway mapping (local database)
t1$cal_pathway(db_type = "kegg", db_path = "kegg_pathway_db.RData")
}
Method cal_pathway_enrich()
Perform pathway enrichment analysis for target metabolites using Fisher's exact test.
Usage
trans_metab$cal_pathway_enrich(
target_metabs = NULL,
background_metabs = NULL,
p_adjust_method = "BH",
p_cutoff = 0.05,
min_metab_count = 3
)
Arguments
target_metabscharacter vector; target metabolite names (row names in tax_table),
e.g., differential metabolites. If NULL, will use all metabolites in the pathway mapping.
background_metabscharacter vector; background metabolite names (row names in tax_table).
If NULL, will use all metabolites in the pathway mapping result.
p_adjust_methoddefault "BH"; p-value adjustment method. See p.adjust function.
p_cutoffdefault 0.05; significance threshold for adjusted p-value.
min_metab_countdefault 3; minimum number of background metabolites in a pathway for enrichment test.
Returns
res_pathway_enrich data.frame stored in the object.
Contains: pathway_id, pathway_name, metab_count, background_count,
enrichment_factor, p_value, p_adjust, significant.
Examples
\dontrun{
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:10]
t1$cal_pathway_enrich(target_metabs = target)
}
Method plot_pathway_enrich()
Visualize pathway enrichment analysis results.
Usage
trans_metab$plot_pathway_enrich(
plot_type = c("bubble", "bar")[1],
top_n = 20,
color_by = "p_adjust",
size_by = "metab_count",
order_by = "p_adjust",
color_gradient_low = "#132B43",
color_gradient_high = "#56B1F7"
)
Arguments
plot_typedefault "bubble"; plot type, "bubble" or "bar".
top_ndefault 20; number of top pathways to display.
color_bydefault "p_adjust"; column name for color mapping.
size_bydefault "metab_count"; column name for size mapping (only for bubble plot).
order_bydefault "p_adjust"; column name for ordering pathways.
color_gradient_lowdefault "#132B43"; color for low values (e.g., high p-value = not significant).
color_gradient_highdefault "#56B1F7"; color for high values (e.g., low p-value = significant).
Returns
ggplot2 object.
Examples
\dontrun{
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:10]
t1$cal_pathway_enrich(target_metabs = target)
t1$plot_pathway_enrich()
}
Method cal_pathway_network()
Build metabolite-pathway network based on pathway mapping and enrichment results.
Usage
trans_metab$cal_pathway_network(
significant_only = TRUE,
p_cutoff = 0.05,
use_enrichment = TRUE
)
Arguments
significant_onlydefault TRUE; whether to use only significant enriched pathways.
p_cutoffdefault 0.05; p-value cutoff for significant pathways (used only when significant_only = TRUE).
use_enrichmentdefault TRUE; whether to require enrichment results. If FALSE, build network from all pathway mappings.
Returns
igraph object representing metabolite-pathway bipartite network.
Examples
\dontrun{
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:50]
t1$cal_pathway_enrich(target_metabs = target)
net <- t1$cal_pathway_network()
}
Method clone()
The objects of this class are cloneable with this method.
Usage
trans_metab$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `trans_metab$new`
## ------------------------------------------------
data(soil_metab)
data(soil_microb)
t1 <- trans_metab$new(metab = soil_metab, microb = soil_microb)
## ------------------------------------------------
## Method `trans_metab$cal_match`
## ------------------------------------------------
## Not run:
t1$cal_match()
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_origin`
## ------------------------------------------------
## Not run:
t1$cal_origin()
t1$cal_origin(match_col = c("names", "HMDB_ID", "KEGG_ID"))
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_origin_network`
## ------------------------------------------------
## Not run:
t1$cal_origin_network()
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_pathway`
## ------------------------------------------------
## Not run:
# KEGG pathway mapping (online)
t1$cal_pathway()
# KEGG pathway mapping (local database)
t1$cal_pathway(db_type = "kegg", db_path = "kegg_pathway_db.RData")
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_pathway_enrich`
## ------------------------------------------------
## Not run:
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:10]
t1$cal_pathway_enrich(target_metabs = target)
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$plot_pathway_enrich`
## ------------------------------------------------
## Not run:
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:10]
t1$cal_pathway_enrich(target_metabs = target)
t1$plot_pathway_enrich()
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_pathway_network`
## ------------------------------------------------
## Not run:
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:50]
t1$cal_pathway_enrich(target_metabs = target)
net <- t1$cal_pathway_network()
## End(Not run)
microeco documentation built on Oct. 20, 2026, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| trans_metab | R Documentation |
Create trans_metab object for metabolite analysis.
Description
This class is a wrapper for a series of metabolite analysis, including origin inference and pathway enrichment.
Methods
Public methods
Method new()
Create the trans_metab object.
Usage
trans_metab$new(metab = NULL, microb = NULL)
Arguments
metabdefault NULL; metabolite data. A
microtableobject or data.frame object. If the input is a data.frame object, the function can judge whether it is abundance table and preprocess the data.microbdefault NULL; A
microtableobject.
Returns
data_metab and data_microb stored in the object.
Examples
\donttest{
data(soil_metab)
data(soil_microb)
t1 <- trans_metab$new(metab = soil_metab, microb = soil_microb)
}
Method cal_match()
Matching compound names against the database from TidyMass2 (DOI: 10.1038/s41467-026-68464-7) to facilitate the acquisition of standardized nomenclature. An alternative way is to use MetaboAnalyst website (https://www.metaboanalyst.ca/faces/upload/ConvertView.xhtml).
Usage
trans_metab$cal_match( database_path = "./metorigindb_split_202602", method = "jw", maxDist = 0.3, ... )
Arguments
database_pathdefault "./metorigindb_split_202602"; directory path of the downloaded database. Please download the pre-collated metorigindb database (RData format) from zenodo (https://zenodo.org/records/18618912) and extract the compressed archive.
methoddefault "jw"; method of approximate string matching. Default "jw" is Jaro-Winkler distance. See the
methodparameter ofamatchfunction in stringdist package.maxDistdefault 0.3; See the
maxDistparameter ofamatchfunction in stringdist package....parameters passed to
amatchfunction of stringdist package.
Returns
res_match table stored in the object.
Examples
\dontrun{
t1$cal_match()
}
Method cal_origin()
Metabolite origin inference based on the preprocessed database from TidyMass2 (DOI: 10.1038/s41467-026-68464-7)
Usage
trans_metab$cal_origin( database_path = "./metorigindb_split_202602", match_col = "names", match_names_distance = 0, bac_level = "Genus" )
Arguments
database_pathdefault "./metorigindb_split_202602"; directory path of the downloaded database. Please download the pre-collated metorigindb database (RData format) from zenodo (https://zenodo.org/records/18618912) and extract the compressed archive.
match_coldefault "names"; How to match to the data of metorigindb. Default "names" means using the input names of metabolites. If the tax_table has other columns like "HMDB_ID" or "KEGG_ID", the user can provide more items, like c("names", "HMDB_ID"). The program will automatically identify the corresponding columns from the input data based on keywords such as HMDB or KEGG, and then match them with the database.
match_names_distancedefault 0; distance threshold used if the
res_match_tableis found in the object, which is calculated from thecal_matchfunction. Available for the "names" option inmatch_colparameter.bac_leveldefault "Genus"; which bacteria level is used to parse the taxa in the
data_microbof object. The function can automatically match the taxa those found in the input data.
Returns
res_origin_rawtable table and res_origin_list list stored in the object.
res_origin_rawtable is the origin table extracted from the metorigindb database based on the match of name or other ID.
In res_origin_list, name is the metabolite; each element is the taxa that may produce the metabolite.
Examples
\dontrun{
t1$cal_origin()
t1$cal_origin(match_col = c("names", "HMDB_ID", "KEGG_ID"))
}
Method cal_origin_network()
Metabolite-bacteria network based on the res_origin_list data from the cal_origin function
Usage
trans_metab$cal_origin_network()
Returns
igraph format network.
Examples
\dontrun{
t1$cal_origin_network()
}
Method cal_pathway()
Map metabolites to pathways based on a database (KEGG, MetaCyc, Reactome, or custom).
Usage
trans_metab$cal_pathway(
db_type = c("kegg", "metacyc", "reactome", "custom")[1],
db_path = NULL,
id_type = NULL,
save_db = FALSE
)Arguments
db_typedefault "kegg"; pathway database type, one of "kegg", "metacyc", "reactome", "custom".
db_pathdefault NULL; pathway database file path (RData format). For KEGG: if NULL, will attempt to fetch from KEGG REST API. For MetaCyc/Reactome/Custom: must provide local database file path.
id_typedefault NULL; column name in tax_table containing metabolite IDs. If NULL, will be automatically set based on db_type: "KEGG_ID" for kegg, "METACYC_ID" for metacyc, "REACTOME_ID" for reactome, "CUSTOM_ID" for custom.
save_dbdefault FALSE; whether save the fetched database (KEGG) to the local folder.
Returns
res_pathway_map (data.frame) stored in the object.
Contains columns: pathway_id, pathway_name, metab_id, metab_name.
Examples
\dontrun{
# KEGG pathway mapping (online)
t1$cal_pathway()
# KEGG pathway mapping (local database)
t1$cal_pathway(db_type = "kegg", db_path = "kegg_pathway_db.RData")
}
Method cal_pathway_enrich()
Perform pathway enrichment analysis for target metabolites using Fisher's exact test.
Usage
trans_metab$cal_pathway_enrich( target_metabs = NULL, background_metabs = NULL, p_adjust_method = "BH", p_cutoff = 0.05, min_metab_count = 3 )
Arguments
target_metabscharacter vector; target metabolite names (row names in tax_table), e.g., differential metabolites. If NULL, will use all metabolites in the pathway mapping.
background_metabscharacter vector; background metabolite names (row names in tax_table). If NULL, will use all metabolites in the pathway mapping result.
p_adjust_methoddefault "BH"; p-value adjustment method. See
p.adjustfunction.p_cutoffdefault 0.05; significance threshold for adjusted p-value.
min_metab_countdefault 3; minimum number of background metabolites in a pathway for enrichment test.
Returns
res_pathway_enrich data.frame stored in the object.
Contains: pathway_id, pathway_name, metab_count, background_count,
enrichment_factor, p_value, p_adjust, significant.
Examples
\dontrun{
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:10]
t1$cal_pathway_enrich(target_metabs = target)
}
Method plot_pathway_enrich()
Visualize pathway enrichment analysis results.
Usage
trans_metab$plot_pathway_enrich(
plot_type = c("bubble", "bar")[1],
top_n = 20,
color_by = "p_adjust",
size_by = "metab_count",
order_by = "p_adjust",
color_gradient_low = "#132B43",
color_gradient_high = "#56B1F7"
)Arguments
plot_typedefault "bubble"; plot type, "bubble" or "bar".
top_ndefault 20; number of top pathways to display.
color_bydefault "p_adjust"; column name for color mapping.
size_bydefault "metab_count"; column name for size mapping (only for bubble plot).
order_bydefault "p_adjust"; column name for ordering pathways.
color_gradient_lowdefault "#132B43"; color for low values (e.g., high p-value = not significant).
color_gradient_highdefault "#56B1F7"; color for high values (e.g., low p-value = significant).
Returns
ggplot2 object.
Examples
\dontrun{
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:10]
t1$cal_pathway_enrich(target_metabs = target)
t1$plot_pathway_enrich()
}
Method cal_pathway_network()
Build metabolite-pathway network based on pathway mapping and enrichment results.
Usage
trans_metab$cal_pathway_network( significant_only = TRUE, p_cutoff = 0.05, use_enrichment = TRUE )
Arguments
significant_onlydefault TRUE; whether to use only significant enriched pathways.
p_cutoffdefault 0.05; p-value cutoff for significant pathways (used only when significant_only = TRUE).
use_enrichmentdefault TRUE; whether to require enrichment results. If FALSE, build network from all pathway mappings.
Returns
igraph object representing metabolite-pathway bipartite network.
Examples
\dontrun{
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:50]
t1$cal_pathway_enrich(target_metabs = target)
net <- t1$cal_pathway_network()
}
Method clone()
The objects of this class are cloneable with this method.
Usage
trans_metab$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `trans_metab$new`
## ------------------------------------------------
data(soil_metab)
data(soil_microb)
t1 <- trans_metab$new(metab = soil_metab, microb = soil_microb)
## ------------------------------------------------
## Method `trans_metab$cal_match`
## ------------------------------------------------
## Not run:
t1$cal_match()
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_origin`
## ------------------------------------------------
## Not run:
t1$cal_origin()
t1$cal_origin(match_col = c("names", "HMDB_ID", "KEGG_ID"))
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_origin_network`
## ------------------------------------------------
## Not run:
t1$cal_origin_network()
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_pathway`
## ------------------------------------------------
## Not run:
# KEGG pathway mapping (online)
t1$cal_pathway()
# KEGG pathway mapping (local database)
t1$cal_pathway(db_type = "kegg", db_path = "kegg_pathway_db.RData")
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_pathway_enrich`
## ------------------------------------------------
## Not run:
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:10]
t1$cal_pathway_enrich(target_metabs = target)
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$plot_pathway_enrich`
## ------------------------------------------------
## Not run:
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:10]
t1$cal_pathway_enrich(target_metabs = target)
t1$plot_pathway_enrich()
## End(Not run)
## ------------------------------------------------
## Method `trans_metab$cal_pathway_network`
## ------------------------------------------------
## Not run:
t1$cal_pathway()
target <- rownames(t1$data_metab$otu_table)[1:50]
t1$cal_pathway_enrich(target_metabs = target)
net <- t1$cal_pathway_network()
## End(Not run)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.