Construct MetaboSet objects from input read by
read_from_excel. Returns a list of MetaboSet objects, one per
mode. The modes are separated by the "Split" column in feature data.
Usage
construct_metabosets(
exprs,
pheno_data,
feature_data,
group_col = NA_character_,
time_col = NA_character_,
subject_col = NA_character_,
split_data = TRUE
)Arguments
- exprs
matrix, the feature abundances, size features x samples
- pheno_data
data frame, sample information, size sample info variables x samples
- feature_data
data frame, information about the features, size features x feature info columns
- group_col
character, the name of the column in pheno_data to use as the grouping variable
- time_col
character, the name of the column in pheno_data to use as the time variable
- subject_col
character, the name of the column in pheno_data to use as the subject ID variable
- split_data
logical, whether to split data by analytical mode recorded in the "Split" column of feature data. If TRUE (the default), will return a list of MetaboSet objects, one per analytical mode. If FALSE, will return a single MetaboSet object.
Examples
data <- read_from_excel(
file = system.file("extdata", "example_set.xlsx",
package = "notame"), sheet = 1, corner_row = 11, corner_column = "H",
split_by = c("Column", "Ion_mode"))
#> INFO [2025-06-23 22:36:30] Corner detected correctly at row 11, column H
#> INFO [2025-06-23 22:36:30]
#> Extracting sample information from rows 1 to 11 and columns I to BF
#> INFO [2025-06-23 22:36:30] Replacing spaces in sample information column names with underscores (_)
#> INFO [2025-06-23 22:36:30] Naming the last column of sample information "Datafile"
#> INFO [2025-06-23 22:36:30]
#> Extracting feature information from rows 12 to 91 and columns A to H
#> INFO [2025-06-23 22:36:30]
#> Extracting feature abundances from rows 12 to 91 and columns I to BF
#> INFO [2025-06-23 22:36:30] Pheno data was cleaned
#> INFO [2025-06-23 22:36:30] Feature data was cleaned
#> INFO [2025-06-23 22:36:30]
#> Checking sample information
#> INFO [2025-06-23 22:36:30] Checking 'Injection_order' column in feature data
#> INFO [2025-06-23 22:36:30] Checking 'Sample_ID' column in pheno data
#> INFO [2025-06-23 22:36:30] Checking 'QC' column in feature data
#> INFO [2025-06-23 22:36:30] Checking that feature abundances only contain numeric values
#> INFO [2025-06-23 22:36:30]
#> Checking feature information
#> INFO [2025-06-23 22:36:30] Checking that feature IDs are unique and not storedas numbers
#> INFO [2025-06-23 22:36:30] Checking that m/z and retention time values are reasonable.
#> INFO [2025-06-23 22:36:30] Identified m/z column Average_Mz and retention time column Average_Rt_min
#> INFO [2025-06-23 22:36:30] Identified m/z column Average_Mz and retention time column Average_Rt_min
#> INFO [2025-06-23 22:36:30] Checking that feature data includes a 'Split' column
#> INFO [2025-06-23 22:36:30] Checking that feature data includes a 'Flag' column
modes <- construct_metabosets(exprs = data$exprs,
pheno_data = data$pheno_data, feature_data = data$feature_data,
group_col = "Group")
#> INFO [2025-06-23 22:36:30]
#> Checking feature information
#> INFO [2025-06-23 22:36:30] Checking that feature IDs are unique and not storedas numbers
#> INFO [2025-06-23 22:36:30] Checking that feature data includes a 'Split' column
#> INFO [2025-06-23 22:36:30] Checking that feature data includes a 'Flag' column
#> INFO [2025-06-23 22:36:30] Checking that feature abundances only contain numeric values
#> INFO [2025-06-23 22:36:30] Setting row and column names of exprs based on feature and pheno data