Man pages for metabolomicsR
Tools for Metabolomics Data

assayDataget assayData
assayData_setset assayData
batch_normbatch normalization
bridgebridge different data sets based on conversion factors
column_missing_ratecolumn missing rate
correlationcorrelation of features between two Metabolite objects
create_MetaboliteCreate a Metabolite object
df_plasmaExample data.
featureDataget featureData
featureData_setset featureData
filter_column_constantfilter columns if values are constant
filter_column_missing_ratefilter columns using missing rate
filter_row_missing_ratefilter rows using missing rate
fit_lmavailable regression methods
imputeimpute missing values
inverse_rank_transformrank-based inverse normal transformation
is_outlieris outlier
load_dataLoad metabolite data from three separate files
load_excelLoad metabolite data from an excel file
merge_datamerge two Metabolite objects
Metabolite-classThe Metabolite class
modelling_normLOESS normalization
nearestQC_normnearest QC sample normalization
outlier_rateoutlier rate
pareto_scalepareto scale transformation
plot_injection_orderinjection order scatterplot
plot_Metaboliteplot a Metabolite object
plot_PCAplot PCA
plot_tsneplot tSNE
plot_UMAPPlot UMAP
plot_volcanovolcano plot for regression results
QCmatrix_normQCmatrix normalization
QC_pipelinequality control pipeline
regressionregression analysis
replace_outlierchange outlier values as NA or winsorize
row_missing_raterow missing rate
run_PCAPrincipal Components Analysis
sampleDataget sampleData
sampleData_setset sampleData
save_dataSave metabolite data
show-Metabolite-methodPrint a Metabolite class object
subsetsubset a Metabolite object.
transformationapply transformation to a Metabolite object
update_MetaboliteUpdate a Metabolite object
metabolomicsR documentation built on April 29, 2022, 9:05 a.m.