Function reference
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Metabolite-classMetabolite - The Metabolite class
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Metabolite2maplet() - Convert a Metabolite object to maplet data
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QC_pipeline() - quality control pipeline
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QC_pipeline_df() - quality control pipeline for a simplified data.frame.
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QCmatrix_norm() - QCmatrix normalization
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RSD() - RSD
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anno_hmdb() - Query metabolite ID in HMDB database
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assayData() - get assayData
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`assayData<-`() - set assayData
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batch_norm() - batch normalization
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batch_norm_df() - batch normalization for a data.frame
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bridge() - bridge different data sets based on conversion factors
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column_missing_rate() - column missing rate
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correlation() - correlation of features between two Metabolite objects
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create_Metabolite() - Create a Metabolite object
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df_plasma - Example data.
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featureData() - get featureData
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`featureData<-`() - set featureData
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filter_column_constant()calculate_column_constant() - filter columns if values are constant
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filter_column_missing_rate() - filter columns using missing rate
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filter_row_missing_rate() - filter rows using missing rate
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fit_lm()fit_logistic()fit_poisson()fit_cox()fit_lme()fit_glmer()fit_lmer() - available regression methods
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genuMet_makefeature() - distinguish genuine untargeted metabolic features without QC samples
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impute()impute_kNN() - impute missing values
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inverse_rank_transform() - rank-based inverse normal transformation
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is_outlier() - is outlier
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load_data() - Load metabolite data from three separate files
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load_excel() - Load metabolite data from an excel file
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maplet2Metabolite() - Convert maplet data to a Metabolite object
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merge_data() - merge two Metabolite objects
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modelling_norm() - LOESS normalization
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nearestQC_norm() - nearest QC sample normalization
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outlier_rate() - outlier rate
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pareto_scale() - pareto scale transformation
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plot_Metabolite() - plot a Metabolite object
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plot_PCA() - plot PCA
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plot_QC() - quality control visualization
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plot_ROC() - ROC
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plot_UMAP() - Plot UMAP
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plot_injection_order() - injection order scatterplot
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plot_tsne() - plot tSNE
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plot_volcano() - volcano plot for regression results
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regression()regression_each()regression_each_as_outcome() - regression analysis
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replace_outlier() - change outlier values as NA or winsorize
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row_missing_rate() - row missing rate
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run_PCA() - Principal Components Analysis
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sampleData() - get sampleData
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`sampleData<-`() - set sampleData
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save_data_txt() - Save metabolite data in txt files
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save_data_xls() - Save metabolite data in an Excel file
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show(<Metabolite>) - Print a Metabolite class object
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subset() - subset a Metabolite object.
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transformation() - apply transformation to a Metabolite object
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update_Metabolite() - Update a Metabolite object