Package index • notame

Tabular data preprocessing

mark_nas(): Mark specified values as missing

flag_detection(): Flag features with low detection rate

flag_contaminants(): Flag contaminants based on blanks

correct_drift(): Correct drift using cubic spline

ruvs_qc(): Remove Unwanted Variation (RUV) between batches

pca_bhattacharyya_dist(): Bhattacharyya distance between batches in PCA space

perform_repeatability(): Compute repeatability measures

assess_quality(): Assess quality information of features

quality(): Extract quality information of features

flag_quality flag_quality(): Flag low-quality features

flag_report(): A report of flagged features

impute_rf(): Impute missing values using random forest

impute_simple(): Simple imputation

cluster_features(): Cluster correlated features originating from the same metabolite

compress_clusters(): Compress clusters of features to a single feature

log(<MetaboSet>) log2(<MetaboSet>) log10(<MetaboSet>) log(<SummarizedExperiment>) log2(<SummarizedExperiment>) log10(<SummarizedExperiment>): Logarithm

exponential(): Exponential function

pqn_normalization(): Probabilistic quotient normalization

inverse_normalize(): Inverse-rank normalization

scale(<MetaboSet>) scale(<SummarizedExperiment>): Scale peak table

Preprocessing visualizations

visualizations(): Write all relevant preprocessing visualizations to pdf

plot_injection_lm(): Estimate the magnitude of drift

plot_sample_boxplots(): Plot a boxplot for each sample

plot_dist_density(): Plot distance density

plot_tsne(): t-SNE scatter plot

plot_pca(): PCA scatter plot

plot_tsne_arrows(): t-SNE plot with arrows

plot_pca_arrows(): PCA plot with arrows

plot_tsne_hexbin(): t-SNE hexbin plot

plot_pca_hexbin(): PCA hexbin plot

plot_dendrogram(): Sample dendrogram

plot_sample_heatmap(): Sample heatmap

plot_pca_loadings(): PCA loadings plot

plot_quality(): Plot quality metrics

save_batch_plots(): Save batch correction plots

Feature selection

Univariate analysis

perform_lm(): Linear models

perform_logistic(): Logistic regression

perform_lmer(): Linear mixed models

perform_oneway_anova(): Welch's ANOVA and classic ANOVA

perform_lm_anova(): Linear models ANOVA table

perform_t_test(): Pairwise and paired t-tests

perform_kruskal_wallis(): Kruskal-Wallis rank-sum test

perform_non_parametric(): Pairwise and paired non-parametric tests

perform_correlation_tests(): Correlation test

perform_auc(): Area under curve

perform_homoscedasticity_tests(): Test homoscedasticity

cohens_d(): Cohen's D

fold_change(): Fold change

summary_statistics(): Summary statistics

summarize_results(): Statistics cleaning

Supervised learning

muvr_analysis(): Multivariate modelling with minimally biased variable selection (MUVR)

mixomics_pls() mixomics_pls_optimize() mixomics_spls_optimize(): PLS

mixomics_plsda() mixomics_plsda_optimize() mixomics_splsda_optimize(): PLS-DA

fit_rf(): Fit Random Forest

importance_rf(): Feature importance in random forest

perform_permanova(): PERMANOVA

Feature wise visualizations

save_beeswarm_plots(): Save beeswarm plots of each feature by group

save_group_boxplots(): Save box plots of each feature by group

save_scatter_plots(): Save scatter plots of each feature against a set variable

save_subject_line_plots(): Save line plots with mean

save_group_lineplots(): Save line plots with errorbars by group

Results visualizations

plot_p_histogram(): Histogram of p-values

volcano_plot(): Volcano plot

manhattan_plot(): Manhattan plot

mz_rt_plot(): Plot m/z vs retention time plot (cloud plot)

plot_effect_heatmap(): Heatmap of effects between variables, such as correlations

Object utilities

read_from_excel(): Read formatted Excel files

construct_metabosets(): Construct MetaboSet objects

write_to_excel(): Write results to Excel file

group_col() `group_col<-`(): Get and set name of the special column for group labels

time_col() `time_col<-`(): Get and set the name of the special column for time points

subject_col() `subject_col<-`(): Get and set the name of special column for subject identifiers

flag() `flag<-`(): Get and set the values in the flag column

drop_flagged(): Drop flagged features

drop_qcs(): Drop QC samples

join_fData(): Join new columns to feature data

join_pData(): Join new columns to pheno data

join_rowData(): Join new columns to feature data

join_colData(): Join new columns to pheno data

combined_data(): Retrieve both sample information and features

merge_metabosets(): Merge MetaboSet objects together

merge_objects(): Merge SummarizedExperiment objects together

fix_object(): Fix object for functioning of notame

combined_data(<MetaboSet>) group_col(<MetaboSet>) `group_col<-`(<MetaboSet>) time_col(<MetaboSet>) `time_col<-`(<MetaboSet>) subject_col(<MetaboSet>) `subject_col<-`(<MetaboSet>) flag(<MetaboSet>) `flag<-`(<MetaboSet>) join_fData(<MetaboSet>,<data.frame>) join_pData(<MetaboSet>,<data.frame>): An S4 class used to represent LC-MS datasets

Other utilities

citations(): Show citations

init_log(): Initialize log to a file

log_text(): Log text to the current log file

finish_log(): Finish a log

save_plot(): Save plot to chosen format

fix_MSMS(): Transform the MS/MS output to publication ready

Datasets

example_set hilic_neg_sample hilic_pos_sample rp_neg_sample rp_pos_sample: Toy data set

Package

notame notame-package: notame package.