Function reference • moiraine

Project management

Getting started with a new data integration project

create_moiraine_pipeline(): Creates a target script file from template

Data import

Importing data into R

import_dataset_csv(): Import a dataset from a csv file

import_fmetadata_csv(): Import feature metadata from a csv file

import_fmetadata_gff(): Import features metadata from a GFF/GTF file

import_smetadata_csv(): Import samples metadata from a csv file

import_dataset_csv_factory(): Target factory for csv datasets import

import_fmetadata_csv_factory(): Target factory for csv features metadata import

import_fmetadata_gff_factory(): Target factory for GFF/GTF features metadata import

import_smetadata_csv_factory(): Target factory for csv samples metadata import

Omics and multi-omics sets creation

Creating omics sets

create_omics_set(): Create a Biobase set object to store omics data

create_omics_set_factory(): Target factory for omics sets creation

create_multiomics_set(): Create a MultiDataSet object to store multi-omics data

add_omics_set(): Adds an omics set to a MultiDataSet object

MetabolomeSet MetabolomeSet-class: Class to contain objects describing high-throughput metabolomics assays.

PhenotypeSet PhenotypeSet-class: Class to contain objects describing phenotypic assays.

add_metabo(): Method to add a MetabolomeSet to a MultiDataSet object.

add_pheno(): Method to add a PhenotypeSet to a MultiDataSet object.

Multi-omics sets (MultiDataSet objects)

Querying

Querying the MultiDataSet object

n_features(): Number of features in each dataset of MultiDataSet object

n_samples(): Number of samples in each dataset of MultiDataSet object

get_features(): Get feature IDs from MultiDataSet

get_samples(): Get sample IDs from MultiDataSet

get_datasets(): Get multi-omics measurement datasets

get_dataset_matrix(): Get multi-omics dataset as matrix

get_features_metadata(): Get features metadata dataframes from MultiDataSet

get_samples_metadata(): Get samples metadata dataframes from MultiDataSet

get_samples_metadata_combined(): Get combined samples metadata data-frame from MultiDataSet

check_missing_values(): Check for missing values in MultiDataSet

get_features_labels(): Get feature labels

join_features_metadata(): Join feature metadata to table

join_samples_metadata(): Join samples metadata to table

Plotting

Plotting properties of omics datasets

plot_samples_upset(): Upset plot of samples

plot_density_data(): Per-dataset density plot for MultiDataSet object

plot_meansd_data(): Per-dataset mean-sd trend plots for MultiDataSet object

plot_data_covariate(): Plots omics data vs sample covariate

plot_data_heatmap(): Plots omics data as heatmap

Modifying

Modifying or subsetting datasets or metadata

replace_dataset(): Replace matrix dataset within a MultiDataSet object

round_dataset(): Round values in omics dataset from MultiDataSet object

add_features_metadata(): Adding data-frame to features metadata

add_samples_metadata(): Adding data-frame to samples metadata

subset_features(): Subset a MultiDataSet object by feature

PCA

PCA and missing values imputation on multi-omics set

run_pca(): Run PCA on MultiDataSet

run_pca_matrix(): Run PCA on matrix

get_complete_data(): Get MultiDataSet object with imputed values

pca_complete_data_factory(): Target factory for PCA run and missing values imputation on each omics dataset

plot_screeplot_pca(): Screeplots for single-omics PCA

plot_samples_coordinates_pca(): Samples score plots for single-omics PCA

get_pca_arguments(): Extract arguments used in PCA run

Transformation

Omics datasets transformation and normalisation

transform_bestNormalise_auto(): Applies the bestNormalize function to rows of a matrix

transform_bestNormalise_manual(): Applies a normalisation method from bestNormalize to rows of a matrix

transform_dataset(): Applies a transformation to a dataset from a MultiDataSet object

transform_logx(): Applies a log-x transformation to matrix

transform_vsn(): Applies Variance Stabilising Normalisation (vsn) to matrix

transform_vst(): Applies Variance Stabilising Transformation (DESeq2) to matrix

transformation_datasets_factory(): Target factory for datasets transformation

get_transformed_data(): Get MultiDataSet with transformed data

get_table_transformations(): Get table with transformation applied to each dataset

zero_to_half_min(): Replace zeros with half-min in matrix

Prefiltering

Features prefiltering for multi-omics set

Unsupervised (Median Absolute Variation)

select_features_mad(): Select features based on Median Absolute Deviation from MultiDataSet

select_features_cov(): Select features based on Coefficient of Variation from MultiDataSet

select_features_mad_matrix(): Select features based on Median Absolute Deviation from matrix

select_features_cov_matrix(): Select features based on Coefficient of Variation from matrix

get_filtered_dataset_variability(): Get filtered MultiDataSet object based on variability measure

feature_preselection_mad_factory(): Target factory for feature preselection based on Median Absolute Deviation

feature_preselection_cov_factory(): Target factory for feature preselection based on Coefficient of Variation

plot_feature_preselection_mad(): Diagnostics plots for MAD-based feature preselection

plot_feature_preselection_cov(): Diagnostics plots for COV-based feature preselection

Supervised (sPLS-DA)

get_input_splsda(): Generate sPLS-DA input data (for mixomics)

perf_splsda(): Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object

run_splsda(): Performs sPLS-DA on omics dataset from MultiDataSet object

get_filtered_dataset_splsda(): Get filtered MultiDataSet object based on sPLS-DA runs

feature_preselection_splsda_factory(): Target factory for feature preselection based on sPLS-DA

plot_feature_preselection_splsda(): Diagnostics plots for sPLS-DA-based feature preselection

Supervised integration

Integration of datasets aiming to discriminate samples based on an outcome of interest

DIABLO

Supervised integration with the DIABLO method from mixOmics

get_input_mixomics_supervised(): Generate mixomics input data for supervised methods

run_pairwise_pls(): Pairwise PLS datasets comparison

diablo_generate_design_matrix(): Generate DIABLO design matrix

diablo_get_optim_ncomp(): Get the optimal ncomp value

diablo_get_pairwise_pls_corr(): Get pairwise correlations from PLS run

diablo_get_params(): Get parameters from DIABLO run

diablo_get_wa_coord(): Get weighted average coordinates

diablo_pairwise_pls_factory(): Target factory for pairwise PLS and design matrix estimation for DIABLO run

diablo_plot(): Plots DIABLO output

diablo_plot_circos(): Plots DIABLO circos plot

diablo_plot_perf(): Plots DIABLO perf results

diablo_plot_tune(): Plots DIABLO tune results

diablo_plot_var(): Plots DIABLO features correlation circle

diablo_predefined_design_matrix(): Generate a design matrix for DIABLO

diablo_run(): Runs DIABLO algorithm

diablo_table_optim_keepX(): Formatted table with optimal keepX values

diablo_tune(): Tunes keepX arg for DIABLO

MEFISTO

Supervised integration with the MEFISTO method from MOFA2 (for time-series or spatially resolved data)

get_input_mefisto(): Generate MEFISTO input data

Unsupervised integration

Integration of datasets aiming at assessing variation common to the datasets

MOFA

Unsupervised integration with the MOFA method from MOFA2

get_input_mofa2(): Generate input data for MOFA2 package

get_input_mofa(): Generate MOFA input data

mofa_get_weights(): Get feature weights from MOFA object

mofa_plot_cor_covariates(): Plots the correlation between factors and covariates for MOFA

sO2PLS

Unsupervised integration of 2 datasets with the sO2PLS method from omicsPLS

get_input_omicspls(): Generate omicsPLS input data

so2pls_compare_samples_joint_components(): Compares sO2PLS samples joint component scores between the two datasets

so2pls_crossval_o2m(): Wrapper for OmicsPLS::crossval_o2m function

so2pls_crossval_o2m_adjR2(): Wrapper for OmicsPLS::crossval_o2m_adjR2 function

so2pls_crossval_sparsity(): Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS

so2pls_get_components(): Get list of latent components from sO2PLS results

so2pls_get_optim_keep(): Extract optimal number of features to keep from cross-validation results for sO2PLS

so2pls_get_optim_ncomp(): Extract optimal number of components from cross-validation results for sO2PLS

so2pls_get_optim_ncomp_adj(): Extract optimal number of components from adjusted cross-validation results for sO2PLS

so2pls_get_variance_explained(): Percentage of variance explained for sO2PLS

so2pls_get_wa_coord(): Computes average sample coordinates for sO2PLS joint components

so2pls_o2m(): Wrapper for OmicsPLS::o2m function

so2pls_plot_cv(): Plots cross-validation results for sO2PLS

so2pls_plot_cv_adj(): Plot adjusted cross-validation results for sO2PLS

so2pls_plot_cv_sparsity(): Plot sparsity cross-validation results for sO2PLS

so2pls_plot_joint_components_coefficients(): Plots sO2PLS contributions between datasets joint components

so2pls_plot_samples_joint_components(): Plots sO2PLS joint components samples scores

so2pls_plot_samples_specific_components(): Plots sO2PLS specific components samples scores

so2pls_plot_summary(): Plot summary of sO2PLS run

so2pls_print_cv_adj(): Print adjusted cross-validation results for sO2PLS

so2pls_print_cv_sparsity(): Print sparsity cross-validation results for sO2PLS

so2pls_screeplot(): Screeplot sO2PLS run

sPLS

Unsupervised integration of 2 datasets with the sPLS method from mixOmics

get_input_spls(): Generate sPLS input data (for mixomics)

get_input_mixomics_unsupervised(): Generate mixomics input data for unsupervised methods

spls_get_optim_ncomp(): Select the optimal ncomp from sPLS cross-validation results

spls_get_params(): Get parameters from sPLS run

spls_get_wa_coord(): Computes average sample coordinates for sPLS components

spls_plot_tune(): Displays results of sPLS tuning

spls_plot_var(): Plots sPLS features correlation circle

spls_run(): Run sPLS algorithm

spls_tune(): Performs cross-validation for mixomics sPLS to select optimal keepX and keepY

Standardised method output

Functions to get and query the results of an integration method as a standardised R object

get_output() get_output_pca() get_output_splsda() get_output_spls() get_output_diablo() get_output_mofa2() get_output_so2pls(): Extract output of integration method in standard format

get_latent_dimensions(): Get latent dimensions levels from dimension reduction output

get_top_features(): Extract top features

get_selected_features(): Extract selected features

Plotting functions

plot_variance_explained(): Plot of variance explained

plot_samples_score(): Plots sample scores as scatterplot matrix

plot_samples_score_pair(): Plots sample scores as a scatterplot

plot_samples_score_covariate(): Plots sample scores against covariate

plot_features_weight_distr(): Plots features weight distribution

plot_features_weight_pair(): Plots features weight as a scatterplot

plot_features_weight_covariate(): Plots features weight against covariate

plot_top_features(): Plots top features importance

Methods evaluation

Evaluating the results of an integration method against prior knowledge

evaluate_feature_selection_table(): Evaluate feature selection against features label

make_feature_sets_from_df(): Makes list of feature sets from data-frame

make_feature_sets_from_fm(): Makes list of feature sets from features metadata

check_feature_sets(): Checks features assignment to sets

reduce_feature_sets_data(): Reduce feature sets to match multi-omics dataset

evaluate_method_enrichment(): Enrichment analysis for integration results

plot_features_weight_set(): Plots features weight in/not in a set

compute_samples_silhouette(): Computes samples silhouette score from method output

Methods comparison

Comparison of the results from several integration methods

get_samples_score_correlation(): Get samples score correlation

get_features_weight_correlation(): Get features weight correlation

comparison_heatmap_corr(): Heatmap of correlation between latent dimensions

comparison_plot_correlation(): Correlation plot between latent components

compute_consensus_importance(): Computes consensus feature importance

consensus_importance_metric(): Calculate features importance score

show_consensus_metrics(): Illustrates importance consensus metrics

Other (utils)

Other miscellaneous functions

Helper MultiDataSet object

check_is_multidataset(): Checks whether object is MultiDataSet

check_input_multidataset(): Check a MultiDataSet input

Helper plotting functions

plot_correlation_matrix(): Plot correlation matrix

plot_correlation_matrix_full(): Plots a full correlation matrix (corrplot-style)

ggpairs_custom(): ggpairs plot with custom colours

plot_x_wrapper(): Wrapper to create plot

plot_x_continuous(): Scatter plot function

plot_x_discrete(): Violin plot function function

Misc.

hclust_matrix_rows(): Hierarchical clustering of matrix rows

options_list_as_tibble(): Returns options list as a tibble

is_equal_or_null(): Check null or equality