Multiple Co-Inertia Analysis via the NIPALS Method

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Documentation for package ‘nipalsMCIA’ version 1.6.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

block_preproc	Block-level preprocessing
block_weights_heatmap	block_weights_heatmap
cc_preproc	Centered Column Profile Pre-processing
col_preproc	Centered Column Profile Pre-processing
data_blocks	NCI-60 Multi-Omics Data
deflate_block_bl	Deflation via block loadings
deflate_block_gs	Deflation via global scores
extract_from_mae	Extract a list of harmonized data matrices from an MAE object
get_colors	Assigning colors to different omics
get_metadata_colors	Assigning colors to different values of a metadata column
get_tv	Computes the total variance of a multi-omics dataset
global_scores_eigenvalues_plot	global_scores_eigenvalues_plot
global_scores_heatmap	Plotting a heatmap of global factors scores (sample v. factors)
gsea_report	Perform biological annotation-based comparison
metadata_NCI60	NCI-60 Multi-Omics Metadata
NipalsResult	An S4 class to contain results computed with 'nipals_multiblock()'
NipalsResult-class	An S4 class to contain results computed with 'nipals_multiblock()'
nipals_iter	NIPALS Iteration
nipals_multiblock	Main NIPALS computation loop
nmb_get_bl	Accessor function for block loadings
nmb_get_bs	Accessor function for block scores
nmb_get_bs_weights	Accessor function for block score weights
nmb_get_eigs	Accessor function for eigenvalues
nmb_get_gl	Accessor function for global loadings
nmb_get_gs	Accessor function for global scores
nmb_get_metadata	Accessor function for metadata
ord_loadings	Ranked global loadings dataframe
predict_gs	Prediction of new global scores based on block loadings and weights
projection_plot	projection_plot
simple_mae	Create an MAE object from a list of data matrices and column data
vis_load_ord	Visualize ranked loadings
vis_load_plot	Visualize all loadings on two factor axes