# Multiple Co-Inertia Analysis via the NIPALS Method

## 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