Build a Deconvolution–Pathway Relationship Dictionary

The deconvolution_dictionary() function integrates cell-type–specific deconvolution features with a pathway activity matrix. It identifies globally consistent pathway clusters and reannotates each deconvolution feature according to its association with those pathway clusters.

The function first computes a global correlation matrix between the full deconvolution matrix and the provided pathway activity matrix, performs hierarchical clustering on the pathways, and automatically determines the optimal number of pathway clusters using the silhouette method. Each deconvolution feature within each cell type is then correlated with the pathways, scored against the global clusters, and classified into the cluster with which it is most strongly associated.

Usage

deconvolution_dictionary(deconv_subgroups, pathway_matrix)

Arguments

deconv_subgroups

Output of compute.deconvolution.analysis()

pathway_matrix

A numeric matrix or data frame of pathway activities (rows = samples, columns = pathways), with the same row names as the deconvolution matrices. Can be any pathway or feature activity matrix.

Value

An updated version of deconv_subgroups containing:

Deconvolution matrix

A rebuilt deconvolution matrix obtained by column-binding all cell-type–specific subgroups after relabeling features with their corresponding pathway cluster.

Deconvolution subgroups per cell types

The list of cell-type– specific matrices, each with feature names updated to reflect their cluster classification (e.g., "FeatureA_Cluster_1").

Clusters

A list of globally defined pathway clusters (e.g., $Cluster_1, $Cluster_2, ...), where each element contains the pathways belonging to that cluster.

Details

• The optimal number of pathway clusters (\(k\)) is determined automatically using the silhouette width criterion via factoextra::fviz_nbclust().

• The clustering is performed globally across all pathways, ensuring consistent interpretation of clusters across all cell types.

• Each deconvolution feature is assigned to the pathway cluster with the highest eigenvector score.

See also

compute.modules.relationship, fviz_nbclust, hclust, cutree