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Project cell group scores onto trained NMF latent factors

project_factors.Rd

Applies the positive-negative split to new samples and projects them onto the latent space learned during training using the Moore-Penrose pseudoinverse of the scaled basis matrix W.

Usage

project_factors(latent_spaces, scores_test)

Arguments

latent_spaces

A list returned by compute.latent_factors(), containing at minimum W (features x rank), rank, and the RcppML model object with nmf_model$d scaling vector.

scores_test

A samples x cell groups matrix of signed CCA composite scores for the test cohort. Column names must match those used in training (before the _pos/_neg suffix was added).

Value

A non-negative matrix of size samples x rank containing the projected NMF factor scores for the test cohort.

Details

The RcppML NMF decomposition is A = W %% diag(d) %% H, not A = W %*% H. The diagonal scaling vector d must be absorbed into W before computing the pseudoinverse, otherwise the projected scores are on the wrong scale.