Run NMF (HALS) on MetaCells

Assumes that the sparse data is pre-filtered for the cells/genes you wish to include. Indices in the sparse data need to be 0-indexed. Both data layers hold the supplied values, so which assay NMF runs on is decided by what is passed in, not by use_second_layer.

Usage

rs_nmf_single_mc(
  sparse_data,
  k,
  preprocessing,
  use_second_layer,
  nmf_hals_params,
  seed,
  verbose
)

Arguments

sparse_data

A named list with data, indptr, indices, nrow, ncol and cs_type. Shape is (metacells, genes).

k

Integer. Number of latent factors to return.

preprocessing

String. One of c("none", "sd", "sqrt_sd").

use_second_layer

Boolean. Shall the second data layer be used.

nmf_hals_params

Named list. Contains the NMF parameters, see params_nmf_hals().

seed

Integer. Random seed for initialisation.

verbose

Integer. 0L - quiet; 1L - normal verbosity; 2L - detailed verbosity.

Value

A list with the following items

• w - The W matrix of shape n_meta_cells x k.

• h - The H matrix of shape k x n_genes.

• final_loss - Final squared Frobenius reconstruction loss.

• n_iter - Number of iterations the algorithm ran for.

• converged - Did the NMF algorithm converge.