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Run fastMNN

fast_mnn_sc.Rd

This function implements the fast mutual nearest neighbour (MNN) from Haghverdi, et al. This version works on the PCA embedding and generates an embedding only and not a fully corrected count matrix. The function will iterate through the batches, identify the MNN and generate correction vectors and generate a corrected embedding which is added to the function.

Usage

fast_mnn_sc(
  object,
  batch_column,
  batch_hvg_genes,
  fastmnn_params = params_sc_fastmnn(),
  use_precomputed_pca = FALSE,
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

SingleCells class.

batch_column

String. The column with the batch information in the obs data of the class.

batch_hvg_genes

Integer vector. These are the highly variable genes, identified by a batch-aware method. Please refer to find_hvg_batch_aware_sc() for more details. These genes have to be 0-indexed!

fastmnn_params

A list, please see params_sc_fastmnn(). The list has the following parameters:

  • sigma - Numeric. Bandwidth of the Gaussian smoothing kernel (as proportion of space radius).

  • cos_norm - Logical. Apply cosine normalisation before computing distances.

  • var_adj - Logical. Apply variance adjustment to avoid kissing effects.

  • no_pcs - Integer. Number of PCs to use for MNN calculations.

  • random_svd - Logical. Use randomised SVD.

  • knn - List of kNN parameters. See params_knn_defaults() for available parameters and their defaults.

use_precomputed_pca

Boolean. Should the PCA in the object be used if found. If you decide to do this, make sure that you have run the PCA on the batch-aware HVG ideally.

seed

Integer. Random seed.

.verbose

Boolean. Controls the verbosity of the function.

Value

The object with the added fastMNN embeddings to the object.

References

Haghverdi, et al., Nat Biotechnol, 2018