Integrate scaled datasets with iNMF or variant methods

LIGER provides dataset integration methods based on iNMF (integrative Non-negative Matrix Factorization [1]) and its variants (online iNMF [2] and UINMF [3]). This function wraps runINMF, runOnlineINMF and runUINMF, of which the help pages have more detailed description.

Usage

runIntegration(
  object,
  k = 20,
  lambda = 5,
  method = c("iNMF", "onlineINMF", "UINMF"),
  ...
)

# S3 method for liger
runIntegration(
  object,
  k = 20,
  lambda = 5,
  method = c("iNMF", "onlineINMF", "UINMF"),
  seed = 1,
  verbose = getOption("ligerVerbose", TRUE),
  ...
)

# S3 method for Seurat
runIntegration(
  object,
  k = 20,
  lambda = 5,
  method = c("iNMF", "onlineINMF"),
  datasetVar = "orig.ident",
  useLayer = "ligerScaleData",
  assay = NULL,
  seed = 1,
  verbose = getOption("ligerVerbose", TRUE),
  ...
)

Arguments

object: A liger object or a Seurat object with non-negative scaled data of variable features (Done with scaleNotCenter).
k: Inner dimension of factorization (number of factors). Generally, a higher k will be needed for datasets with more sub-structure. Default 20.
lambda: Regularization parameter. Larger values penalize dataset-specific effects more strongly (i.e. alignment should increase as lambda increases). Default 5.
method: iNMF variant algorithm to use for integration. Choose from "iNMF", "onlineINMF", "UINMF". Default "iNMF".
...: Arguments passed to other methods and wrapped functions.
seed: Random seed to allow reproducible results. Default 1.
verbose: Logical. Whether to show information of the progress. Default getOption("ligerVerbose") or TRUE if users have not set.
datasetVar: Metadata variable name that stores the dataset source annotation. Default "orig.ident".
useLayer: For Seurat>=4.9.9, the name of layer to retrieve input non-negative scaled data. Default "ligerScaleData". For older Seurat, always retrieve from scale.data slot.
assay: Name of assay to use. Default NULL uses current active assay.

Value

Updated input object. For detail, please refer to the refered method linked in Description.

References

Joshua D. Welch and et al., Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity, Cell, 2019
Chao Gao and et al., Iterative single-cell multi-omic integration using online learning, Nat Biotechnol., 2021
April R. Kriebel and Joshua D. Welch, UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization, Nat. Comm., 2022

Examples

pbmc <- normalize(pbmc)
#> ℹ Normalizing datasets "ctrl"
#> ℹ Normalizing datasets "stim"
#> ✔ Normalizing datasets "stim" ... done
#> 
#> ℹ Normalizing datasets "ctrl"

#> ✔ Normalizing datasets "ctrl" ... done
#> 
pbmc <- selectGenes(pbmc)
#> ℹ Selecting variable features for dataset "ctrl"
#> ✔ ... 168 features selected out of 249 shared features.
#> ℹ Selecting variable features for dataset "stim"
#> ✔ ... 166 features selected out of 249 shared features.
#> ✔ Finally 173 shared variable features are selected.
pbmc <- scaleNotCenter(pbmc)
#> ℹ Scaling dataset "ctrl"
#> ✔ Scaling dataset "ctrl" ... done
#> 
#> ℹ Scaling dataset "stim"
#> ✔ Scaling dataset "stim" ... done
#> 
if (requireNamespace("RcppPlanc", quietly = TRUE)) {
    pbmc <- runIntegration(pbmc)
}