RunOptimizeALS | R Documentation |
Run optimizeALS on a Seurat object
RunOptimizeALS(
object,
k,
assay = NULL,
split.by = "orig.ident",
lambda = 5,
thresh = 1e-06,
max.iters = 30,
reduction.name = "iNMF_raw",
reduction.key = "riNMF_",
nrep = 1,
H.init = NULL,
W.init = NULL,
V.init = NULL,
rand.seed = 1,
print.obj = FALSE,
...
)
object |
A merged Seurat object |
k |
Inner dimension of factorization (number of factors). Run suggestK to determine appropriate value; a general rule of thumb is that a higher k will be needed for datasets with more sub-structure. |
assay |
Assay to use, defaults to the default assay of the first object |
split.by |
Attribute for splitting, defaults to "orig.ident" |
lambda |
Regularization parameter. Larger values penalize dataset-specific effects more strongly (ie. alignment should increase as lambda increases). Run suggestLambda to determine most appropriate value for balancing dataset alignment and agreement (default 5.0). |
thresh |
Convergence threshold. Convergence occurs when |obj0-obj|/(mean(obj0,obj)) < thresh. (default 1e-6) |
max.iters |
Maximum number of block coordinate descent iterations to perform (default 30). |
reduction.name |
Name to store resulting DimReduc object as |
reduction.key |
Key for resulting DimReduc |
nrep |
Number of restarts to perform (iNMF objective function is non-convex, so taking the best objective from multiple successive initializations is recommended). For easier reproducibility, this increments the random seed by 1 for each consecutive restart, so future factorizations of the same dataset can be run with one rep if necessary. (default 1) |
H.init |
Initial values to use for H matrices. (default NULL) |
W.init |
Initial values to use for W matrix (default NULL) |
V.init |
Initial values to use for V matrices (default NULL) |
rand.seed |
Random seed to allow reproducible results (default 1). |
print.obj |
Print objective function values after convergence (default FALSE). |
... |
Arguments passed to other methods |
A Seurat object with embeddings and loadings from optimizeALS
stored as a DimReduc object with name reduction.name
(key set to reduction.key
);
per-dataset feature loadings matrices stored in the tool
slot, accessible with
Tool
optimizeALS
Tool
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