View source: R/liger_reduce_dims.R
liger_reduce_dims | R Documentation |
Dimensionality reduction using Liger factorization
liger_reduce_dims(
ligerex,
k = 30,
lambda = 5,
thresh = 1e-04,
max_iters = 100,
nrep = 1,
h_init = NULL,
w_init = NULL,
v_init = NULL,
rand_seed = 1,
print_obj = FALSE,
quantiles = 50,
ref_dataset = NULL,
min_cells = 2,
knn_k = 20,
center = FALSE,
resolution = 1,
...
)
ligerex |
SingleCellExperiment object preprocessed for Liger Perform iNMF on scaled datasets: |
k |
Inner dimension of factorization (number of factors). Set to k=30 as default. |
lambda |
Regularization parameter. Larger values penalize dataset-specific effects more strongly (ie. alignment should increase as lambda increases). Set to lambda=5.0 as default. |
thresh |
Convergence threshold. Convergence occurs when |obj0-obj|/(mean(obj0,obj)) < thresh (default 1e-4). |
max_iters |
Maximum number of block coordinate descent iterations to perform (default 100). |
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). Quantile align (normalize) factor loadings: |
quantiles |
Number of quantiles to use for quantile normalization (default 50). |
ref_dataset |
Name of dataset to use as a "reference" for normalization. By default, the dataset with the largest number of cells is used. |
min_cells |
Minimum number of cells to consider a cluster shared across datasets (default 2) |
knn_k |
Number of nearest neighbors for within-dataset knn graph (default 20). |
center |
Centers the data when scaling factors (useful for less sparse modalities like methylation data). (default FALSE) |
resolution |
Controls the number of communities detected. Higher resolution -> more communities. (default 1) |
... |
Additional arguments. |
liger object with H, H.norm, W, and V slots sets.
Other Data integration:
integrate_sce()
,
liger_preprocess()
,
report_integrated_sce()
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