Quantile align (normalize) factor loadings
This process builds a shared factor neighborhood graph to jointly cluster cells, then quantile normalizes corresponding clusters.
quantile_norm(object, ...) ## S3 method for class 'list' quantile_norm( object, quantiles = 50, ref_dataset = NULL, min_cells = 20, knn_k = 20, dims.use = NULL, do.center = FALSE, max_sample = 1000, eps = 0.9, refine.knn = TRUE, rand.seed = 1, ... ) ## S3 method for class 'liger' quantile_norm( object, quantiles = 50, ref_dataset = NULL, min_cells = 20, knn_k = 20, dims.use = NULL, do.center = FALSE, max_sample = 1000, eps = 0.9, refine.knn = TRUE, rand.seed = 1, ... )
object |
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... |
Arguments passed to other methods |
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 20) |
knn_k |
Number of nearest neighbors for within-dataset knn graph (default 20). |
dims.use |
Indices of factors to use for shared nearest factor determination (default 1:ncol(H[[1]])). |
do.center |
Centers the data when scaling factors (useful for less sparse modalities like methylation data). (default FALSE) |
max_sample |
Maximum number of cells used for quantile normalization of each cluster and factor. (default 1000) |
eps |
The error bound of the nearest neighbor search. (default 0.9) Lower values give more accurate nearest neighbor graphs but take much longer to computer. |
refine.knn |
whether to increase robustness of cluster assignments using KNN graph.(default TRUE) |
rand.seed |
Random seed to allow reproducible results (default 1) |
The first step, building the shared factor neighborhood graph, is performed in SNF(), and produces a graph representation where edge weights between cells (across all datasets) correspond to their similarity in the shared factor neighborhood space. An important parameter here is knn_k, the number of neighbors used to build the shared factor space.
Next we perform quantile alignment for each dataset, factor, and cluster (by stretching/compressing datasets' quantiles to better match those of the reference dataset). These aligned factor loadings are combined into a single matrix and returned as H.norm.
liger object with 'H.norm' and 'clusters' slot set.
## Not run: # ligerex (liger object), factorization complete # do basic quantile alignment ligerex <- quantile_norm(ligerex) # higher resolution for more clusters (note that SNF is conserved) ligerex <- quantile_norm(ligerex, resolution = 1.2) # change knn_k for more fine-grained local clustering ligerex <- quantile_norm(ligerex, knn_k = 15, resolution = 1.2) ## End(Not run)
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