This process builds a shared factor neighborhood graph to jointly cluster cells, then quantile normalizes corresponding clusters.
quantile_norm(object, ...)# S3 method for 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 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,
...
)
liger object with 'H.norm' and 'clusters' slot set.
liger object. Should run optimizeALS before calling.
Arguments passed to other methods
Number of quantiles to use for quantile normalization (default 50).
Name of dataset to use as a "reference" for normalization. By default, the dataset with the largest number of cells is used.
Minimum number of cells to consider a cluster shared across datasets (default 20)
Number of nearest neighbors for within-dataset knn graph (default 20).
Indices of factors to use for shared nearest factor determination (default 1:ncol(H[[1]])).
Centers the data when scaling factors (useful for less sparse modalities like methylation data). (default FALSE)
Maximum number of cells used for quantile normalization of each cluster and factor. (default 1000)
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.
whether to increase robustness of cluster assignments using KNN graph.(default TRUE)
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.
ligerex <- createLiger(list(ctrl = ctrl, stim = stim))
ligerex <- normalize(ligerex)
ligerex <- selectGenes(ligerex)
ligerex <- scaleNotCenter(ligerex)
ligerex <- optimizeALS(ligerex, k = 5, max.iters = 1)
ligerex <- quantile_norm(ligerex)
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