![[Superseded]](figures/lifecycle-superseded.svg)
Please turn to quantileNorm.
This process builds a shared factor neighborhood graph to jointly cluster cells, then quantile normalizes corresponding clusters.
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.
Arguments
- object
ligerobject. Should run optimizeALS before calling.- knn_k
Number of nearest neighbors for within-dataset knn graph (default 20).
- 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)
- quantiles
Number of quantiles to use for quantile normalization (default 50).
- 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.
- 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)
- refine.knn
whether to increase robustness of cluster assignments using KNN graph.(default TRUE)
- rand.seed
Random seed to allow reproducible results (default 1)