quantile_norm: Quantile align (normalize) factor loadings

In rliger: Linked Inference of Genomic Experimental Relationships

Quantile align (normalize) factor loadings

Description

This process builds a shared factor neighborhood graph to jointly cluster cells, then quantile normalizes corresponding clusters.

Usage

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,
  ...
)

Arguments

object	liger object. Should run optimizeALS before calling.
...	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)

Details

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.

Value

liger object with 'H.norm' and 'clusters' slot set.

Examples

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)

rliger documentation built on Nov. 9, 2023, 1:07 a.m.