alignFactors: Align factor loadings to get final integration

Description

This function is a wrapper to switch between alternative factor loading alignment methods that LIGER provides, which is a required step for producing the final integrated result. Two methods are provided (click on options for more details):

method = "quantileNorm": Previously published quantile normalization method. (default)
method = "centroidAlign": Newly developed centroid alignment method.

Usage

alignFactors(object, method = c("quantileNorm", "centroidAlign"), ...)
# S3 method for liger
alignFactors(object, method = c("quantileNorm", "centroidAlign"), ...)
# S3 method for Seurat
alignFactors(object, method = c("quantileNorm", "centroidAlign"), ...)

Arguments

object

A liger or Seurat object with valid factorization result available (i.e. runIntegration performed in advance).

method

Character, method to align factors. Default "centroidAlign". Optionally "quantileNorm".

...

Additional arguments passed to selected methods. For "quantileNorm":

quantiles: Number of quantiles to use for quantile normalization. Default 50.

reference

Character, numeric or logical selection of one dataset, out of all available datasets in object, to use as a "reference" for quantile normalization. Default NULL tries to find an RNA dataset with the largest number of cells; if no RNA dataset available, use the globally largest dataset.

minCells

Minimum number of cells to consider a cluster shared across datasets. Default 20.

nNeighbors

Number of nearest neighbors for within-dataset knn graph. Default 20.

useDims

Indices of factors to use for shared nearest factor determination. Default NULL uses all factors.

center

Whether to center the data when scaling factors. Could be useful for less sparse modalities like methylation data. Default FALSE.

maxSample

Maximum number of cells used for quantile normalization of each cluster and factor. Default 1000.

eps

The error bound of the nearest neighbor search. Lower values give more accurate nearest neighbor graphs but take much longer to compute. Default 0.9.

refineKNN

Whether to increase robustness of cluster assignments using KNN graph. Default TRUE.

clusterName

Variable name that will store the clustering result in metadata of a liger object or a Seurat object. Default "quantileNorm_cluster".

seed

Random seed to allow reproducible results. Default 1.

verbose

Logical. Whether to show information of the progress. Default getOption("ligerVerbose") or TRUE if users have not set.

For "centroidAlign" :

lambda: Ridge regression penalty applied to each dataset. Can be one number that applies to all datasets, or a numeric vector with length equal to the number of datasets. Default 1.
useDims: Indices of factors to use considered for the alignment. Default NULL uses all factors.
scaleEmb: Logical, whether to scale the factor loading being considered as the embedding. Default TRUE.
centerEmb: Logical, whether to center the factor loading being considered as the embedding before scaling it. Default TRUE.
scaleCluster: Logical, whether to scale the factor loading being considered as the cluster assignment probability. Default FALSE.
centerCluster: Logical, whether to center the factor loading being considered as the cluster assignment probability before scaling it. Default FALSE.
shift: Logical, whether to shift the factor loading being considered as the cluster assignment probability after centered scaling. Default FALSE.
diagnosis: Logical, whether to return cell metadata variables with diagnostic information. Default FALSE.

Description

Usage

Arguments

See Also