rliger (version 1.0.0)

calcAlignment: Calculate alignment metric

Description

This metric quantifies how well-aligned two or more datasets are. Alignment is defined as in the documentation for Seurat. We randomly downsample all datasets to have as many cells as the smallest one. We construct a nearest-neighbor graph and calculate for each cell how many of its neighbors are from the same dataset. We average across all cells and compare to the expected value for perfectly mixed datasets, and scale the value from 0 to 1. Note that in practice, alignment can be greater than 1 occasionally.

Usage

calcAlignment(
  object,
  k = NULL,
  rand.seed = 1,
  cells.use = NULL,
  cells.comp = NULL,
  clusters.use = NULL,
  by.cell = FALSE,
  by.dataset = FALSE
)

Arguments

object

liger object. Should call quantile_norm before calling.

k

Number of nearest neighbors to use in calculating alignment. By default, this will be floor(0.01 * total number of cells), with a lower bound of 10 in all cases except where the total number of sampled cells is less than 10.

rand.seed

Random seed for reproducibility (default 1).

cells.use

Vector of cells across all datasets to use in calculating alignment

cells.comp

Vector of cells across all datasets to compare to cells.use when calculating alignment (instead of dataset designations). These can be from the same dataset as cells.use. (default NULL)

clusters.use

Names of clusters to use in calculating alignment (default NULL).

by.cell

Return alignment calculated individually for each cell (default FALSE).

by.dataset

Return alignment calculated for each dataset (default FALSE).

Value

Alignment metric.

Examples

# NOT RUN {
# ligerex (liger object ), factorization complete
ligerex <- quantile_norm(ligerex)
alignment <- calcAlignment(ligerex)
# }