The liger object is created from two or more single cell datasets. To construct a liger object, the user needs to provide at least two expression (or another single-cell modality) matrices. The class provides functions for data preprocessing, integrative analysis, and visualization.
raw.data
List of raw data matrices, one per experiment/dataset (genes by cells)
norm.data
List of normalized matrices (genes by cells)
scale.data
List of scaled matrices (cells by genes)
sample.data
List of sampled matrices (gene by cells)
h5file.info
List of HDF5-related information for each input dataset. Paths to raw data, indices, indptr, barcodes, genes and the pipeline through which the HDF5 file is formated (10X, AnnData, etc), type of sampled data (raw, normalized or scaled).
cell.data
Dataframe of cell attributes across all datasets (nrows equal to total number cells across all datasets)
var.genes
Subset of informative genes shared across datasets to be used in matrix factorization
H
Cell loading factors (one matrix per dataset, dimensions cells by k)
H.norm
Normalized cell loading factors (cells across all datasets combined into single matrix)
W
Shared gene loading factors (k by genes)
V
Dataset-specific gene loading factors (one matrix per dataset, dimensions k by genes)
A
Matrices used for online learning (XH)
B
Matrices used for online learning (HTH)
tsne.coords
Matrix of 2D coordinates obtained from running t-SNE on H.norm or H matrices
alignment.clusters
Initial joint cluster assignments from shared factor alignment
clusters
Joint cluster assignments for cells
snf
List of values associated with shared nearest factor matrix for use in clustering and alignment (out.summary contains edge weight information between cell combinations)
agg.data
Data aggregated within clusters
parameters
List of parameters used throughout analysis
version
Version of package used to create object
The key slots used in the liger object are described below.