The Seurat object is the center of each single cell analysis. It stores all information associated with the dataset, including data, annotations, analyses, etc. All that is needed to construct a Seurat object is an expression matrix (rows are genes, columns are cells), which should be log-scale
# S4 method for seurat
show(object)
A Seurat
object
raw.data
The raw project data
data
The normalized expression matrix (log-scale)
scale.data
scaled (default is z-scoring each gene) expression matrix; used for dimensional reduction and heatmap visualization
var.genes
Vector of genes exhibiting high variance across single cells
is.expr
Expression threshold to determine if a gene is expressed (0 by default)
ident
THe 'identity class' for each cell
meta.data
Contains meta-information about each cell, starting with
number of genes detected (nFeature) and the original identity class
(orig.ident); more information is added using AddMetaData
project.name
Name of the project (for record keeping)
dr
List of stored dimensional reductions; named by technique
assay
List of additional assays for multimodal analysis; named by technique
hvg.info
The output of the mean/variability analysis for all genes
imputed
Matrix of imputed gene scores
cell.names
Names of all single cells (column names of the expression matrix)
cluster.tree
List where the first element is a phylo object containing the phylogenetic tree relating different identity classes
snn
Spare matrix object representation of the SNN graph
calc.params
Named list to store all calculation-related parameter choices
kmeans
Stores output of gene-based clustering from DoKMeans
spatial
Stores internal data and calculations for spatial mapping of single cells
misc
Miscellaneous spot to store any data alongside the object (for example, gene lists)
version
Version of package used in object creation
Each Seurat object has a number of slots which store information. Key slots to access are listed below.