seurat-class: The Seurat Class

Description

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

Arguments

Slots

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

Details

Each Seurat object has a number of slots which store information. Key slots to access are listed below.