The Seurat object is the center of each single cell analysis. It stores all information associated with the dataset, including data, annotations, analyes, 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
raw.data:"ANY", The raw project data
data:"ANY", The expression matrix (log-scale)
scale.data:"ANY", The scaled (after z-scoring
each gene) expression matrix. Used for PCA, ICA, and heatmap plotting
var.genes:"vector", Variable genes across single cells
is.expr:"numeric", Expression threshold to determine if a gene is expressed
ident:"vector", The 'identity class' for each single cell
data.info:"data.frame", Contains information about each cell, starting with # of genes detected (nGene)
the original identity class (orig.ident), user-provided information (through AddMetaData), etc.
project.name:"character", Name of the project (for record keeping)
pca.x:"data.frame", Gene projection scores for the PCA analysis
pca.x.full:"data.frame", Gene projection scores for the projected PCA (contains all genes)
pca.rot:"data.frame", The rotation matrix (eigenvectors) of the PCA
ica.x:"data.frame", Gene projection scores for ICA
ica.rot:"data.frame", The estimated source matrix from ICA
tsne.rot:"data.frame", Cell coordinates on the t-SNE map
mean.var:"data.frame", The output of the mean/variability analysis for all genes
imputed:"data.frame", Matrix of imputed gene scores
final.prob:"data.frame", For spatial inference, posterior probability of each cell mapping to each bin
insitu.matrix:"data.frame", For spatial inference, the discretized spatial reference map
cell.names:"vector", Names of all single cells (column names of the expression matrix)
cluster.tree:"list", List where the first element is a phylo object containing the
phylogenetic tree relating different identity classes
snn.sparse:"dgCMatrix", Sparse matrix object representation of the SNN graph
snn.dense:"matrix", Dense matrix object representation of the SNN graph
snn.k:"numeric", k used in the construction of the SNN graph
Each Seurat object has a number of slots which store information. Key slots to access are listed below.