The SCseq class is the central object storing all information generated during cell type identification with the RaceID3 algorithm. It comprises a number of slots for a variety of objects.

validity function for SCceq

object

An SCseq object.

`expdata`

The raw expression data matrix with cells as columns and genes as rows in sparse matrix format.

`ndata`

Filtered data with expression normalized to one for each cell.

`counts`

Vector with total transcript counts for each cell in `ndata`

remaining after filtering.

`genes`

Vector with gene names of all genes in `ndata`

remaining after filtering.

`dimRed`

list object object storing information on a feature matrix obtained by dimensional reduction, batch effect correction etc.
Component `x`

stores the actual feature matrix.

`distances`

distance (or dis-similarity) matrix computed by RaceID3.

`imputed`

list with two matrices computed for imputing gene expression. The first matrix `nn`

contains the cell indices of the `knn`

nearest neighbours,
the second matrix contains the probabilities at which each cell contributes to thye imputed gene expression value for the cell correponding to the columns.

`tsne`

data.frame with coordinates of two-dimensional tsne layout computed by RaceID3.

`fr`

data.frame with coordinates of two-dimensional Fruchterman-Rheingold graphlayout computed by RaceID3.

`cluster`

list storing information on the initial clustering step of the RaceID3 algorithm

`background`

list storing the polynomial fit for the background model of gene expression variability computed by RaceID3, which is used for outlier identification.

`out`

list storing information on outlier cells used for the prediction of rare cell types by RaceID3

`cpart`

vector containing the final clustering (i.e. cell type) partition computed by RaceID3

`fcol`

vector contaning the colour scheme for the RaceID3 clusters

`medoids`

vector containing the cell ids for th cluster medoids

`filterpar`

list containing the parameters used for cell and gene filterung

`clusterpar`

list containing the parameters used for clustering

`outlierpar`

list containing the parameters used for outlier identification