The DISCBIO class is the central object storing all information generated throughout the pipeline.
An DISCBIO object.
SingleCellExperiment
Representation of the single cell input data, including both cells from regular and ERCC spike-in samples. Data are stored in a SingleCellExperiment object.
expdata
The raw expression data matrix with cells as columns and genes as rows in sparse matrix format. It does not contain ERCC spike-ins.
expdataAll
The raw expression data matrix with cells as columns and genes as rows in sparse matrix format. It can contain ERCC spike-ins.
ndata
Data with expression normalized to one for each cell.
fdata
Filtered data with expression normalized to one for each cell.
distances
A distance matrix.
tsne
A data.frame with coordinates of two-dimensional tsne layout for the K-means clustering.
background
A list storing the polynomial fit for the background model of gene expression variability. It is used for outlier identification.
out
A list storing information on outlier cells used for the prediction of rare cell types.
cpart
A vector containing the final clustering partition computed by K-means.
fcol
A vector contaning the colour scheme for the clusters.
filterpar
A list containing the parameters used for cell and gene filtering based on expression.
clusterpar
A list containing the parameters used for the K-means clustering.
outlierpar
A list containing the parameters used for outlier identification.
kmeans
A list containing the results of running the Clustexp() function.
MBclusters
A vector containing the final clustering partition computed by Model-based clustering.
kordering
A vector containing the Pseudo-time ordering based on k-means clusters.
MBordering
A vector containing the Pseudo-time ordering based on Model-based clusters.
MBtsne
A data.frame with coordinates of two-dimensional tsne layout for the Model-based clustering.
noiseF
A vector containing the gene list resulted from running the noise filtering.
FinalGeneList
A vector containing the final gene list resulted from running the noise filtering or/and the expression filtering.
DISCBIO
class(valuesG1msTest)
G1_reclassified <- DISCBIO(valuesG1msTest)
class(G1_reclassified)
str(G1_reclassified, max.level = 2)
identical(G1_reclassified@expdataAll, valuesG1msTest)
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