This function performs unsupervised PCA on each mixscape class separately and projects each subspace onto all cells in the data. Finally, it uses the first 10 principle components from each projection as input to lda in MASS package together with mixscape class labels.
Usage
MixscapeLDA(
object,
assay = NULL,
ndims.print = 1:5,
nfeatures.print = 30,
reduction.key = "LDA_",
seed = 42,
pc.assay = "PRTB",
labels = "gene",
nt.label = "NT",
npcs = 10,
verbose = TRUE,
logfc.threshold = 0.25
)Arguments
- object
An object of class Seurat.
- assay
Assay to use for performing Linear Discriminant Analysis (LDA).
- ndims.print
Number of LDA dimensions to print.
- nfeatures.print
Number of features to print for each LDA component.
- reduction.key
Reduction key name.
- seed
Value for random seed
- pc.assay
Assay to use for running Principle components analysis.
- labels
Meta data column with target gene class labels.
- nt.label
Name of non-targeting cell class.
- npcs
Number of principle components to use.
- verbose
Print progress bar.
- logfc.threshold
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25. Increasing logfc.threshold speeds up the function, but can miss weaker signals.