SignacFast uses pre-computed neural network models to classify cellular phenotypes in single cell data: these models were pre-trained with the HPCA training data. Any features that are present in the training data and absent in the single cell data are set to zero. This is a factor of ~5-10 speed improvement over Signac.

SignacFast(
  E,
  Models = "default",
  spring.dir = NULL,
  num.cores = 1,
  threshold = 0,
  smooth = TRUE,
  impute = TRUE,
  verbose = TRUE,
  do.normalize = TRUE,
  return.probability = FALSE
)

Arguments

E

a gene (rows) by cell (column) matrix, sparse matrix or a Seurat object. Rows are HUGO symbols.

Models

if 'default', as returned by GetModels_HPCA. An ensemble of 1,800 neural network models.

spring.dir

If using SPRING, directory to categorical_coloring_data.json. Default is NULL.

num.cores

number of cores to use for parallel computation. Default is 1.

threshold

Probability threshold for assigning cells to "Unclassified." Default is 0.

smooth

if TRUE, smooths the cell type classifications. Default is TRUE.

impute

if TRUE, gene expression values are imputed prior to cell type classification (see KSoftImpute). Default is TRUE.

verbose

if TRUE, code will report outputs. Default is TRUE.

do.normalize

if TRUE, cells are normalized to the mean library size. Default is TRUE.

return.probability

if TRUE, returns the probability associated with each cell type label. Default is TRUE.

Value

A list of character vectors: cell type annotations (L1, L2, ...) at each level of the hierarchy as well as 'clusters' for the Louvain clustering results.

See also

Signac for another classification function.

Signac

Examples

if (FALSE) { # download single cell data for classification file.dir = "https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_1k_v3/" file = "pbmc_1k_v3_filtered_feature_bc_matrix.h5" download.file(paste0(file.dir, file), "Ex.h5") # load data, process with Seurat library(Seurat) E = Read10X_h5(filename = "Ex.h5") pbmc <- CreateSeuratObject(counts = E, project = "pbmc") pbmc <- SCTransform(pbmc) pbmc <- RunPCA(pbmc, verbose = FALSE) pbmc <- RunUMAP(pbmc, dims = 1:30, verbose = FALSE) pbmc <- FindNeighbors(pbmc, dims = 1:30, verbose = FALSE) # classify cells labels = SignacFast(E = pbmc) celltypes = GenerateLabels(labels, E = pbmc) # add labels to Seurat object, visualize lbls <- factor(celltypes$CellStates) levels(lbls) <- sort(unique(lbls)) pbmc <- AddMetaData(pbmc, metadata=celltypes$CellStates, col.name = "celltypes") pbmc <- SetIdent(pbmc, value='celltypes') DimPlot(pbmc, label = T) # save results saveRDS(pbmc, "pbmcs.rds") saveRDS(celltypes, "celltypes.rds") }