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Project Query Data Onto PCA Space of Reference Data

Source: R/projectPCA.R
projectPCA.Rd

This function projects a query singleCellExperiment object onto the PCA space of a reference singleCellExperiment object. The PCA analysis on the reference data is assumed to be pre-computed and stored within the object. Optionally filters by cell types and downsamples the results.

Usage

projectPCA(
  query_data,
  reference_data,
  query_cell_type_col,
  ref_cell_type_col,
  cell_types = NULL,
  pc_subset = 1:10,
  assay_name = "logcounts",
  max_cells_query = NULL,
  max_cells_ref = NULL
)

Arguments

query_data

A SingleCellExperiment object containing numeric expression matrix for the query cells.

reference_data

A SingleCellExperiment object containing numeric expression matrix for the reference cells.

query_cell_type_col

character. The column name in the colData of query_data that identifies the cell types.

ref_cell_type_col

character. The column name in the colData of reference_data that identifies the cell types.

cell_types

A character vector specifying which cell types to retain in the output. If NULL, no cell type filtering is performed. Default is NULL.

pc_subset

A numeric vector specifying the subset of principal components (PCs) to compare. Default is 1:10.

assay_name

Name of the assay on which to perform computations. Defaults to "logcounts".

max_cells_query

Maximum number of query cells to retain after cell type filtering. If NULL, no downsampling of query cells is performed. Default is NULL.

max_cells_ref

Maximum number of reference cells to retain after cell type filtering. If NULL, no downsampling of reference cells is performed. Default is NULL.

Value

A data.frame containing the projected data in rows (reference and query data combined), optionally filtered by cell types and downsampled. Rownames preserve the original cell names from the SCE objects.

Details

This function assumes that the "PCA" element exists within the reducedDims of the reference data (obtained using reducedDim(reference_data)) and that the genes used for PCA are present in both the reference and query data. It performs centering and scaling of the query data based on the reference data before projection using the FULL datasets to maintain proper mean centering. Cell type filtering and downsampling are performed AFTER projection to preserve the statistical properties of the PCA space. Cell names from the original SCE objects are preserved as rownames in the output.

Author

Anthony Christidis, anthony-alexander_christidis@hms.harvard.edu

Examples

# Load data
data("reference_data")
data("query_data")

# Project the query data onto PCA space of reference
pca_output <- projectPCA(query_data = query_data,
                         reference_data = reference_data,
                         query_cell_type_col = "SingleR_annotation",
                         ref_cell_type_col = "expert_annotation",
                         pc_subset = 1:10)

# Project with cell type filtering and balanced downsampling
pca_output_filtered <- projectPCA(query_data = query_data,
                                  reference_data = reference_data,
                                  query_cell_type_col = "SingleR_annotation",
                                  ref_cell_type_col = "expert_annotation",
                                  pc_subset = 1:5,
                                  cell_types = c("CD4", "CD8"),
                                  max_cells_ref = 1000,
                                  max_cells_query = 1000)