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Project Query Data Onto SIR Space of Reference Data

Source: R/projectSIR.R
projectSIR.Rd

This function projects a query SingleCellExperiment object onto the SIR (supervised independent component) space of a reference SingleCellExperiment object. The SVD of the reference data is computed on conditional means per cell type, and the query data is projected based on these reference components.

Usage

projectSIR(
  query_data,
  reference_data,
  query_cell_type_col,
  ref_cell_type_col,
  cell_types = NULL,
  multiple_cond_means = TRUE,
  cumulative_variance_threshold = 0.7,
  n_neighbor = 1,
  assay_name = "logcounts",
  max_cells_query = 5000,
  max_cells_ref = 5000
)

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

A character string specifying the column in the colData of query_data that identifies the cell types.

ref_cell_type_col

A character string specifying the column in the colData of reference_data that identifies the cell types.

cell_types

A character vector of cell types for which to compute conditional means in the reference data.

multiple_cond_means

A logical value indicating whether to compute multiple conditional means per cell type (through PCA and clustering). Defaults to TRUE.

cumulative_variance_threshold

A numeric value between 0 and 1 specifying the variance threshold for PCA when computing multiple conditional means. Defaults to 0.7.

n_neighbor

An integer specifying the number of nearest neighbors for clustering when computing multiple conditional means. Defaults to 1.

assay_name

A character string specifying the assay name 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 5000.

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 5000.

Value

A list containing:

cond_means

A matrix of the conditional means computed for the reference data.

rotation_mat

The rotation matrix obtained from the SVD of the conditional means.

sir_projections

A data.frame containing the SIR projections for both the reference and query datasets.

percent_var

The percentage of variance explained by each component of the SIR projection.

Details

The genes used for the projection (SVD) must be present in both the reference and query datasets. The function first computes conditional means for each cell type in the reference data, then performs SVD on these conditional means to obtain the rotation matrix used for projecting both the reference and query datasets. The query data is centered and scaled based on the reference data.

Author

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

Examples

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

# Project the query data onto SIR space of reference
sir_output <- projectSIR(query_data = query_data,
                         reference_data = reference_data,
                         query_cell_type_col = "SingleR_annotation",
                         ref_cell_type_col = "expert_annotation")