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Create .cells Object from Count Matrices

Source: R/preprocess.R
get.cells.list.mat.Rd

Converts a list of count matrices into tinydenseR's standardized .cells format. Each matrix represents one sample and is saved as a temporary RDS file for memory-efficient processing. Use this when you already have count data loaded in R memory.

Usage

get.cells.list.mat(.count.mat.list, .meta, .compress = FALSE, .verbose = TRUE)

Arguments

.count.mat.list

Named list of count matrices, one per sample. Names must match rownames(.meta). Each matrix can be dense, sparse (dgCMatrix), or similar matrix-like object with features as rows and cells as columns.

.meta

Data frame with sample-level metadata. Rownames must match names(.count.mat.list). Should include experimental variables like Condition, Replicate, etc.

.compress

Logical: compress RDS files? Default FALSE for faster I/O. Set TRUE to save disk space for large datasets.

.verbose

Logical: print progress messages? Default TRUE.

Value

Named list of file paths to temporary RDS files, one per sample. Structure suitable for setup.tdr.obj(.cells = ...).

Details

This function creates temporary RDS files containing each sample's count matrix. These files persist for the R session and allow tinydenseR to process data without loading all samples into memory simultaneously.

All matrices in .count.mat.list should have:

  • Same feature names (rownames) across samples

  • Cell barcodes as column names

  • Raw or normalized counts (specify via setup.tdr.obj(.assay.type = ...))

See also

get.cells for automatic format detection, get.cells.SCE for SingleCellExperiment input, setup.tdr.obj for next step in workflow

Examples

if (FALSE) { # \dontrun{
# Load example trajectory data
trajectory_data <- fetch_trajectory_data()
sim_trajectory.meta <- trajectory_data$meta
sim_trajectory <- trajectory_data$SCE
  
# Prepare sample-level metadata
sim_trajectory.meta <- sim_trajectory.meta[, c("Condition", "Replicate", "Sample")] |>
  unique()
rownames(sim_trajectory.meta) <- sim_trajectory.meta$Sample

# Extract count matrices for two samples
count.matrices <- list(
  A_R1 = SingleCellExperiment::counts(sim_trajectory)[, 
           sim_trajectory$Sample == "A_R1"],
  B_R1 = SingleCellExperiment::counts(sim_trajectory)[, 
           sim_trajectory$Sample == "B_R1"]
)

# Create .cells object
cells <- get.cells.list.mat(.count.mat.list = count.matrices,
                            .meta = sim_trajectory.meta[c("A_R1", "B_R1"), ])

# Use in tinydenseR workflow
lm.obj <- setup.tdr.obj(.cells = cells,
                       .meta = sim_trajectory.meta[c("A_R1", "B_R1"), ],
                       .assay.type = "RNA")
} # }