Attempt to recover raw counts from the normalized matrix.

Usage

RecoverCounts(
  srt,
  assay = NULL,
  trans = c("expm1", "exp", "none"),
  min_count = c(1, 2, 3),
  tolerance = 0.1,
  sf = NULL,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
assay: Name of assay to recover counts.
trans: The transformation function to applied when data is presumed to be log-normalized.
min_count: Minimum UMI count of genes.
tolerance: When recovering the raw counts, the nCount of each cell is theoretically calculated as an integer. However, due to decimal point preservation during normalization, the calculated nCount is usually a floating point number close to the integer. The tolerance is its difference from the integer. Default is 0.1
sf: Set the scaling factor manually.
verbose: Whether to show messages.

Examples

data("pancreas_sub")
raw_counts <- GetAssayData5(
  pancreas_sub,
  assay = "RNA",
  layer = "counts"
)

# Normalized the data
pancreas_sub <- Seurat::NormalizeData(pancreas_sub)
#> Normalizing layer: counts

# Now replace counts with the log-normalized data matrix
data <- GetAssayData5(
  pancreas_sub,
  assay = "RNA",
  layer = "data"
)
new_pancreas_sub <- SeuratObject::SetAssayData(
  object = pancreas_sub,
  layer = "counts",
  new.data = data,
  assay = "RNA"
)
# Recover the counts and compare with the raw counts matrix
pancreas_sub <- RecoverCounts(new_pancreas_sub)
#> ℹ [2025-07-02 02:54:30] The data is presumed to be log-normalized.
#> ℹ [2025-07-02 02:54:30] Perform expm1 on the raw data.
#> ℹ [2025-07-02 02:54:30] The presumed scale factor: 10000
new_counts <- GetAssayData5(
  pancreas_sub,
  assay = "RNA",
  layer = "counts"
)
identical(raw_counts, new_counts)
#> [1] TRUE