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: Which assay to use. If NULL, the default assay of the Seurat object will be used.
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 print the message. Default is TRUE.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 07:53:57] Start standard scop workflow...
#> ℹ [2026-01-27 07:53:58] Checking a list of <Seurat>...
#> ! [2026-01-27 07:53:58] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 07:53:58] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 07:54:00] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 07:54:01] Use the separate HVF from srt_list
#> ℹ [2026-01-27 07:54:01] Number of available HVF: 2000
#> ℹ [2026-01-27 07:54:01] Finished check
#> ℹ [2026-01-27 07:54:01] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 07:54:01] Perform pca linear dimension reduction
#> ℹ [2026-01-27 07:54:02] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 07:54:02] Reorder clusters...
#> ℹ [2026-01-27 07:54:03] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 07:54:03] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 07:54:06] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 07:54:10] Run scop standard workflow completed
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 [2026-01-27 07:54:12] Data type is log-normalized
#> ℹ [2026-01-27 07:54:12] The data is presumed to be log-normalized
#> ℹ [2026-01-27 07:54:12] Perform "expm1" on the raw data
new_counts <- GetAssayData5(
  pancreas_sub,
  assay = "RNA",
  layer = "counts"
)
identical(raw_counts, new_counts)
#> [1] TRUE

Attempt to recover raw counts from the normalized matrix

Usage

Arguments

See also

Examples