Type:	Package
Title:	Cell DiffErential Expression by Pooling ('CellDEEP')
Version:	1.0.1
Description:	Pool cells together before running differentially expression (DE) analysis. Tell 'CellDEEP' how many cells you want to pool together (which shall be determined by the overall cell number of data), then run DE analysis. Cheng et al. (2026) <doi:10.64898/2026.03.09.710522>.
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Imports:	Seurat
Suggests:	knitr, rmarkdown, testthat (≥ 3.2.3)
Config/testthat/edition:	3
VignetteBuilder:	knitr
Depends:	R (≥ 3.5)
NeedsCompilation:	no
Packaged:	2026-03-24 16:10:07 UTC; andrewmccluskey
Author:	Yiyi Cheng [aut, cre]
Maintainer:	Yiyi Cheng <2593244c@student.gla.ac.uk>
Repository:	CRAN
Date/Publication:	2026-03-29 15:30:02 UTC

K-means Based Cell Pooling for Seurat Objects

Description

Pools cells into "pseudocells" by applying k-means clustering to PCA embeddings. This reduces data sparsity while maintaining the biological grouping of sample, cluster, and condition.

Usage

CellDEEP.Kmean(
  dataset,
  n_cells = 10,
  nstart = 100,
  assay_name = "RNA",
  readcounts = "mean",
  min_cells_per_subgroup = 25
)

Arguments

Value

A new Seurat object where each "cell" is a pooled group of original cells.

Note

This function requires that PCA has already been run on the input dataset, as it uses the "pca" reduction for clustering.

Examples

data("sim")
pool_input <- prepare_data(
  sim,
  sample_id = "DonorID",
  group_id = "Status",
  cluster_id = "cluster_id"
)

pooled_kmean <- CellDEEP.Kmean(
  pool_input,
  readcounts = "sum",
  n_cells = 3,
  min_cells_per_subgroup = 1,
  assay_name = "RNA"
)
pooled_kmean

Random Cell Pooling for Seurat Objects

Description

Pools cells into pseudocells by random selection within biological groups. Includes a minimum threshold filter of 25 cells per subgroup to ensure pooling quality.

Usage

CellDEEP.Random(
  dataset,
  n_cells = 10,
  assay_name = "RNA",
  min_cells_per_subgroup = 25,
  readcounts = "mean"
)

Arguments

Value

A new Seurat object containing the aggregated pseudocells.

Note

Subgroups (sample-cluster combinations) with fewer than 25 cells are automatically skipped. The function also generates a DimPlot to visualize the random pooling across samples.

Examples

data("sim")
pool_input <- prepare_data(
  sim,
  sample_id = "DonorID",
  group_id = "Status",
  cluster_id = "cluster_id"
)

pooled_random <- CellDEEP.Random(
  pool_input,
  readcounts = "sum",
  n_cells = 3,
  min_cells_per_subgroup = 1,
  assay_name = "RNA"
)
pooled_random

Differential Expression with Optional Cell Pooling

Description

It can run Seurat DE directly or first aggregate cells into metacells using CellDEEP pooling.

Usage

FindMarker.CellDEEP(
  object,
  ident.1 = NULL,
  ident.2 = NULL,
  group.by = "group_id",
  sample_id = NULL,
  group_id = NULL,
  cluster_id = NULL,
  prepare = TRUE,
  test.use = "wilcox",
  Pool = TRUE,
  readcounts = "sum",
  n_cells = 10,
  assay = "RNA",
  min_cells_per_subgroup = 25,
  cell_selection = "kmean",
  name.only = TRUE,
  logfc.threshold = 0.25,
  min.pct = 0.01,
  p_cutoff = 0.05,
  full_list = FALSE,
  ...
)

Arguments

Value

A vector of gene names or a DE data.frame.

Standardize Seurat Metadata for CellDEEP

Description

Standardizes metadata columns to sample_id, group_id, and cluster_id so CellDEEP functions can run consistently.

Usage

prepare_data(
  Subset.Seurat,
  assay = "RNA",
  sample_id,
  group_id,
  cluster_id,
  file_path = NULL
)

Arguments

Value

A Seurat object with standardized metadata fields.

Perform Differential Expression and Filter Results

Description

A wrapper for Seurat::FindMarkers that simplifies the extraction of Differentially Expressed (DE) genes. It supports p-value filtering and can return either gene names or a full results table.

Usage

return.DE(
  dataset,
  test.use = "wilcox",
  DE.ident.1,
  DE.ident.2,
  DE.group,
  assay = "RNA",
  p_cutoff = 0.05,
  name.only = TRUE,
  logfc.threshold = 0.25,
  min.pct = 0.01,
  full_list = FALSE,
  ...
)

Arguments

Value

A character vector of genes or a marker data.frame.

Sample simulated cells from muscat package

Description

A dataset containing 200 simulated cells(100 per group) for demonstrating CellDEEP functions. Can be found at doi:10.5281/zenodo.18863779

Usage

data(sim)

Format

A Seurat object

Source

simulated data with muscat package