scagg

Pseudobulk and metacell aggregation for single-cell RNA-seq

scagg provides three aggregation strategies for turning single-cell data into sample-level or metacell-level objects, ready for bulk-style differential expression (DESeq2, edgeR, dream) or other downstream analyses:

Strategy	Function	When to use
Pseudobulk	make_pseudobulk	One aggregate per unique sample × cell-type combination
Metacells by size	make_metacells_by_size	~N cells per metacell, maximises usage of all cells
Metacells by count	make_metacells_by_num	Exactly N metacells per group, balances replication

Available for Python (AnnData/scanpy) and R (Seurat). Full cell → metacell traceability is built in.

---

Installation

Python

pip install git+https://github.com/ethanfenton/scagg.git

Requirements: Python ≥ 3.10, anndata, numpy, pandas, scipy (all present in any standard scanpy environment).

R

if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("ethanfenton/scagg", subdir = "R/scagg")

Requirements: Seurat, dplyr.

---

Quick start — Python

import scanpy as sc
import scagg

adata = sc.read_h5ad("my_data.h5ad")

# True pseudobulk: one obs per sample × cell_type
pb = scagg.make_pseudobulk(
    adata,
    group_vars = ["cell_type", "sample"],
    save_membership = "results/pseudobulk_membership.csv",
)

# Metacells of ~10 cells each
mc_size = scagg.make_metacells_by_size(
    adata,
    group_vars = ["cell_type", "sample"],
    cell_size  = 10,
    save_membership = "results/metacell_size10_membership.csv",
)

# Exactly 5 metacells per cell_type × sample group
mc_num = scagg.make_metacells_by_num(
    adata,
    group_vars  = ["cell_type", "sample"],
    n_metacells = 5,
    save_membership = "results/metacell_num5_membership.csv",
)

Access results

# Result is a standard AnnData
mc_size.obs          # metacell metadata (n_cells, cell_type, sample, …)
mc_size.X            # summed counts matrix (metacells × genes)
mc_size.uns["metacell_membership"]  # DataFrame: cell_barcode → metacell_id

CLI

# Pseudobulk
scagg pseudobulk \
  --input data.h5ad --output results/pb.h5ad \
  --group-vars cell_type sample \
  --save-membership results/pb_membership.csv

# Metacells by size
scagg metacells-by-size \
  --input data.h5ad --output results/mc.h5ad \
  --group-vars cell_type sample \
  --cell-size 10 \
  --save-membership results/mc_membership.csv

# Metacells by count
scagg metacells-by-num \
  --input data.h5ad --output results/mc.h5ad \
  --group-vars cell_type sample \
  --n-metacells 5

---

Quick start — R (Seurat)

library(scagg)

# True pseudobulk
res <- make_pseudobulk(
  so_obj     = seurat_obj,
  group_vars = c("cell_type", "sample"),
  assays     = c("SCT", "RNA"),
  save_membership = "results/pseudobulk_membership.csv"
)
pb_so      <- res$obj         # aggregated Seurat object
membership <- res$membership  # data.frame: barcode → metacell_id

# Metacells of ~10 cells
res <- make_metacells_by_size(
  so_obj     = seurat_obj,
  group_vars = c("cell_type", "sample"),
  cell_size  = 10,
  save_membership = "results/mc_size10_membership.csv"
)

# Exactly 5 metacells per group
res <- make_metacells_by_num(
  so_obj      = seurat_obj,
  group_vars  = c("cell_type", "sample"),
  n_metacells = 5
)

Carry custom metadata

res <- make_metacells_by_size(
  so_obj     = seurat_obj,
  group_vars = c("cell_type", "sample"),
  cell_size  = 10,
  meta_vars  = c("Treatment", "Timepoint", "sex", "Prepper", "batch")
)

---

API reference

Python

scagg.make_pseudobulk(adata, group_vars, *, meta_vars, layer, save_membership)
scagg.make_metacells_by_size(adata, group_vars, cell_size, *, cell_min, meta_vars,
                              layer, seed, save_membership)
scagg.make_metacells_by_num(adata, group_vars, n_metacells, *, min_cells, meta_vars,
                             layer, seed, save_membership)

Parameter	Default	Notes
group_vars	required	One or more obs column names
cell_size	required	Target cells per metacell
cell_min	70 % of cell_size	Min cells to include a group
n_metacells	required	Metacells per group
min_cells	n_metacells	Min cells to include a group
meta_vars	all obs columns	Which metadata to carry forward
layer	None (use X)	AnnData layer to aggregate
seed	42	RNG seed for reproducibility
save_membership	None	CSV path for traceability output

R

make_pseudobulk(so_obj, group_vars, meta_vars, assays, save_membership,
                idents_col = NULL)
make_metacells_by_size(so_obj, group_vars, cell_size, cell_min, meta_vars,
                       assays, seed, save_membership, idents_col = NULL)
make_metacells_by_num(so_obj, group_vars, n_metacells, min_cells, meta_vars,
                      assays, seed, save_membership, idents_col = NULL)

idents_col: name of any column in the result's metadata to set as the active Seurat Idents. NULL (default) leaves Idents unset. The column can be a pre-existing metadata column (e.g. "cell_type") or one you derive after aggregation:

All R functions return a named list:

• $obj — aggregated Seurat object
• $membership — data.frame mapping every input barcode to its metacell ID

---

Traceability output

Every function writes (optionally) a CSV with one row per input cell:

cell_barcode, metacell_id, cell_type, sample, Treatment, ...
AAACCCAGTCCGAACC-1, mc3_A_s1, ExN, s1, KO, ...
AAACCCATCAGCTTGC-1, mc3_A_s1, ExN, s1, KO, ...
AAACGAAGTCCTGTAG-1, mc1_B_s2, InN, s2, WT, ...
AAAGGATAGCTCCATG-1, unassigned, ExN, s3, KO, ...   ← below cell_min threshold

The metacell_id column uses the format mc{N}_{group_vars_joined}. Cells below the minimum threshold are marked unassigned.

---

Choosing a strategy

Pseudobulk is the gold standard for DE testing (recommended when you have ≥ 4 biological replicates per condition). It produces one observation per sample × cell-type and is unambiguous.

Metacells by size is best when samples have variable cell counts and you want to maximise the number of metacells from well-represented groups while naturally excluding sparse ones (below cell_min).

Metacells by count is best when you want equal replication across all groups regardless of cell count, e.g. for downstream tools that assume balanced designs.

In all cases, expression is aggregated by summing raw counts. If you are using normalised/scaled values (SCT slot), pass layer= pointing to the raw counts layer for biologically meaningful aggregation.

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Migrating from your existing code

If you were using make_metacells_by_size(so_obj, cell_size=10) with hardcoded ct3/sample columns and a hardcoded time_treat Idents:

# Old (hardcoded ct3/sample, hardcoded time_treat Idents)
metacell_so <- make_metacells_by_size(so_obj, cell_size = 10)

# New (scagg, generalised)
res <- make_metacells_by_size(
  so_obj,
  group_vars = c("ct3", "sample"),
  cell_size  = 10,
  meta_vars  = c("Treatment", "Timepoint", "sex", "Prepper"),
  assays     = "SCT"
)
metacell_so <- res$obj

# Add a derived column, then point Idents at it
metacell_so$time_treat <- paste(metacell_so$Timepoint, metacell_so$Treatment, sep = "_")
Seurat::Idents(metacell_so) <- "time_treat"

# OR — pass idents_col directly if the column already exists in meta_vars:
res <- make_metacells_by_size(
  so_obj,
  group_vars = c("ct3", "sample"),
  cell_size  = 10,
  meta_vars  = c("Treatment", "Timepoint", "sex", "Prepper"),
  idents_col = "ct3"   # set Idents to cell type
)

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License

MIT © 2026 Ethan Fenton