diff --git a/CHANGELOG.md b/CHANGELOG.md
index f3033975..9c211678 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -29,6 +29,7 @@
 
 * Added `method/limma_removebatcheffect` component (PR #79).
 
+* Added ComBat-Seq method (PR #55).
 * Added `methods/scmerge2` component (PR #63).
 
 ## Minor changes
diff --git a/_viash.yaml b/_viash.yaml
index f8da6a98..090573ea 100644
--- a/_viash.yaml
+++ b/_viash.yaml
@@ -130,6 +130,11 @@ authors:
     info:
       github: liuwd15
       orcid: 0000-0002-5124-9338
+  - name: Maximilien Colange
+    roles: [contributor]
+    info:
+      github: EpigeneMax
+      orcid: 0000-0003-4769-3302
 config_mods: |
   .runners[.type == "nextflow"].config.labels := { lowmem : "memory = 20.Gb", midmem : "memory = 50.Gb", highmem : "memory = 100.Gb", lowcpu : "cpus = 5", midcpu : "cpus = 15", highcpu : "cpus = 30", lowtime : "time = 1.h", midtime : "time = 4.h", hightime : "time = 8.h", veryhightime : "time = 24.h" }
 
diff --git a/src/methods/combat_seq/config.vsh.yaml b/src/methods/combat_seq/config.vsh.yaml
new file mode 100644
index 00000000..f8273cfe
--- /dev/null
+++ b/src/methods/combat_seq/config.vsh.yaml
@@ -0,0 +1,52 @@
+__merge__: /src/api/comp_method.yaml
+name: combat_seq
+label: ComBat-Seq
+summary: Adjusting batch effects in RNA-Seq expression data using empirical Bayes
+  methods
+description: |
+  ComBat-Seq extends the ComBat method for batch correction in RNA-Seq data.
+  While ComBat assumes normally distributed data, ComBat-Seq uses a negative
+  binomial distribution to model the data.  While initially developed for
+  RNA-Seq data, ComBat-Seq can be applied to single-cell RNA-Seq data as well.
+
+  The method is implemented in Python as a part of the inmoose package.  It is
+  based on the original R implementation, distributed through the sva package.
+
+references:
+  doi:
+    - 10.1093/nargab/lqaa078
+    - 10.1186/s12859-023-05578-5
+
+links:
+  documentation: https://inmoose.readthedocs.io/en/stable/pycombatseq.html
+  repository: https://github.com/epigenelabs/inmoose
+
+# Metadata for your component
+info:
+  # Which normalisation method this component prefers to use (required).
+  preferred_normalization: counts
+  method_types: [feature]
+
+# Resources required to run the component
+resources:
+  - type: python_script
+    path: script.py
+  - path: /src/utils/read_anndata_partial.py
+
+engines:
+  # Specifications for the Docker image for this component.
+  - type: docker
+    image: openproblems/base_python:1
+    # Add custom dependencies here (optional). For more information, see
+    # https://viash.io/reference/config/engines/docker/#setup .
+    setup:
+      - type: python
+        pip: inmoose
+
+runners:
+  # This platform allows running the component natively
+  - type: executable
+  # Allows turning the component into a Nextflow module / pipeline.
+  - type: nextflow
+    directives:
+      label: [midtime,midmem,midcpu]
diff --git a/src/methods/combat_seq/script.py b/src/methods/combat_seq/script.py
new file mode 100644
index 00000000..f86c06dd
--- /dev/null
+++ b/src/methods/combat_seq/script.py
@@ -0,0 +1,41 @@
+import sys
+
+import anndata as ad
+import numpy as np
+from inmoose.pycombat import pycombat_seq
+from scipy.sparse import csr_matrix, issparse
+
+# VIASH START
+# Note: this section is auto-generated by viash at runtime. To edit it, make changes
+# in config.vsh.yaml and then run `viash config inject config.vsh.yaml`.
+par = {"input": "resources_test/.../input.h5ad", "output": "output.h5ad"}
+meta = {"name": "combat_seq"}
+# VIASH END
+
+sys.path.append(meta["resources_dir"])
+from read_anndata_partial import read_anndata
+
+print("Read input", flush=True)
+adata = read_anndata(par["input"], X="layers/counts", obs="obs", var="var", uns="uns")
+
+print("Run Combat-Seq", flush=True)
+# pycombat_seq wants a dense genes x cells matrix
+counts = (adata.X.toarray() if issparse(adata.X) else adata.X).T.astype(np.double)
+corrected_counts = pycombat_seq(counts, adata.obs["batch"])
+
+print("Store output", flush=True)
+output = ad.AnnData(
+    obs=adata.obs[[]],
+    var=adata.var[[]],
+    uns={
+        "dataset_id": adata.uns["dataset_id"],
+        "normalization_id": adata.uns["normalization_id"],
+        "method_id": meta["name"],
+    },
+    layers={
+        "corrected_counts": csr_matrix(corrected_counts.T),
+    },
+)
+
+print("Write output to file", flush=True)
+output.write_h5ad(par["output"], compression="gzip")
diff --git a/src/workflows/run_benchmark/config.vsh.yaml b/src/workflows/run_benchmark/config.vsh.yaml
index 891abab5..ae2be6cc 100644
--- a/src/workflows/run_benchmark/config.vsh.yaml
+++ b/src/workflows/run_benchmark/config.vsh.yaml
@@ -93,6 +93,7 @@ dependencies:
   - name: methods/bbknn
   - name: methods/cellplm
   - name: methods/combat
+  - name: methods/combat_seq
   - name: methods/density_adaptive
   - name: methods/fadvi
   - name: methods/geneformer
diff --git a/src/workflows/run_benchmark/main.nf b/src/workflows/run_benchmark/main.nf
index 4f564571..2f5bf7c9 100644
--- a/src/workflows/run_benchmark/main.nf
+++ b/src/workflows/run_benchmark/main.nf
@@ -23,6 +23,7 @@ methods = [
     args: [model: file("s3://openproblems-work/cache/cellplm-ckpt.zip")]
   ),
   combat,
+  combat_seq,
   density_adaptive,
   fadvi,
   geneformer,