diff --git a/src/methods/drvi/config.vsh.yaml b/src/methods/drvi/config.vsh.yaml
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+++ b/src/methods/drvi/config.vsh.yaml
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+# The API specifies which type of component this is.
+# It contains specifications for:
+#   - The input/output files
+#   - Common parameters
+#   - A unit test
+__merge__: ../../api/comp_method.yaml
+
+# A unique identifier for your component (required).
+# Can contain only lowercase letters or underscores.
+name: drvi
+# A relatively short label, used when rendering visualisations (required)
+label: DRVI
+# A one sentence summary of how this method works (required). Used when 
+# rendering summary tables.
+summary: "DrVI is an unsupervised generative model capable of learning non-linear interpretable disentangled latent representations from single-cell count data."
+# A multi-line description of how this component works (required). Used
+# when rendering reference documentation.
+description: |
+  Disentangled Representation Variational Inference (DRVI) is an unsupervised deep generative model designed for integrating single-cell RNA sequencing (scRNA-seq) data across different batches.
+  It extends the variational autoencoder (VAE) framework by learning a latent representation that captures biological variation while disentangling and correcting for batch effects.
+  DRVI conditions both the encoder and decoder on batch covariates, allowing it to explicitly model and mitigate batch-specific variations during training.
+  By incorporating a KL-divergence regularization term, it balances data reconstruction with latent space structure, resulting in a unified embedding where similar cells cluster together regardless of batch.
+references:
+  doi: 
+    - 10.1101/2024.11.06.622266
+#   bibtex:
+#     - |
+#       @article{foo,
+#         title={Foo},
+#         author={Bar},
+#         journal={Baz},
+#         year={2024}
+#       }
+links:
+  # URL to the documentation for this method (required).
+  documentation: https://drvi.readthedocs.io/latest/index.html
+  # URL to the code repository for this method (required).
+  repository: https://github.com/theislab/DRVI?tab=readme-ov-file
+
+
+
+# Metadata for your component
+info:
+  # Which normalisation method this component prefers to use (required).
+  preferred_normalization: counts
+
+# Component-specific parameters (optional)
+arguments:
+  - name: --n_hvg
+    type: integer
+    default: 2000
+    description: Number of highly variable genes to use.
+  - name: --n_epochs
+    type: integer
+    default: 400
+    description: Number of epochs
+
+# Resources required to run the component
+resources:
+  # The script of your component (required)
+  - type: python_script
+    path: script.py
+  - path: /src/utils/read_anndata_partial.py
+
+  # Additional resources your script needs (optional)
+  # - type: file
+  #   path: weights.pt
+
+engines:
+  # Specifications for the Docker image for this component.
+  - type: docker
+    image: openproblems/base_pytorch_nvidia:1.0.0 
+    # Add custom dependencies here (optional). For more information, see
+    # https://viash.io/reference/config/engines/docker/#setup .
+    setup:
+      - type: python
+        pypi:
+        - drvi-py==0.1.7
+        - torch==2.3.0
+        - torchvision==0.18.0
+        # packages:
+
+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,lowcpu,gpu]
diff --git a/src/methods/drvi/script.py b/src/methods/drvi/script.py
new file mode 100644
index 00000000..f7f2f1ea
--- /dev/null
+++ b/src/methods/drvi/script.py
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+import anndata as ad
+import scanpy as sc
+import drvi
+from drvi.model import DRVI
+from drvi.utils.misc import hvg_batch
+import pandas as pd
+import numpy as np
+import warnings
+import sys
+import scipy.sparse
+
+## 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/task_batch_integration/cxg_immune_cell_atlas/dataset.h5ad',
+  'output': 'output.h5ad',
+  'n_hvg': 2000,
+  'n_epochs': 400
+}
+meta = {
+  'name': 'drvi'
+}
+## VIASH END
+
+sys.path.append(meta["resources_dir"])
+from read_anndata_partial import read_anndata
+
+print('Reading input files', flush=True)
+adata = read_anndata(
+    par['input'],
+    X='layers/counts',
+    obs='obs',
+    var='var',
+    uns='uns'
+)
+# Remove dataset with non-count values
+counts = adata.X
+import scipy.sparse
+
+if scipy.sparse.issparse(counts):
+    counts_dense = counts.toarray()
+else:
+    counts_dense = counts
+
+if not np.allclose(counts_dense, np.round(counts_dense)):
+    warnings.warn("Non-integer values detected. Rounding to nearest integer.")
+    adata.X = np.round(counts_dense).astype(int)
+
+adata.layers["counts"] = adata.X.copy()
+
+if par["n_hvg"]: 
+     print(f"Select top {par['n_hvg']} high variable genes", flush=True) 
+     idx = adata.var["hvg_score"].to_numpy().argsort()[::-1][:par["n_hvg"]] 
+     adata = adata[:, idx].copy() 
+
+print('Train model with DRVI', flush=True)
+# Setup data
+DRVI.setup_anndata(
+    adata,
+    # DRVI accepts count data by default.
+    # Do not forget to change gene_likelihood if you provide a non-count data.
+    layer="counts",
+    # Always provide a list. DRVI can accept multiple covariates.
+    categorical_covariate_keys=["batch"],
+    # DRVI accepts count data by default.
+    # Set to false if you provide log-normalized data and use normal distribution (mse loss).
+    is_count_data=False,
+)
+
+# construct the model
+model = DRVI(
+    adata,
+    # Provide categorical covariates keys once again. Refer to advanced usages for more options.
+    categorical_covariates=["batch"],
+    n_latent=128,
+    # For encoder and decoder dims, provide a list of integers.
+    encoder_dims=[128, 128],
+    decoder_dims=[128, 128],
+)
+model
+
+# train the model
+model.train(
+    max_epochs=par["n_epochs"],
+    early_stopping=False,
+    early_stopping_patience=20,
+    # mps
+    # accelerator="mps", devices=1,
+    # cpu
+    # accelerator="cpu", devices=1,
+    # gpu: no additional parameter
+    #
+    # No need to provide `plan_kwargs` if n_epochs >= 400.
+    plan_kwargs={
+        "n_epochs_kl_warmup": par["n_epochs"],
+    },
+)
+
+embed = ad.AnnData(model.get_latent_representation(), obs=adata.obs)
+
+print("Store outputs", flush=True)
+output = ad.AnnData(
+    obs=adata.obs.copy(),
+    var=adata.var.copy(),
+    obsm={
+        "X_emb": model.get_latent_representation(),
+    },
+    uns={
+        "dataset_id": adata.uns.get("dataset_id", "unknown"),
+        "normalization_id": adata.uns.get("normalization_id", "unknown"),
+        "method_id": meta["name"],
+    },
+)
+
+print("Write output AnnData to file", flush=True)
+output.write_h5ad(par['output'], compression='gzip')