Add geneformer (#7)

* add geneformer

* add to workflow

* inherit from base method

* fix variable name

* fix mapping

* cleanup script

* update info

* minor changes

src/methods/geneformer/config.vsh.yaml

@@ -0,0 +1,46 @@
+__merge__: ../../api/base_method.yaml
+
+name: geneformer
+label: Geneformer
+summary: Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
+description: |
+  Geneformer is a context-aware, attention-based deep learning model pretrained on a large-scale corpus of single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. Here, a pre-trained Geneformer model is fine-tuned and used to predict cell type labels for an unlabelled dataset.
+
+info:
+  preferred_normalization: counts
+
+references: 
+  doi: 
+    - 10.1038/s41586-023-06139-9
+    - 10.1101/2024.08.16.608180
+
+links: 
+  documentation: https://geneformer.readthedocs.io/en/latest/index.html
+  repository: https://huggingface.co/ctheodoris/Geneformer
+
+arguments:
+  - name: "--model"
+    type: "string"
+    description: String representing the Geneformer model to use
+    choices: ["gf-6L-30M-i2048", "gf-12L-30M-i2048", "gf-12L-95M-i4096", "gf-20L-95M-i4096"]
+    default: "gf-12L-95M-i4096"
+
+resources:
+  - type: python_script
+    path: script.py
+
+engines:
+  - type: docker
+    image: openproblems/base_pytorch_nvidia:1.0.0
+    setup:
+      - type: python
+        pip:
+        - pyarrow<15.0.0a0,>=14.0.1
+        - huggingface_hub
+        - git+https://huggingface.co/ctheodoris/Geneformer.git
+
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [midtime, midmem, midcpu, gpu]

src/methods/geneformer/script.py

@@ -0,0 +1,213 @@
+import os
+from tempfile import TemporaryDirectory
+import anndata as ad
+from geneformer import Classifier, TranscriptomeTokenizer, DataCollatorForCellClassification
+from huggingface_hub import hf_hub_download
+import numpy as np
+import datasets
+import pickle
+from transformers import BertForSequenceClassification, Trainer
+
+## VIASH START
+par = {
+  'input_train': 'resources_test/task_label_projection/cxg_immune_cell_atlas/train.h5ad',
+  'input_test': 'resources_test/task_label_projection/cxg_immune_cell_atlas/test.h5ad',
+  'output': 'output.h5ad',
+  "model": "gf-12L-95M-i4096",
+}
+meta = {
+  'name': 'geneformer'
+}
+## VIASH END
+
+n_processors = os.cpu_count()
+
+print('>>> Reading input files', flush=True)
+input_train = ad.read_h5ad(par['input_train'])
+input_test = ad.read_h5ad(par['input_test'])
+
+if input_train.uns["dataset_organism"] != "homo_sapiens":
+  raise ValueError(
+    f"Geneformer can only be used with human data "
+    f"(dataset_organism == '{input_train.uns['dataset_organism']}')"
+  )
+
+is_ensembl = all(var_name.startswith("ENSG") for var_name in input_train.var_names)
+if not is_ensembl:
+  raise ValueError(f"Geneformer requires input_train.var_names to contain ENSEMBL gene ids")
+
+print(f">>> Getting settings for model '{par['model']}'...", flush=True)
+model_split = par["model"].split("-")
+model_details = {
+  "layers": model_split[1],
+  "dataset": model_split[2],
+  "input_size": int(model_split[3][1:]),
+}
+print(model_details, flush=True)
+
+print(">>> Getting model dictionary files...", flush=True)
+if model_details["dataset"] == "95M":
+  dictionaries_subfolder = "geneformer"
+elif model_details["dataset"] == "30M":
+  dictionaries_subfolder = "geneformer/gene_dictionaries_30m"
+else:
+  raise ValueError(f"Invalid model dataset: {model_details['dataset']}")
+print(f"Dictionaries subfolder: '{dictionaries_subfolder}'")
+
+dictionary_files = {
+  "ensembl_mapping": hf_hub_download(
+    repo_id="ctheodoris/Geneformer",
+    subfolder=dictionaries_subfolder,
+    filename=f"ensembl_mapping_dict_gc{model_details['dataset']}.pkl",
+  ),
+  "gene_median": hf_hub_download(
+    repo_id="ctheodoris/Geneformer",
+    subfolder=dictionaries_subfolder,
+    filename=f"gene_median_dictionary_gc{model_details['dataset']}.pkl",
+  ),
+  "gene_name_id": hf_hub_download(
+    repo_id="ctheodoris/Geneformer",
+    subfolder=dictionaries_subfolder,
+    filename=f"gene_name_id_dict_gc{model_details['dataset']}.pkl",
+  ),
+  "token": hf_hub_download(
+    repo_id="ctheodoris/Geneformer",
+    subfolder=dictionaries_subfolder,
+    filename=f"token_dictionary_gc{model_details['dataset']}.pkl",
+  ),
+}
+
+print(">>> Creating working directory...", flush=True)
+work_dir = TemporaryDirectory()
+
+input_train_dir = os.path.join(work_dir.name, "input_train")
+os.makedirs(input_train_dir)
+tokenized_train_dir = os.path.join(work_dir.name, "tokenized_train")
+os.makedirs(tokenized_train_dir)
+classifier_train_dir = os.path.join(work_dir.name, "classifier_train")
+os.makedirs(classifier_train_dir)
+classifier_fine_tuned_dir = os.path.join(work_dir.name, "classifier_fine_tuned")
+os.makedirs(classifier_fine_tuned_dir)
+input_test_dir = os.path.join(work_dir.name, "input_test")
+os.makedirs(input_test_dir)
+tokenized_test_dir = os.path.join(work_dir.name, "tokenized_test")
+os.makedirs(tokenized_test_dir)
+
+print(f"Working directory: '{work_dir.name}'", flush=True)
+
+print(f">>> Getting model files for model '{par['model']}'...", flush=True)
+model_files = {
+  "model": hf_hub_download(
+    repo_id="ctheodoris/Geneformer",
+    subfolder=par["model"],
+    filename="model.safetensors",
+  ),
+  "config": hf_hub_download(
+    repo_id="ctheodoris/Geneformer",
+    subfolder=par["model"],
+    filename="config.json",
+  ),
+}
+model_dir = os.path.dirname(model_files["model"])
+
+print(">>> Preparing input data...", flush=True)
+input_train.X = input_train.layers["counts"]
+input_train.var["ensembl_id"] = input_train.var["feature_id"]
+input_train.obs["n_counts"] = input_train.layers["counts"].sum(axis=1)
+input_train.obs["celltype"] = input_train.obs["label"]
+num_types = len(input_train.obs["celltype"].unique())
+input_train.write_h5ad(os.path.join(input_train_dir, "input_train.h5ad"))
+
+input_test.X = input_test.layers["counts"]
+input_test.var["ensembl_id"] = input_test.var["feature_id"]
+input_test.obs["n_counts"] = input_test.layers["counts"].sum(axis=1)
+input_test.write_h5ad(os.path.join(input_test_dir, "input_test.h5ad"))
+
+print(">>> Tokenizing train data...", flush=True)
+special_token = model_details["dataset"] == "95M"
+print(f"Input size: {model_details['input_size']}, Special token: {special_token}")
+tokenizer = TranscriptomeTokenizer(
+  custom_attr_name_dict={"celltype": "celltype"},
+  nproc=n_processors,
+  model_input_size=model_details["input_size"],
+  special_token=special_token,
+  gene_median_file=dictionary_files["gene_median"],
+  token_dictionary_file=dictionary_files["token"],
+  gene_mapping_file=dictionary_files["ensembl_mapping"],
+)
+tokenizer.tokenize_data(input_train_dir, tokenized_train_dir, "tokenized", file_format="h5ad")
+
+print(">>> Tokenizing test data...", flush=True)
+special_token = model_details["dataset"] == "95M"
+print(f"Input size: {model_details['input_size']}, Special token: {special_token}")
+tokenizer = TranscriptomeTokenizer(
+  nproc=n_processors,
+  model_input_size=model_details["input_size"],
+  special_token=special_token,
+  gene_median_file=dictionary_files["gene_median"],
+  token_dictionary_file=dictionary_files["token"],
+  gene_mapping_file=dictionary_files["ensembl_mapping"],
+)
+tokenizer.tokenize_data(input_test_dir, tokenized_test_dir, "tokenized", file_format="h5ad")
+
+print(">>> Fine-tuning pre-trained geneformer model for cell state classification...", flush=True)
+cc = Classifier(
+  classifier="cell",
+  cell_state_dict = {"state_key": "celltype", "states": "all"},
+  nproc=n_processors,
+  token_dictionary_file=dictionary_files["token"],
+  num_crossval_splits=1,
+  split_sizes={"train": 0.9, "valid": 0.1, "test": 0.0},
+)
+
+cc.prepare_data(
+  input_data_file=os.path.join(tokenized_train_dir, "tokenized.dataset"),
+  output_directory=classifier_train_dir,
+  output_prefix="classifier",
+)
+
+train_data = datasets.load_from_disk(classifier_train_dir + "/classifier_labeled.dataset")
+
+cc.train_classifier(
+  model_directory=model_dir,
+  num_classes=num_types,
+  train_data=train_data,
+  eval_data=None,
+  output_directory=classifier_fine_tuned_dir,
+  predict=False
+)
+
+print(">>> Generating predictions...", flush=True)
+
+# dictionary mapping labels from classifier to cell types
+with open(f"{classifier_train_dir}/classifier_id_class_dict.pkl", "rb") as f:
+  id_class_dict = pickle.load(f)
+
+with open(dictionary_files["token"], "rb") as f:
+  token_dict = pickle.load(f)
+
+# Load fine-tuned model
+model = BertForSequenceClassification.from_pretrained(classifier_fine_tuned_dir)
+
+test_data = datasets.load_from_disk(tokenized_test_dir + "/tokenized.dataset")
+test_data = test_data.add_column("label", [0] * len(test_data))
+
+# Get predictions
+trainer = Trainer(model=model, data_collator=DataCollatorForCellClassification(token_dictionary=token_dict))
+predictions = trainer.predict(test_data)
+
+# Select the most likely cell type based on the probability vector from the predictions of each cell
+predicted_label_ids = np.argmax(predictions.predictions, axis=1)
+predicted_logits = [predictions.predictions[i][predicted_label_ids[i]] for i in range(len(predicted_label_ids))]
+input_test.obs['label_pred'] = [id_class_dict[p] for p in predicted_label_ids]
+
+print(">>> Write output AnnData to file", flush=True)
+output = ad.AnnData(
+  obs=input_test.obs[["label_pred"]],
+  uns={
+    'method_id': meta['name'],
+    'dataset_id': input_test.uns['dataset_id'],
+    'normalization_id': input_test.uns['normalization_id']
+  }
+)
+output.write_h5ad(par['output'], compression='gzip')

src/workflows/run_benchmark/config.vsh.yaml

@@ -80,6 +80,7 @@ dependencies:
   - name: control_methods/majority_vote
   - name: control_methods/random_labels
   - name: control_methods/true_labels
+  - name: methods/geneformer
   - name: methods/knn
   - name: methods/logistic_regression
   - name: methods/mlp

src/workflows/run_benchmark/main.nf

@@ -11,6 +11,7 @@ methods = [
   majority_vote,
   random_labels,
   true_labels,
+  geneformer,
   knn,
   logistic_regression,
   mlp,