| name | single-cell-foundation-model-geneformer |
| description | Use this skill when a task involves Geneformer workflows, especially TranscriptomeTokenizer input preparation, tokenized `.dataset` generation, cell or gene classification with `Classifier`, embedding extraction with `EmbExtractor`, and in silico perturbation analysis with `InSilicoPerturber`.
|
Geneformer
Use This Skill When
Use this skill when the task involves official Geneformer workflows such as:
- converting raw scRNA-seq data into Geneformer tokenized datasets
- fine-tuning Geneformer for cell or gene classification
- extracting cell or gene embeddings
- generating state embeddings for downstream perturbation analysis
- running in silico perturbation or in silico treatment style analyses
- distinguishing pretrained zero-shot usage from fine-tuned classifier usage
This skill is for Geneformer-specific workflows, not generic single-cell model use.
Start Here
- Confirm the input is raw-count scRNA-seq data and still suitable for tokenization.
- Check that
ensembl_id and n_counts are available.
- Tokenize first unless the user already has a Geneformer
.dataset.
- Decide whether the task is classification, embedding extraction, or in silico perturbation.
Choose A Path
Tokenization
Use TranscriptomeTokenizer first for almost every Geneformer workflow.
This step converts raw-count .loom or .h5ad data into tokenized datasets
used by the downstream APIs.
Geneformer expects:
- row attribute
ensembl_id
- cell attribute
n_counts
Optional metadata can be passed through during tokenization.
Classification
Use Classifier for:
- cell state classification
- cell type annotation
- gene classification tasks
The input is a tokenized Geneformer .dataset object, not raw AnnData.
Embedding extraction
Use EmbExtractor when the task is to:
- extract CLS, cell, or gene embeddings
- plot or inspect cell embeddings
- generate state embeddings for later perturbation analysis
In silico perturbation
Use InSilicoPerturber for zero-shot or model-based perturbation analyses such as:
- deleting or shifting genes
- modeling start and goal cell states
- ranking perturbations by movement toward a desired cell state
This is one of Geneformer's defining workflows and should be treated as more
than ordinary classifier inference.
Guardrails
- Do not pass feature-selected matrices into the tokenizer; the docs expect raw counts without feature selection.
- Do not use gene symbols where the tokenizer expects
ensembl_id.
- Do not confuse tokenized
.dataset files with AnnData objects.
- Do not skip tokenization and jump directly to classifiers or perturbation APIs.
- For perturbation tasks, be explicit about model type, embedding mode, and target cell states.
Official Workflow Surface
| Component | Use |
|---|
TranscriptomeTokenizer | create tokenized datasets |
Classifier | fine-tune cell or gene classifiers |
MTLClassifier | multitask cell classification |
EmbExtractor | extract and summarize embeddings |
InSilicoPerturber | simulate perturbations / treatment directions |
Read More Only If Needed
- For operational usage and required input fields, read
references/workflows.md.
- For official source locations and model-specific notes, read
references/sources-and-notes.md.
