How to Build a Virtual Cell in Python from Scratch
This is a gentle introduction to building a virtual cell in Python.
We build a simple model that predicts how a cell’s gene expression changes in response to a perturbation.
I try to explain everything step by step to show the complete thought process behind every decision.
We start by answering the fundamental questions: what is a virtual cell and why it matters for disease understanding and drug discovery.
Then we go through the entire process and cover:
downloading and exploring single-cell RNA-seq data,
preprocessing the data,
designing a training pipeline,
splitting data by unseen perturbations,
representing perturbations with gene embeddings,
building PyTorch datasets and data loaders,
training a simple neural net,
testing the model,
comparing against a baseline,
dealing with model collapse,
improving the model with highly variable genes, pseudobulk expression, better evaluation, and delta prediction,
discussing possible next steps.
The final model takes a perturbation embedding, predicts a change in expression, and adds that change to the control cell state.
To be clear - this tutorial is for educational purposes and aims to illustrate the main steps involved in building a virtual cell.
It does not produce a competitive model for perturbation response prediction, but it is a starting point for you to play around with and improve.
Code: https://github.com/MaciejPiernik/virt...
Resources
Arc Institute Virtual Cell Atlas: https://arcinstitute.org/tools/virtua...
Virtual Cell Challenge dataset: https://github.com/ArcInstitute/arc-v...
Gene embeddings (benchmark paper + downloads): https://www.biorxiv.org/content/10.11...
CELLxGENE: https://cellxgene.cziscience.com/
Chapters
0:00 Intro
6:30 Representing a cell
11:24 Project setup
12:58 Intuition
20:25 What data we need?
25:02 Downloading data
28:58 Exploring data
35:09 Preprocessing data
43:12 The training pipeline
45:52 Splitting data
57:31 Encoding perturbations
1:02:55 Gene embeddings
1:12:40 The full training loop
1:27:52 The model
1:30:34 Data loaders
1:39:31 Mapping genes to embeddings
1:57:52 First training run
2:01:00 Refactor
2:07:32 Testing the model
2:15:21 Technical improvements
2:21:17 Model collapse
2:24:55 Fix #1: Highly variable genes
2:26:52 Fix #2: Pseudobulk
2:34:06 Fix #3: Loss & eval
2:40:20 Baseline
2:43:55 Fix #4: Predicting delta
2:48:13 Improving over baseline
2:54:46 Next steps and Conclusion
#VirtualCell #MachineLearning #Bioinformatics #Python #SingleCell #RNASeq #DeepLearning #DrugDiscovery #ComputationalBiology
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