Fine-Tuning Single-Cell Bio Foundation Models: A Beginner’s Guide
Imagine being able to decode the unique molecular blueprint of every single cell in the human body, unveiling the mysteries of our biology at a remarkable level of detail. This exciting advancement is becoming possible through the integration of AI across various domains, including molecular biology.
In this short post, we will give you an overview of the most promising open-source single-cell foundation models that you should test and integrate into your research!
Challenges in single-cell RNA-seq analyses
If the Human Genome Project provided us with the book of life, single-cell analyses show us how each cell reads this book. These analyses shed light on the roles of individual cells in development, disease progression, and response to treatments. However, the high-dimensional and large-scale nature of single-cell data presents significant analytical challenges. Researchers face hurdles in integrating and interpreting vast datasets, extracting meaningful features, and dealing with differences due to technical effects that can obscure true biological signals. Moreover, overcorrecting for batch effects can be equally problematic, as it may eliminate genuine biological variation, further complicating data analysis.
Single-cell foundation models are well positioned to address those challenges
Overview of some of the most promising open-source models
- Geneformer: a foundation transformer model pre-trained on Genecorpus-30M, a corpus comprising approximately 30 million single-cell transcriptomes from a broad range of human tissues. This open-source foundation model under Apache-2.0 license seems to perform best on most benchmarks.
- UCE (Universal Cell Embeddings): this model creates a universal biological representation space for cells, leveraging a self-supervised learning approach on cell atlas data from diverse species. UCE creates an atlas of over 36 million cells, with more than 1,000 uniquely named cell types, from hundreds of experiments, dozens of tissues, and eight species. Being the only open-source single-cell foundation model (MIT license) being trained on multiple species, it is particularly interesting for tasks such as cross-species integration.
- scGPT: pre-trained on data from over 33 million human cells under non-disease conditions, encompassing a wide range of cell types from 51 organs or tissues and 441 studies. This open-source model is available under MIT license.
Challenges in single-cell RNA-seq analyses
import numpy as np
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt
# Compute the confusion matrix
cm = confusion_matrix(classification_labels_test, outputs.argmax(axis=1))
# Perform row-wise normalization
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
# Get unique labels in the order they appear in the confusion matrix
unique_labels = np.unique(
np.concatenate((classification_labels_test, outputs.argmax(axis=1)))
)
# Use id_class_dict to get the class names
class_names = [id_class_dict[label] for label in unique_labels]
# Create and plot the normalized confusion matrix
fig, ax = plt.subplots(figsize=(15, 15))
disp = ConfusionMatrixDisplay(
confusion_matrix=cm_normalized,
display_labels=class_names
)
disp.plot(ax=ax, xticks_rotation='vertical', values_format='.2f', cmap='coolwarm')
# Customize the plot
ax.set_title('Normalized Confusion Matrix (Row-wise)')
fig.set_facecolor("none")
# Adjust layout and display the plot
plt.tight_layout()
plt.show()
If the Human Genome Project provided us with the book of life, single-cell analyses show us how each cell reads this book. These analyses shed light on the roles of individual cells in development, disease progression, and response to treatments. However, the high-dimensional and large-scale nature of single-cell data presents significant analytical challenges. Researchers face hurdles in integrating and interpreting vast datasets, extracting meaningful features, and dealing with differences due to technical effects that can obscure true biological signals. Moreover, overcorrecting for batch effects can be equally problematic, as it may eliminate genuine biological variation, further complicating data analysis.
Helical’s open-source package aims to simplify this by providing standardized tools and resources.
If the Human Genome Project provided us with the book of life, single-cell analyses show us how each cell reads this book. These analyses shed light on the roles of individual cells in development, disease progression, and response to treatments. However, the high-dimensional and large-scale nature of single-cell data presents significant analytical challenges. Researchers face hurdles in integrating and interpreting vast datasets, extracting meaningful features, and dealing with differences due to technical effects that can obscure true biological signals. Moreover, overcorrecting for batch effects can be equally problematic, as it may eliminate genuine biological variation, further complicating data analysis.
If the Human Genome Project provided us with the book of life, single-cell analyses show us how each cell reads this book. These analyses shed light on the roles of individual cells in development, disease progression, and response to treatments.
Get started
While these models show great promise, they often lie in decentralized GitHub repositories, and users need to delve deeply into the accompanying literature to utilize them effectively. Additionally, integrating these models into existing workflows, specific applications, and ensuring compatibility with various data formats can be challenging.
About Helical
Helical is an open-core platform for computational biologists and data scientists to effortlessly integrate single-cell & genomics AI Bio Foundation Models in early-stage drug discovery.
Check out our
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