scPRINT is a large transformer model built for the inference of gene networks (connections between genes explaining the cell's expression profile) from scRNAseq data.
It uses novel encoding and decoding of the cell expression profile and new pre-training methodologies to learn a cell model.
scPRINT can be used to perform the following analyses:
- expression denoising: increase the resolution of your scRNAseq data
- cell embedding: generate a low-dimensional representation of your dataset
- label prediction: predict the cell type, disease, sequencer, sex, and ethnicity of your cells
- gene network inference: generate a gene network from any cell or cell cluster in your scRNAseq dataset
Read the manuscript! if you would like to know more about scPRINT.
- scPRINT: Large Cell Model for scRNAseq data
- Table of Contents
- Install
scPRINT - Usage
- FAQ
- I want to generate gene networks from scRNAseq data:
- I want to generate cell embeddings and cell label predictions from scRNAseq data:
- I want to denoise my scRNAseq dataset:
- I want to generate an atlas-level embedding
- I need to generate gene tokens using pLLMs
- I want to pre-train scPRINT from scratch on my own data
- how can I find if scPRINT was trained on my data?
- can I use scPRINT on other organisms rather than human?
- how long does scPRINT takes? what kind of resources do I need? (or in alternative: can i run scPRINT locally?)
- I have different scRNASeq batches. Should I integrate my data before running scPRINT?
- Documentation
- Model Weights
- Development
- Work in progress (PR welcomed):
For the moment scPRINT has been tested on MacOS and Linux (Ubuntu 20.04) with Python 3.10.
If you want to be using flashattention2, know that it only supports triton 2.0 MLIR's version and torch==2.0.0 for now.
conda create -n "[whatever]" python==3.10
#one of
pip install scprint # OR
pip install scprint[dev] # for the dev dependencies (building etc..) OR
pip install scprint[flash] && pip install -e "git+https:/
/github.com/triton-lang/triton.git@legacy-backend
#egg=triton&subdirectory=python" # to use flashattention2, you will need to install triton 2.0.0.dev20221202 specifically, working on removing this dependency # only if you have a compatible gpu (e.g. not available for apple GPUs for now, see https://github.com/triton-lang/triton?tab=readme-ov-file#compatibility)
#OR pip install scPRINT[dev,flash]We make use of some additional packages we developed alongside scPRint.
Please refer to their documentation for more information:
- scDataLoader: a dataloader for training large cell models.
- GRnnData: a package to work with gene networks from single cell data.
- benGRN: a package to benchmark gene network inference methods from single cell data.
In that case, connect with google or github to lamin.ai, then be sure to connect before running anything (or before starting a notebook): lamin login <email> --key <API-key>. Follow the instructions on their website.
This is the most minimal example of how scPRINT works:
from lightning.pytorch import Trainer
from scprint import scPrint
from scdataloader import DataModule
datamodule = DataModule(...)
model = scPrint(...)
# to train / fit / test the model
trainer = Trainer(...)
trainer.fit(model, datamodule=datamodule)
# to do predictions Denoiser, Embedder, GNInfer
denoiser = Denoiser(...)
adata = sc.read_h5ad(...)
denoiser(model, adata=adata)
...or, from a bash command line
$ scprint fit/train/predict/test/denoise/embed/gninfer --config config/[medium|large|vlarge] ...find out more about the commands by running scprint --help or scprint [command] --help.
more examples of using the command line are available in the docs.
If you do not have triton installed you will not be able to take advantage of GPU acceleration, but you can still use the model on the CPU.
In that case, if loading from a checkpoint that was trained with flashattention, you will need to specify transformer="normal" in the load_from_checkpoint function like so:
model = scPrint.load_from_checkpoint(
'../data/temp/last.ckpt', precpt_gene_emb=None,
transformer="normal")We now explore the different usages of scPRINT:
-> Refer to the section . gene network inference in this notebook.
-> More examples in this notebook ./notebooks/assessments/bench_omni.ipynb.
-> Refer to the embeddings and cell annotations section in this notebook.
-> Refer to the Denoising of B-cell section in this notebook.
-> More example in our benchmark notebook ./notebooks/assessments/bench_denoising.ipynb.
-> Refer to the notebook nice_umap.ipynb.
To run scPRINT, you can use the option to define the gene tokens using protein language model embeddings of genes. This is done by providing the path to a parquet file of the precomputed set of embeddings for each gene name to scPRINT via "precpt_gene_emb"
-> To generate this file please refer to the notebook generate_gene_embeddings.
-> Refer to the documentation page pretrain scprint
If your data is available in cellxgene, scPRINT was likely trained on it. However some cells, datasets were dropped due to low quality data and some were randomly removed to be part of the validation / test sets.
scPRINT has been pretrained on both humans and mouse, and can be used on any organism with a similar gene set. If you want to use scPRINT on very different organisms, you will need to generate gene embeddings for that organism and re-train scPRINT
how long does scPRINT takes? what kind of resources do I need? (or in alternative: can i run scPRINT locally?)
please look at our supplementary tables in the manuscript
scPRINT takes raw count as inputs, so please don't use integrated data. Just give the raw counts to scPRINT and it will take care of the rest.
For more information on usage please see the documentation in https://www.jkobject.com/scPRINT/
Model weights are available on hugging face.
Read the CONTRIBUTING.md file.
Read the training runs document to know more about how pre-training was performed and the its behavior.
Acknowledgement: python template laminDB lightning
- remove the triton dependencies
- add version with additional labels (tissues, age) and organisms (mouse, zebrafish) and more datasets from cellxgene
- version with separate transformer blocks for the encoding part of the bottleneck learning and for the cell embeddings
- improve classifier to output uncertainties and topK predictions when unsure
- setup latest lamindb version
Awesome Large Cell Model created by Jeremie Kalfon.