mist-vae 1.0.0

MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

MIST (Multi-InSight for T cell)

MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

Installation

Install from PyPI

pip install mist-vae

Install from GitHub

install the latest develop version

pip install git+https://github.com/aapupu/MIST.git

or git clone and install

git clone git://github.com/aapupu/MIST.git
cd MIST
pip install -e .

Note: Python 3.8 is recommended. MIST is implemented in Pytorch framework. If cuda is available, GPU modes will be run automatically.

Usage

1. API function

from mist import MIST
adata, model = MIST(rna_path, tcr_path, batch, rna_data_type, tcr_data_type, type)

Parameters of API function are similar to command line options.
The output includes a trained model and an Anndata object, which can be further analyzed using scanpy and scirpy.
rna_path List of paths to scRNA-seq data files.
tcr_path List of paths to scTCR-seq data files.
batch List of batch labels.
rna_data_type Type of scRNA-seq data file (e.g., 'h5ad').
tcr_data_type Type of scTCR-seq data file (e.g., '10X').
type Type of model to train ('joint', 'rna', or 'tcr').

2. Command line

MIST --rna_path rna_path1 rna_path2 --tcr_path tcr_path1 tcr_path2 --batch batch1 batch2 --rna_data_type h5ad --tcr_data_type 10X --type joint

Output

• adata.h5ad: preprocessed data and results
• model.pt: saved model

Option

• --rna_path
  Paths to scRNA-seq data files. (example: XXX1.h5ad XXX2.h5ad)
• --tcr_path
  Paths to scTCR-seq data files. (example: XXX1.csv XXX2.csv)
• --batch
  Batch labels.
• --rna_data_type
  Type of scRNA-seq data file (e.g., 10X mtx, h5, or h5ad). Default: h5ad
• --tcr_data_type
  Type of scTCR-seq data file (e.g., 10X, tracer, BD, or h5ad). Default: 10X
• --protein_path
  Path to merged protein (ADT) data file.
• --type
  Type of model to train (e.g., joint, rna, or tcr). Default: joint
• --min_genes
  Filtered out cells that are detected in less than min_genes. Default: 600
• --min_cells
  Filtered out genes that are detected in less than min_cells. Default: 3
• --pct_mt
  Filtered out cells that are detected in more than percentage of mitochondrial genes. If None, Filtered out mitochondrial genes. Default: None
• --n_top_genes
  Number of highly-variable genes to keep. Default: 2000
• --batch_size
  Batch size for training. Default: 128
• --pooling_dims
  Dimensionality of pooling layer. Default: 16
• --z_dims
  Dimensionality of latent space. If type='rna', z_dims=pooling_dims. Default: 128
• --drop_prob
  Dropout probability. Default: 0.1
• --lr
  Learning rate for the optimizer. Default: 1e-4
• --weight_decay
  L2 regularization strength. Default: 1e-3
• --max_epoch
  Maximum number of epochs. Default: 300
• --patience
  Patience for early stopping. Default: 30
• --warmup
  Warmup epochs. Default: 30
• --gpu
  Index of GPU to use if GPU is available. Default: 0
• --seed
  Random seed. Default: 42
• --outdir
  Output directory.

Help

Explore further applications of MIST

MIST.py --help 

The running examples of MIST can be found in the jupyter folder.

Citation

MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis
Wenpu Lai, Yangqiu Li, Oscar Junhong Luo
bioRxiv 2024.07.05.602192; doi: https://doi.org/10.1101/2024.07.05.602192

Contacts

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