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cellvgae-0.0.1b4
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CellVGAE uses the connectivity between cells (such as k-nearest neighbour graphs) with gene expression values as node features to learn high-quality cell representations in a lower-dimensional space
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cellvgae-0.0.1b4 |
| نام | cellvgae |
| نسخه کتابخانه | 0.0.1b4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | David Buterez |
| ایمیل نویسنده | david.buterez@gmail.com |
| آدرس صفحه اصلی | https://github.com/davidbuterez/CellVGAE |
| آدرس اینترنتی | https://pypi.org/project/cellvgae/ |
| مجوز | MIT |
# CellVGAE
An unsupervised scRNA-seq analysis workflow with graph attention networks
CellVGAE uses the connectivity between cells (such as *k*-nearest neighbour graphs or KNN) with gene expression values as node features to learn high-quality cell representations in a lower-dimensional space, with applications in downstream analyses like (density-based) clustering, visualisation, gene set enrichment analysis and others. CellVGAE leverages both the variational graph autoencoder and graph attention networks to offer a powerful and more interpretable machine learning approach. It is implemented in PyTorch using the PyTorch Geometric library.
## Requirements
Installing CellVGAE with pip will attempt to install PyTorch and PyTorch Geometric, however it is recommended that the appropriate GPU/CPU versions are installed manually beforehand. For Linux:
1. Install PyTorch GPU:
```conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia```
or PyTorch CPU:
```conda install pytorch torchvision torchaudio cpuonly -c pytorch```
2. Install PyTorch Geometric:
`conda install pyg -c pyg -c conda-forge`
3. (Optional) Install Faiss CPU:
`conda install -c pytorch faiss-cpu`
Faiss is only required if using the option `--graph_type "KNN Faiss"` . It is a soft dependency as it is not available for some platforms (currently Apple M1). Attempting to use CellVGAE with Faiss without installing it will result in an exception.
A GPU version of Faiss for CUDA 11.1 is not yet available.
4. Install CellVGAE with pip:
`pip install cellvgae --pre`
5. (Optional) For the attention graph visualisations of Figure 6, `igraph` is required:
`pip install python-igraph`
If using the R preprocessing code, we recommend installing the following:
`Seurat 3`, `scran`, `SingleCellExperiment`. `scRNAseq`, `BiocSingular`, `igraph`, `dplyr` and `textshape`.
## Example use
Using the example files in this repo (.h5ad file is the same as downloaded by Scanpy 1.8.1):
```bash
python -m cellvgae --input_gene_expression_path "example_data/paul15_myeloid_scanpy.h5ad" --graph_file_path "example_data/paul15_Faiss_KNN_K3_KHVG2500.txt" --graph_convolution "GAT" --num_hidden_layers 2 --hidden_dims 128 128 --num_heads 3 3 3 3 --dropout 0.4 0.4 0.4 0.4 --latent_dim 50 --epochs 50 --model_save_path "model_saved_out"
```
Other examples are available in `examples/cellvgae_example_scripts.txt`
(also consult the help section below)
## Usage
Invoke the training script with `python -m cellvgae` with the arguments detailed below:
```
usage: train [-h] [--input_gene_expression_path INPUT_GENE_EXPRESSION_PATH] [--hvg HVG] [--khvg KHVG] [--graph_type {KNN Scanpy,KNN Faiss,PKNN}] [--k K] [--graph_n_pcs GRAPH_N_PCS]
[--graph_metric {euclidean,manhattan,cosine}] [--graph_distance_cutoff_num_stds GRAPH_DISTANCE_CUTOFF_NUM_STDS] [--save_graph] [--raw_counts] [--faiss_gpu]
[--hvg_file_path HVG_FILE_PATH] [--khvg_file_path KHVG_FILE_PATH] [--graph_file_path GRAPH_FILE_PATH] [--graph_convolution {GAT,GATv2,GCN}] [--num_hidden_layers {2,3}]
[--num_heads [NUM_HEADS [NUM_HEADS ...]]] [--hidden_dims [HIDDEN_DIMS [HIDDEN_DIMS ...]]] [--dropout [DROPOUT [DROPOUT ...]]] [--latent_dim LATENT_DIM] [--loss {kl,mmd}] [--lr LR]
[--epochs EPOCHS] [--val_split VAL_SPLIT] [--test_split TEST_SPLIT] [--transpose_input] [--use_linear_decoder] [--decoder_nn_dim1 DECODER_NN_DIM1] [--name NAME] --model_save_path MODEL_SAVE_PATH [--umap] [--hdbscan]
Train CellVGAE.
optional arguments:
-h, --help show this help message and exit
--input_gene_expression_path INPUT_GENE_EXPRESSION_PATH
Input gene expression file path.
--hvg HVG Number of HVGs.
--khvg KHVG Number of KHVGs.
--graph_type {KNN Scanpy,KNN Faiss,PKNN}
Type of graph.
--k K K for KNN or Pearson (PKNN) graph.
--graph_n_pcs GRAPH_N_PCS
Use this many Principal Components for the KNN (only Scanpy).
--graph_metric {euclidean,manhattan,cosine}
--graph_distance_cutoff_num_stds GRAPH_DISTANCE_CUTOFF_NUM_STDS
Number of standard deviations to add to the mean of distances/correlation values. Can be negative.
--save_graph Save the generated graph to the output path specified by --model_save_path.
--raw_counts Enable preprocessing recipe for raw counts.
--faiss_gpu Use Faiss on the GPU (only for KNN Faiss).
--hvg_file_path HVG_FILE_PATH
HVG file if not using command line options to generate it.
--khvg_file_path KHVG_FILE_PATH
KHVG file if not using command line options to generate it. Can be the same file as --hvg_file_path if HVG = KHVG.
--graph_file_path GRAPH_FILE_PATH
Graph specified as an edge list (one edge per line, nodes separated by whitespace, not comma), if not using command line options to generate it.
--graph_convolution {GAT,GATv2,GCN}
--num_hidden_layers {2,3}
Number of hidden layers (must be 2 or 3).
--num_heads [NUM_HEADS [NUM_HEADS ...]]
Number of attention heads for each layer. Input is a list that must match the total number of layers = num_hidden_layers + 2 in length.
--hidden_dims [HIDDEN_DIMS [HIDDEN_DIMS ...]]
Output dimension for each hidden layer. Input is a list that matches --num_hidden_layers in length.
--dropout [DROPOUT [DROPOUT ...]]
Dropout for each layer. Input is a list that must match the total number of layers = num_hidden_layers + 2 in length.
--latent_dim LATENT_DIM
Latent dimension (output dimension for node embeddings).
--loss {kl,mmd} Loss function (KL or MMD).
--lr LR Learning rate for Adam.
--epochs EPOCHS Number of training epochs.
--val_split VAL_SPLIT
Validation split e.g. 0.1.
--test_split TEST_SPLIT
Test split e.g. 0.1.
--transpose_input Specify if inputs should be transposed.
--use_linear_decoder Turn on a neural network decoder, similar to traditional VAEs.
--decoder_nn_dim1 DECODER_NN_DIM1
First hidden dimenson for the neural network decoder, if specified using --use_linear_decoder.
--name NAME Name used for the written output files.
--model_save_path MODEL_SAVE_PATH
Path to save PyTorch model and output files. Will create the entire path if necessary.
--umap Compute and save the 2D UMAP embeddings of the output node features.
--hdbscan Compute and save different HDBSCAN clusterings.
```
نیازمندی
| مقدار | نام |
|---|---|
| >=1.6.0 | torch |
| >=0.5.1 | umap-learn |
| >=0.8.27 | hdbscan |
| >=0.11.1 | seaborn |
| >=3.3.4 | matplotlib |
| >=1.7.2 | scanpy |
| >=0.7.5 | anndata |
| >=4.61.2 | tqdm |
| >=1.1.0 | termcolor |
| >=1.19.5 | numpy |
| >=1.2.4 | pandas |
| >=1.7.0 | torch-geometric |
| >=0.24.2 | scikit-learn |
| >=0.6.12 | torch-sparse |
| >=2.0.8 | torch-scatter |
نحوه نصب
نصب پکیج whl cellvgae-0.0.1b4:
pip install cellvgae-0.0.1b4.whl
نصب پکیج tar.gz cellvgae-0.0.1b4:
pip install cellvgae-0.0.1b4.tar.gz