Developed by Omar Kana [email protected]
CLI integration by David Filipovic [email protected]
Maintained by David Filipovic [email protected]
Single Cell Variational Inference of the Dose Response (scVIDR) a variational autoencoder tool used to predict expression of chemcial perturbations across cell types.
git clone https://github.com/BhattacharyaLab/scVIDR.git
cd scVIDR
conda create -n scVIDR python=3.8.5
conda activate scVIDR
pip3 install -r requirements.txt
pip3 install geomloss==0.2.5
pip install torch==1.8.1+cu111 torchaudio==0.8.1 torchvision==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
To get the data directory for figure notebooks: https://drive.google.com/file/d/11fzDbp0B19Dy47MtD742Jl4Hz2bdSiiq/view?usp=sharing
Once you download data.zip copy it to scVIDR/data and unzip it there.
To train a single dose VAE model run the `scvidr_train.py single_dose` command (Figure 2 of the manuscript).
To train a multi dose VAE model run the `scvidr_train.py multi_dose` command (Figure 3 of the manuscript).
Both types of models expect an AnnData file in the h5ad format as input. In addition to the scRNAseq data, the obs table is also expected to contain a dose and a cell type column.
The single dose model expects at least two distinct doses, whereas the multi dose model expects at least three.
Command arguments are the same for both types of models and are listed below.
usage: `scvidr_train.py {single_dose/multi_dose} [-h] [--dose_column DOSE_COLUMN] [--celltype_column CELLTYPE_COLUMN] [--test_celltype TEST_CELLTYPE] [--treated_dose CONTROL_DOSE] [--treated_dose TREATED_DOSE] [--celltypes_keep CELLTYPES_KEEP] h5ad_data_file model_path`
Train a VAE model applicable to scGen and scVIDR using a h5ad input dataset
positional arguments:
h5ad_data_file The data file containing the raw reads in h5ad format
model_path Path to the directory where the trained model will be saved
model arguments:
-h, --help show this help message and exit
--dose_column DOSE_COLUMN
Name of the column within obs dataframe representing the dose (default "Dose")
--celltype_column CELLTYPE_COLUMN
Name of the column within obs dataframe representing the cell type (default "celltype")
--test_celltype TEST_CELLTYPE
Name of the cell type to be left out for testing - surround by quotation marks for cell types containing spaces (default "Hepatocytes - portal"
--control_dose CONTROL_DOSE
Control dose (default "0")
--treated_dose TREATED_DOSE
Treated dose (default "30")
--celltypes_keep CELLTYPES_KEEP
Cell types to keep in the dataset during training/testing - either a file containing list of cell types (one cell type per line) or semicolon separated list of cell types (surround in quotation marks) - default all available cell types
(default "ALL")
To train all the single dose models for all individual cell types used in the manuscript execute
python scvidr_train.py single_dose --celltypes_keep ../metadata/liver_celltypes --test_celltype "Hepatocytes - portal" ../data/nault2021_singleDose.h5ad "../data/VAE_Binary_Prediction_Dioxin_5000g_Hepatocytes - central.pt/"
python scvidr_train.py single_dose --celltypes_keep ../metadata/liver_celltypes --test_celltype "Hepatocytes - central" ../data/nault2021_singleDose.h5ad "../data/VAE_Binary_Prediction_Dioxin_5000g_Hepatocytes - portal.pt/"
python scvidr_train.py single_dose --celltypes_keep ../metadata/liver_celltypes --test_celltype "Cholangiocytes" ../data/nault2021_singleDose.h5ad "../data/VAE_Binary_Prediction_Dioxin_5000g_Cholangiocytes.pt/"
python scvidr_train.py single_dose --celltypes_keep ../metadata/liver_celltypes --test_celltype "Stellate Cells" ../data/nault2021_singleDose.h5ad "../data/VAE_Binary_Prediction_Dioxin_5000g_Stellate Cells.pt/"
python scvidr_train.py single_dose --celltypes_keep ../metadata/liver_celltypes --test_celltype "Portal Fibroblasts" ../data/nault2021_singleDose.h5ad "../data/VAE_Binary_Prediction_Dioxin_5000g_Portal Fibroblasts.pt/"
python scvidr_train.py single_dose --celltypes_keep ../metadata/liver_celltypes --test_celltype "Endothelial Cells" ../data/nault2021_singleDose.h5ad "../data/VAE_Binary_Prediction_Dioxin_5000g_Endothelial Cells.pt/"
To train all the multi dose models for all individual cell types used in the manuscript execute
python scvidr_train.py multi_dose --control_dose 0.0 --celltypes_keep ../metadata/liver_celltypes --test_celltype "Hepatocytes - central" ../data/nault2021_multiDose.h5ad "../data/VAE_Cont_Prediction_Dioxin_5000g_Hepatocytes - central.pt/"
python scvidr_train.py multi_dose --control_dose 0.0 --celltypes_keep ../metadata/liver_celltypes --test_celltype "Hepatocytes - portal" ../data/nault2021_multiDose.h5ad "../data/VAE_Cont_Prediction_Dioxin_5000g_Hepatocytes - portal.pt/"
python scvidr_train.py multi_dose --control_dose 0.0 --celltypes_keep ../metadata/liver_celltypes --test_celltype "Cholangiocytes" ../data/nault2021_multiDose.h5ad "../data/VAE_Cont_Prediction_Dioxin_5000g_Cholangiocytes.pt/"
python scvidr_train.py multi_dose --control_dose 0.0 --celltypes_keep ../metadata/liver_celltypes --test_celltype "Stellate Cells" ../data/nault2021_multiDose.h5ad "../data/VAE_Cont_Prediction_Dioxin_5000g_Stellate Cells.pt/"
python scvidr_train.py multi_dose --control_dose 0.0 --celltypes_keep ../metadata/liver_celltypes --test_celltype "Portal Fibroblasts" ../data/nault2021_multiDose.h5ad "../data/VAE_Cont_Prediction_Dioxin_5000g_Portal Fibroblasts.pt/"
python scvidr_train.py multi_dose --control_dose 0.0 --celltypes_keep ../metadata/liver_celltypes --test_celltype "Endothelial Cells" ../data/nault2021_multiDose.h5ad "../data/VAE_Cont_Prediction_Dioxin_5000g_Endothelial Cells.pt/"
Note: all of these models are available pretrained (same names as listed above)
usage: scvidr_predict.py single_dose [-h] [--model MODEL]
[--dose_column DOSE_COLUMN]
[--celltype_column CELLTYPE_COLUMN]
[--test_celltype TEST_CELLTYPE]
[--control_dose CONTROL_DOSE]
[--treated_dose TREATED_DOSE]
[--celltypes_keep CELLTYPES_KEEP]
h5ad_data_file model_path output_path
Predict treatment condition using a pretrained scVIDR or scGEN model
positional arguments:
h5ad_data_file The data file containing the raw reads in h5ad format
model_path Path to the directory where the trained model was
saved in the model training step
output_path Path to the driectory where the anndata will be output
to in an h5ad format
optional arguments:
-h, --help show this help message and exit
--model MODEL Use scVIDR or scGen for prediciton (defualt "scVIDR")
--dose_column DOSE_COLUMN
Name of the column within obs dataframe representing
the dose (default "Dose")
--celltype_column CELLTYPE_COLUMN
Name of the column within obs dataframe representing
the cell type (default "celltype")
--test_celltype TEST_CELLTYPE
Name of the cell type to be left out for testing -
surround by quotation marks for cell types containing
spaces (default "Hepatocytes - portal"
--control_dose CONTROL_DOSE
Control dose (default "0")
--treated_dose TREATED_DOSE
Treated dose (default "30")
--celltypes_keep CELLTYPES_KEEP
Cell types to keep in the dataset during
training/testing - either a file containing list of
cell types (one cell type per line) or semicolon
separated list of cell types (surround in quotation
marks) - default all available cell types (default
"ALL")
Example of single does prediction command:
python scvidr_predict.py single_dose ../data/nault2021_singleDose.h5ad ../data/VAE_Binary_Prediction_Dioxin_5000g_Hepatocytes\ -\ portal.pt/ ../data/SingleDose_TCDD \--model scVIDR --dose_column Dose --celltype_column celltype --test_celltype Hepatocytes - portal --control_dose 0 --treated_dose 30 --celltypes_keep ../metadata/liver_celltypes
usage: scvidr_predict.py multi_dose [-h] [--model MODEL]
[--dose_column DOSE_COLUMN]
[--celltype_column CELLTYPE_COLUMN]
[--test_celltype TEST_CELLTYPE]
[--control_dose CONTROL_DOSE]
[--treated_dose TREATED_DOSE]
[--celltypes_keep CELLTYPES_KEEP]
h5ad_data_file model_path output_path
positional arguments:
h5ad_data_file The data file containing the raw reads in h5ad format
model_path Path to the directory where the trained model was
saved in the model training step
output_path Path to the driectory where the anndata will be output
to in an h5ad format
optional arguments:
-h, --help show this help message and exit
--model MODEL Use scVIDR or scGen for prediciton (defualt "scVIDR")
--dose_column DOSE_COLUMN
Name of the column within obs dataframe representing
the dose (default "Dose")
--celltype_column CELLTYPE_COLUMN
Name of the column within obs dataframe representing
the cell type (default "celltype")
--test_celltype TEST_CELLTYPE
Name of the cell type to be left out for testing -
surround by quotation marks for cell types containing
spaces (default "Hepatocytes - portal"
--control_dose CONTROL_DOSE
Control dose (default "0")
--treated_dose TREATED_DOSE
Treated dose (default "30")
--celltypes_keep CELLTYPES_KEEP
Cell types to keep in the dataset during
training/testing - either a file containing list of
cell types (one cell type per line) or semicolon
separated list of cell types (surround in quotation
marks) - default all available cell types (default
"ALL")
Example of multidose prediction command:
python scvidr_predict.py multi_dose ../data/nault2021_multiDose.h5ad ../data/VAE_Cont_Prediction_Dioxin_5000g_Hepatocytes\ -\ portal.pt/ ../data/MultiDose_TCDD \--model scVIDR --dose_column Dose --celltype_column celltype --test_celltype Hepatocytes - portal --control_dose 0.0 --treated_dose 30.0 --celltypes_keep ../metadata/liver_celltypes
usage: scvidr_genescores.py [-h] [--dose_column DOSE_COLUMN]
[--celltype_column CELLTYPE_COLUMN]
[--test_celltype TEST_CELLTYPE]
[--control_dose CONTROL_DOSE]
[--treated_dose TREATED_DOSE]
[--celltypes_keep CELLTYPES_KEEP]
[--training_size TRAINING_SIZE]
h5ad_data_file model_path output_path
Interpret scVIDR predictions using ridge regression. Outputs CSV file of gene scores.
positional arguments:
h5ad_data_file The data file containing the raw reads in h5ad format
model_path Path to the directory where the trained model was
saved in the model training step
output_path Path to the driectory where the gene scores will be
saved as a csv file
optional arguments:
-h, --help show this help message and exit
--dose_column DOSE_COLUMN
Name of the column within obs dataframe representing
the dose (default "Dose")
--celltype_column CELLTYPE_COLUMN
Name of the column within obs dataframe representing
the cell type (default "celltype")
--test_celltype TEST_CELLTYPE
Name of the cell type to be left out for testing -
surround by quotation marks for cell types containing
spaces (default "Hepatocytes - portal"
--control_dose CONTROL_DOSE
Control dose (default "0")
--treated_dose TREATED_DOSE
Treated dose (default "30")
--celltypes_keep CELLTYPES_KEEP
Cell types to keep in the dataset during
training/testing - either a file containing list of
cell types (one cell type per line) or semicolon
separated list of cell types (surround in quotation
marks) - default all available cell types (default
"ALL")
--training_size TRAINING_SIZE
Number of samples generated from latent distribution
Example of calculating gene_scores:
python scvidr_genescores.py ../data/nault2021_multiDose.h5ad ../data/VAE_Cont_Prediction_Dioxin_5000g_Hepatocytes\ -\ portal.pt/ ../data/MultiDose_TCDD --dose_column Dose --celltype_column celltype --test_celltype Hepatocytes\ -\ portal --control_dose 0.0 --treated_dose 30.0 --celltypes_keep ../metadata/liver_celltypes
| figure | notebook path | Description |
|---|---|---|
| Figure 2 | notebooks/Figure2.ipynb | Single Dose TCDD |
| Figure 3 | notebooks/Figure3.ipynb | Multi Dose TCDD |
| Figure 4 | notebooks/Figure4.ipynb | Gene Scores |
| Figure 5 | notebooks/Figure5.ipynb | Pseudodose |
| Supplemental Figure 2 | notebooks/SupplementalFigure2.ipynb | PCA of |
| Supplemental Figure 3 | notebooks/SupplementalFigure3.ipynb | Single Dose IFNB |
| Supplemental Figure 4 | notebooks/SupplementalFigure4.ipynb | Multi Dose sciplex |
| Supplemental Figure 5 | notebooks/SupplementalFigure5.ipynb | scVIDR Analysis TCDD |
| Supplemental Figure 6 | notebooks/SupplementalFigure6.ipynb | scVIDR Analysis sciplex |
| Supplemental Figure 7 | notebooks/SupplementalFigure7.ipynb | scVIDR cross study |
| Supplemental Figure 8 | notebooks/SupplementalFigure8.ipynb | scVIDR cross species |
| Supplemental Figure 9 | notebooks/SupplementalFigure9.ipynb | scVIDR equal scGen |