Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space
pip install scalex
install the latest develop version
pip install git+https://github.com/jsxlei/scalex.git
or git clone and install
git clone git://github.com/jsxlei/scalex.git
cd scalex
python setup.py install
SCALEX is implemented in Pytorch framework.
SCALEX can be run on CPU devices, and running SCALEX on GPU devices if available is recommended.
SCALEX can both used under command line and API function in jupyter notebook
Please refer to the Documentation and Tutorial
from scalex import SCALEX
adata = SCALEX(data_list, batch_categories)
Function of parameters are similar to command line options.
Output is a Anndata object for further analysis with scanpy.
data_list
can be
- data_path, file format included txt, csv, h5ad, h5mu/rna, h5mu/atac, dir contains mtx
- list of data_paths
- Anndata
- list of AnnData
- above mixed
batch_categories
is optional, name of each batch, will be range from 0 to N-1 if not provided
SCALEX --data_list data1 data2 dataN --batch_categories batch_name1 batch_name2 batch_nameN
--data_list
: data path of each batch of single-cell dataset, use -d
for short
--batch_categories
: name of each batch, batch_categories will range from 0 to N-1 if not specified
Output will be saved in the output folder including:
- checkpoint: saved model to reproduce results cooperated with option --checkpoint or -c
- adata.h5ad: preprocessed data and results including, latent, clustering and imputation
- umap.png: UMAP visualization of latent representations of cells
- log.txt: log file of training process
SCALEX --data_list <filename.h5ad>
Specify batch in anadata.obs
using --batch_name
if only one concatenated h5ad file provided, batch_name can be e.g. conditions, samples, assays or patients, default is batch
SCALEX --data_list <filename.h5ad> --batch_name <specific_batch_name>
SCALEX --data_list <filename.h5ad> --profile ATAC
Inputation simultaneously along with Integration, add option --impute
, results are stored at anndata.layers['impute']
SCALEX --data_list <atac_filename.h5ad> --profile ATAC --impute True
SCALEX --data_list <filename.h5ad> --n_top_features features.txt
SCALEX --data_list <filename.h5ad> --processed
- --data_list
A list of matrices file (each as abatch
) or a single batch/batch-merged file. - --batch_categories
Categories for the batch annotation. By default, use increasing numbers if not given - --batch_name
Use this annotation in anndata.obs as batches for training model. Default: 'batch'. - --profile
Specify the single-cell profile, RNA or ATAC. Default: RNA. - --min_features
Filtered out cells that are detected in less than min_features. Default: 600 for RNA, 100 for ATAC. - --min_cells
Filtered out genes that are detected in less than min_cells. Default: 3. - --n_top_features
Number of highly-variable genes to keep. Default: 2000 for RNA, 30000 for ATAC. - --outdir
Output directory. Default: 'output/'. - --projection
Use for new dataset projection. Input the folder containing the pre-trained model. Default: None. - --impute
If True, calculate the imputed gene expression and store it at adata.layers['impute']. Default: False. - --chunk_size
Number of samples from the same batch to transform. Default: 20000. - --ignore_umap
If True, do not perform UMAP for visualization and leiden for clustering. Default: False. - --join
Use intersection ('inner') or union ('outer') of variables of different batches. - --batch_key
Add the batch annotation to obs using this key. By default, batch_key='batch'. - --batch_size
Number of samples per batch to load. Default: 64. - --lr
Learning rate. Default: 2e-4. - --max_iteration
Max iterations for training. Training one batch_size samples is one iteration. Default: 30000. - --seed
Random seed for torch and numpy. Default: 124. - --gpu
Index of GPU to use if GPU is available. Default: 0. - --verbose
Verbosity, True or False. Default: False.
Look for more usage of SCALEX
SCALEX.py --help
See the changelog.
Xiong, L., Tian, K., Li, Y., Ning, W., Gao, X., & Zhang, Q. C. (2022). Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space. Nature Communications, 13(1), 6118. https://doi.org/10.1038/s41467-022-33758-z