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Cell-BLAST-0.3.8
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مشاهده بیشترتوضیحات
Single-cell transcriptome querying tool
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | Cell-BLAST-0.3.8 |
| نام | Cell-BLAST |
| نسخه کتابخانه | 0.3.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Zhijie Cao |
| ایمیل نویسنده | caozj@mail.cbi.pku.edu.cn |
| آدرس صفحه اصلی | https://github.com/gao-lab/Cell_BLAST |
| آدرس اینترنتی | https://pypi.org/project/Cell-BLAST/ |
| مجوز | - |
# Cell BLAST
Cell BLAST is a cell querying tool for single-cell transcriptomics data.
For each query cell, it searches for most similar cells in the reference database.
Annotations in reference cells, e.g. cell type, can then be transfered to query
cells based on cell-to-cell similarities. See our
[publication](https://www.nature.com/articles/s41467-020-17281-7) for details.
## Installing the Python package
We only support installation via pip right now.
Installation within virtual environments are recommended, see
[virtualenv](https://virtualenv.pypa.io/en/latest/) or
[conda](https://conda.io/docs/user-guide/tasks/manage-environments.html).
For conda, here's a one-liner to set up an empty environment
for installing Cell BLAST:
`conda create -n cb python=3.6 && conda activate cb`
Now follow the instructions below to install Cell BLAST:
1. Make sure you have a working version of tensorflow or tensorflow-gpu
(version >= 1.5). You can follow the
[official instructions](https://www.tensorflow.org/install/)
about how to install tensorflow (and dependencies like CUDA and CuDNN
for the GPU version), or just install via anaconda, which handles
dependencies automatically:
For installing the GPU supported version:
`conda install tensorflow-gpu=1.8`
For installing the CPU only version:
`conda install tensorflow=1.8`
2. Install Cell BLAST by running:
`pip install Cell-BLAST`
3. Check if the package can be imported in Python interpreter:
`import Cell_BLAST as cb`
## Documentation
Online documentation can be found [here](http://cblast.gao-lab.org/doc-latest/index.html).
## Web-based interface
We also provide a [Web-based service](http://cblast.gao-lab.org/) for
off-the-shelf querying of our ACA reference panels.
## Repository structure
* The `Cell_BLAST` directory contains the Cell BLAST Python package.
* The `Datasets` directory contains data metatables and scripts for data collection.
* The `Evaluation` directory contains scripts used for benchmarking
and producing some figures of the manuscript.
* The `Notebooks` directory contains scripts used for additional experiments,
case studies, and a pipeline for building the ACA database.
* The `docs` directory contains files used to generate the online documentation.
* The `test` directory contains unit tests for the Python package.
## Reproduce results
### Obtain required data
For convenience, all required datasets have been packed into two data pack files.
First download these files to the `Datasets` directory:
* [`ftp://ftp.cbi.pku.edu.cn/pub/cell-blast-download/data_pack.tar.gz`](ftp://ftp.cbi.pku.edu.cn/pub/cell-blast-download/data_pack.tar.gz):
Contains datasets required for most benchmarks and case studies, except for
those used in the query speed benchmark (because these datasets are especially
large, and were packed independently).
* [`ftp://ftp.cbi.pku.edu.cn/pub/cell-blast-download/data_pack_ext.tar.gz`](ftp://ftp.cbi.pku.edu.cn/pub/cell-blast-download/data_pack_ext.tar.gz):
Contains datasets required for the query speed benchmark.
Then extract the files under the `Datasets` directory:
```bash
# Under the `Datasets` directory
tar xf "data_pack.tar.gz"
tar xf "data_pack_ext.tar.gz"
```
### Environment setup
#### Python
First create a conda environment and install most packages via:
```bash
conda env create -n cb-gpu -f env.yml && conda activate cb-gpu
```
Optionally, if GPU does not work properly (possibly due to inconsistent conda
channels used), reinstalling tensorflow should solve the problem:
```bash
conda install tensorflow=1.8.0 tensorflow-base=1.8.0 tensorflow-gpu=1.8.0 --force-reinstall
```
Finally, install customized packages or packages unavailable in conda.
All dependencies have already been installed via `env.yml`,
so `--no-deps` is added to prevent overwriting conda installed packages:
```bash
# Under project root
pip install Cell-BLAST==0.3.7 --no-deps
pip install local/scScope-0.1.5.tar.gz --no-deps # Add random seed setting
pip install local/DCA-0.2.2.tar.gz --no-deps # Allow GPU memory growth, suppress integer warning
pip install local/DCA_modpp-0.2.2.tar.gz --no-deps # Modify preprocessing
pip install local/ZIFA-0.1.tar.gz --no-deps # Remove fixed random seeds
pip install local/Dhaka-0.1.tar.gz --no-deps
pip install local/scvi-0.2.3.tar.gz --no-deps # Fix torch bugs
tar xf local/SAUCIE.tar.gz -C ${CONDA_PREFIX}/lib/python3.6/site-packages/ # Add random seed setting
pip install fcswrite # Dependency of SAUCIE not available in conda
```
For scPhere, we use a separate environment because of conflicting dependencies
(the environment should be named "scphere" for it to be found in the benchmarking pipeline):
```bash
# Under project root
conda create -n scphere 'python>=3.6' 'numpy>=1.16.4' 'scipy>=1.3.0' \
'pandas>=0.21.0' 'matplotlib>=3.1.0' 'tensorflow=1.14.0' \
'tensorflow-probability=0.7.0' 'ipykernel' && conda activate scphere
pip install local/scPhere-0.1.0.tar.gz --no-deps
pip install Cell-BLAST==0.3.7 # Still need cb.data to read data (tf dependent functions may not work properly)
```
#### R
Start R (tested on version `3.6.0`) at project root and run:
```R
# Under project root
packrat::restore()
```
Then install the customized version of Seurat by:
```R
# Under project root
install.packages("local/seurat-2.3.3.tar.gz", repos=NULL, type="source") # Remove fixed random seeds
```
For CCA anchor (Seurat v3) and Harmony, we used a separate packrat environment.
To build this dedicated environment, start R at directory "packrat/envs/seurat_v3" and run:
```R
# Under the `packrat/envs/seurat_v3` directory
packrat::restore()
```
Then install the customized version of Seurat v3 by:
```R
# Under the `packrat/envs/seurat_v3` directory
install.packages("../../../local/seurat-3.0.2.tar.gz", repos=NULL, type="source") # Remove fixed random seeds
```
### Run all benchmarks and reproduce figures
Make sure the conda environment create above is activated.
Go to directory "Evaluation" and run the following command:
```bash
# Under project root
snakemake -prk
```
Some jobs will likely fail, e.g. due to timeout or memory issues, and cause
downstream steps, including result plotting, to fail as well.
These failing jobs will be blacklisted in future runs, so just run the above
command for a second time, and results for the successful jobs should be
summarized and plotted without error.
## Contact
Feel free to submit an issue or contact us at
[cblast@mail.cbi.pku.edu.cn](mailto:cblast@mail.cbi.pku.edu.cn)
for problems about the Python package, website or database.
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| >=0.39.0 | numba |
| >=1.0.0 | scipy |
| >=0.12.0 | joblib |
| >=0.17.0 | scikit-learn |
| >=4.12.0 | tqdm |
| >=0.21.0 | pandas |
| >=2.7.0 | h5py |
| >=0.7.1 | python-igraph |
| >=0.10.2 | pronto |
| >=0.9.0 | seaborn |
| >=0.2.1 | umap-learn |
| >=0.6.14 | anndata |
| >=2.0.6 | loompy |
| >=0.8.0 | statsmodels |
| - | plotly |
نحوه نصب
نصب پکیج whl Cell-BLAST-0.3.8:
pip install Cell-BLAST-0.3.8.whl
نصب پکیج tar.gz Cell-BLAST-0.3.8:
pip install Cell-BLAST-0.3.8.tar.gz