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  • 2022-04-28 19:53
SCALE v2:通过潜在特征提取进行单细胞整合分析 安装 从PyPI安装 pip install scale-v2 从GitHub安装 git clone git://github.com/jsxlei/scale_v2.git cd scale_v2 python setup.py install SCALE v2在框架中实现。 如果可用,建议在CUDA上运行SCALE v2。 安装仅需几分钟。 快速开始 SCALE v2可以在Jupyter Notebook中的命令行和API函数下使用 1.命令行 SCALE.py --data_list data1 data2 --batch_categories batch1 batch2 选项 -data_list 矩阵文件列表(每个都是batch或单个批处理/批处理合并的文件)。 -batch_categories 批处理批注的类别。 默认
[![Stars](https://img.shields.io/github/stars/jsxlei/SCALE_v2?logo=GitHub&color=yellow)](https://github.com/jsxlei/scale_v2/stargazers) [![PyPI](https://img.shields.io/pypi/v/scale-v2.svg)](https://pypi.org/project/scale-v2) [![Documentation Status](https://readthedocs.org/projects/scale-v2/badge/?version=latest)](https://scale-v2.readthedocs.io/en/latest/?badge=stable) [![Downloads](https://pepy.tech/badge/scale_v2)](https://pepy.tech/project/scale_v2) # SCALE v2: Single-cell integrative Analysis via latent Feature Extraction ## [Documentation](https://scale-v2.readthedocs.io/en/latest/index.html) ## Installation #### install from PyPI pip install scale-v2 #### install from GitHub git clone git://github.com/jsxlei/scale_v2.git cd scale_v2 python setup.py install SCALE v2 is implemented in [Pytorch](https://pytorch.org/) framework. Running SCALE v2 on CUDA is recommended if available. Installation only requires a few minutes. ## Quick Start SCALE v2 can both used under command line and API function in jupyter notebook ### 1. Command line SCALE.py --data_list data1 data2 --batch_categories batch1 batch2 #### Option * --**data_list** A list of matrices file (each as a `batch` or a single batch/batch-merged file. * --**batch_categories** Categories for the batch annotation. By default, use increasing numbers. * --**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_name** Use this annotation in obs as batches for training model. Default: '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. #### Output Output will be saved in the output folder including: * **checkpoint**: saved model to reproduce results cooperated with option --checkpoint or -c * **[adata.h5ad](https://anndata.readthedocs.io/en/stable/anndata.AnnData.html#anndata.AnnData)**: 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 #### Useful options * output folder for saveing results: [-o] or [--outdir] * filter rare genes, default 3: [--min_cell] * filter low quality cells, default 600: [--min_gene] * select the number of highly variable genes, keep all genes with -1, default 2000: [--n_top_genes] #### Help Look for more usage of SCALE v2 SCALE.py --help ### 2. API function from scale import SCALE adata = SCALE(data_list, batch_categories) Function of parameters are similar to command line options. Output is a Anndata object for further analysis with scanpy. ## [Tutorial](https://scale-v2.readthedocs.io/en/latest/tutorial/index.html) ## Previous version [SCALE](https://github.com/jsxlei/SCALE) Previous SCALE for single-cell ATAC-seq analysis is still available in SCALE v2 by command line (--version 1) or api (SCALE_v1). ### Command line SCALE.py -d data --version 1 ### API from scale.extensions import SCALE_v1 SCALE_v1(data) All the usage is the same with previous SCALE version 1.