scilifelablts/lts-workflows-sm-scrnaseq-ci
scilifelablts
下载次数: 0状态：社区镜像维护者：scilifelablts仓库类型：镜像最近更新：8 年前
让 AI 帮你使用轩辕镜像？ · 展开查看说明 · 点击收起说明
如果你使用 DeepSeek、元宝 AI、Copilot、ChatGPT 等 AI 工具，协助配置轩辕镜像、编写 docker pull 命令、修改 Docker Compose 镜像地址、配置镜像加速、排查镜像拉取失败、分析报错日志等问题，请先让 AI 阅读并遵守轩辕镜像的规则文档。
只需在 AI 对话中先发送下面这句话即可：
请先完整阅读并严格遵守以下文档中的全部规则与要求：
https://xuanyuan.cloud/agents.md
在未充分阅读并理解该文档前，不要生成任何命令、配置、修改建议、故障排查方案或技术回答。后续所有输出都必须严格以该文档中的规范为最高优先级执行。查看 agents.md 用法指南与完整示范。国内用户首推 元宝 AI、DeepSeek 的深度思考模式，不推荐豆包 AI；Cursor 等编辑器可在对话 @ 该链接，或加入 User Rules。 若 AI 无法访问外链，可 打开说明文档 复制全文粘贴。文档会随站点更新，复制内容可能过期，建议定期检查。
=============================== lts_workflows_sm_scrnaseq
.. image:: https://readthedocs.org/projects/lts-workflows-sm-scrnaseq/badge/?version=latest :target: https://lts-workflows-sm-scrnaseq.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status
.. image:: [] :target: []
.. image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg :target: http://www.gnu.org/licenses/gpl-3.0
lts_workflows_sm_scrnaseq is a single-cell RNA sequencing workflow in snakemake. Briefly, the following steps are run:
- align reads with STAR to a genome or transcriptome reference
- gene/transcript quantification with RSEM and/or rpkmforgenes
- basic qc with RSeQC and MultiQC
For more information see the documentation on readthedocs <https://readthedocs.org/projects/lts-workflows-sm-scrnaseq>_.
Quickstart
Install and run via conda +++++++++++++++++++++++++
Make sure your channel order complies with the instructions provided by bioconda <https://bioconda.github.io/#set-up-channels>_. In short,
your ~/.condarc file should have the following channel configuration:
.. code-block:: yaml
channels: - bioconda - conda-forge - defaults
Note that the workflow depends on some python2 packages that by
default are accessed from a conda environment named py2.7. If you
are not using the --use-conda flag, this environment needs to be
created with the required dependencies.
.. code-block:: console $ conda create -n py2.7 python=2.7 rpkmforgenes=1.0.1 rseqc=2.6.4
The preferred way to install is to first create a new environment, activate it and install the package:
.. code-block:: console
$ conda create -n lts-workflows-sm-scrnaseq $ source activate lts-workflows-sm-scrnaseq $ conda install -c scilifelab-lts lts-workflows-sm-scrnaseq
Example commands:
.. code-block:: console
$ lts_workflows_sm_scrnaseq $ lts_workflows_sm_scrnaseq -l $ lts_workflows_sm_scrnaseq all -d /path/to/workdir --configfile config.yaml $ lts_workflows_sm_scrnaseq --use-conda all -d /path/to/workdir --configfile config.yaml
The package ships a workflow test. Run
.. code-block:: console
$ pytest -v -rs -s --pyargs lts_workflows_sm_scrnaseq
to see it in action.
Docker image ++++++++++++
.. code-block:: console
$ docker pull scilifelablts/lts-workflows-sm-scrnaseq $ docker run scilifelablts/lts-workflows-sm-scrnaseq $ docker run -v /path/to/workdir:/workspace -w /workspace scilifelablts/lts-workflows-sm-scrnaseq -l $ docker run -v /path/to/workdir:/workspace -w /workspace scilifelablts/lts-workflows-sm-scrnaseq all
Credits
This package was created with Cookiecutter_ and the audreyr/cookiecutter-pypackage_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _audreyr/cookiecutter-pypackage: https://github.com/audreyr/cookiecutter-pypackage
镜像拉取方式
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本，请访问 标签列表页面。
轩辕镜像加速拉取命令点我查看更多 lts-workflows-sm-scrnaseq-ci 镜像标签
docker pull docker.xuanyuan.run/scilifelablts/lts-workflows-sm-scrnaseq-ci:<标签>
DockerHub 原生拉取命令
docker pull scilifelablts/lts-workflows-sm-scrnaseq-ci:<标签>
镜像拉取常见问题
功能
错误码
用户好评
来自真实用户的反馈，见证轩辕镜像的优质服务
oldzhang
运维工程师
Linux服务器
5
"Docker访问体验非常流畅，大镜像也能快速完成下载。"