scDIOR

scDIOR: Single cell RNA-seq Data IO softwaRe

Directory

• scDIOR
  • Overview
  • Installing scDIOR
    • 1. Conda environment
    • 2. Docker image
    • Version control
  • Operation Environment
    • Docker image
    • Conda environment
  • scDIOR demo
    • 1. Comparison of trajectory inferences
    • 2. Data IO for batch correction
    • 3. Data IO for spatial omics data
    • 4. Extended function
  • Reference websites

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Overview

scDIOR software contains two modules, dior for R and diopy for Python. The data conversion was implemented by a ‘.h5’ file of HDF5 format, which harmonizes the different data types between R and Python. The different aspects of single-cell information were stored in HDF5 group with dataset. scDIOR creates 8 HDF5 groups to store core single-cell information, including data, layers, obs, var, dimR, graphs, uns and spatial.

Installing scDIOR

Users install and operate scDIOR following two ways:

1. The environment is created by conda create in which scDIOR is installed.
2. Docker images are available on the jiekailab/scdior-image.

1. Conda environment

The environment is created by conda create in which dior and diopy are installed.

conda create -n conda_env python=3.8 R=4.0

1. R installation:

# for R
install.packages('devtools')
devtools::install_github('JiekaiLab/dior')
# or devtools::install_github('JiekaiLab/dior@HEAD')

2. Python installation:

# for python
pip install diopy

2. Docker image

It is recommend to perform scDIOR in docker image, which ensures that the operating environment remains stable. scDIOR image is available on the jiekailab/scdior-image.

Brief description

1. We first built the basic jupyter image which based on jupyter/base-notebook (jupyter managing Python and R) and fixuid (fixing user/group mapping issues in containers). This basic image is on jiekailab/scdior-image:base-jupyter-notebook1.0.
2. Based on our customized basic image, we built scDIOR image again by Dockerfile. For the content of Dockerfile, it is at this link.

The current latest image contains the following main analysis platforms and software:

R	version	Python	version
R	4.0.5	Python	3.8.8
Seurat	4.0.2	Scanpy	1.8.1
SingleCellExperiment	1.12.0	scvelo	0.2.3
monocle3	1.0.0	anndata	0.7.6
dior	0.1.5	diopy	0.5.2

Version control

At present, scDIOR is widely compatible with Seurat (v3~v4) and Scanpy (1.4~1.8) in different docker image. We configured multiple version docker image (https://hub.docker.com/repository/docker/jiekailab/scdior-image) to confirm that scDIOR can work well between multiple versions of Scanpy and Seurat.

Demo link

Platform	Software	Version	data IO
R	Seurat	v3~v4	☑️
Python	Scanpy	1.4~1.8	☑️

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Operation Environment

1. Docker image

Pulling the scDIOR image by docker pull jiekailab/scdior-image.

1. Local computer

   1. Starting the container of scDIOR image

   docker run -p 8840:8888 \ # Port mapping (local computer and docker image)
           --name scdior \
           -e JUPYTER_ENABLE_LAB=yes \
           -e JUPYTER_TOKEN=1234 \
           -v ~/:/home/jovyan \ # Directory mapping
           --rm \
           -it jiekailab/scdior-image

   2. Opening jupyter notebook in user's browser

   localhost:8840

2. **Remote server **

   1. Logging in server through ssh -L

   ssh -L localhost:8840:localhost:8853 user@remote_ip
   # localhost:8840:localhost:8853 Port mapping (local and remote server)
   #  user@remote_ip remote useer

   2. Starting container of scDIOR image

   IMG=jiekailab/scdior-image
   PORT=8853
   PROJECT=scdior
   MEMORY=64g 
   CWD=$(docker inspect $IMG | grep WorkingDir | head -n 1 | sed 's/.* "//;s/"//g;s/,//g') 
   docker run -p $PORT:8888 \ # Port mapping (remote server  and docker image)
           --name $PROJECT \
           -m $MEMORY \
           -u $(id -u):$(id -g) \ # id mapping (user id and user group)
           -e JUPYTER_ENABLE_LAB=yes \
           -e JUPYTER_TOKEN=1234 \
           -v $PWD:$CWD \ # # Directory mapping
           --rm \
           -it $IMG

   3. Starting jupyter in user's browser

   localhost:8840

2. Conda environment

The environment is activated by conda activate.

1. Local computer

   1. Activating conda environment

   conda activate conda_env

   2. Operating jupyter lab or jupyter notebook

   jupyter lab --port=8840
   # or jupyter notebook --port=8840

   3. Opening jupyter notebook in user's browser

   localhost:8840

2. Remote server

   1. Logging in server through ssh -L

   ssh -L localhost:8840:localhost:8853 user@remote_ip
   # localhost:8840:localhost:8853 Port mapping (local and remote server)
   #  user@remote_ip remote useer

   2. Activating conda environment

   conda activate conda_env

   3. Operating jupyter lab or jupyter notebook

   jupyter lab --port=8853
   # or jupyter notebook --port=8853

   4. Opening jupyter notebook in user's browser

   localhost:8840

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scDIOR demo

Here, we list several demos to show the powerful performance of scDIOR.

1. Comparison of trajectory inferences

Users can perform trajectory analysis using Monocle3 in R, then transform the single-cell data to Scanpy in Python using scDIOR, such as expression profiles of spliced and unspliced, as well as cell layout. The expression profile can be used to run dynamical RNA velocity analysis and results can be projected on the layout of Monocle3.

Demo link

2. Data IO for batch correction

User can employ single-cell data preprocess and normalization method provided by Scanpy, and utilize batches correction method provided by Seurat.

Demo link

3. Data IO for spatial omics data

scDIOR supports spatial omics data IO between R and Python platforms.

Demo link

4. Extended function

1. The function to load ‘.rds’ file in Python directly;
2. The function to load ‘.h5ad’ file in R directly;
3. Command line

Demo link

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Reference websites

1. jupyter docker stacks:
   1. https://github.com/jupyter/docker-stacks
   2. https://jupyter-docker-stacks.readthedocs.io/en/latest/using/selecting.html
2. fixuid: https://github.com/boxboat/fixuid
3. Seurat: https://satijalab.org/seurat/index.html
4. monocle3:https://cole-trapnell-lab.github.io/monocle3/
5. Scanpy: https://scanpy.readthedocs.io/en/stable/index.html
6. Scvelo: https://scanpy.readthedocs.io/en/stable/index.html