A pipeline for integrative analysis for scTCR- and scRNA-seq data
Project description
immunopipe
Integrative analysis for scTCR- and scRNA-seq data
Requirements & Installation
-
python
:3.7+
- Other python depedencies should be installed via
pip install -U immunopipe
- Other python depedencies should be installed via
-
R
- A bunch of R packages
-
Other
-
Checking requirements
pip install -U pipen-cli-require pipen require immunopipe.pipeline:pipeline <pipeline arguments>
-
Quick way to install the dependencies using conda
conda env install --name <env_name> --file docker/environment.yml # then conda activate <env_name>
Running as a container
Using docker:
docker run \
-w /immunopipe/workdir \
-v $(pwd)/:/immunopipe/workdir \
-v /tmp \
-v $(pwd)/prepared-data:/mnt \
justold/immunopipe:<tag> # or :dev to use the development version
Using singularity:
singularity run -w \ # need it to be writable
--pwd /immunopipe/workdir -B .:/immunopipe/workdir \ # Could use other directory instead of "."
# --contain: don't map host filesystem
# --cleanenv: recommended, to avoid other host's environment variables to be used
# For example, $CONDA_PREFIX to affect host's conda environment
--contain --cleanenv \
docker://justold/immunopipe:<tag> # or :dev to use the development version
# The mount your data directory to /mnt, which will make startup faster
# For example
# -B .:/immunopipe/workdir,/path/to/data:/mnt
# Where /path/to/data is the data directory containing the data files
# You may also want to bind other directories (i.e. /tmp)
# -B <other bindings>,/tmp
# Or you can pull the image first by:
singularity pull --force --dir images/ docker://justold/immunopipe:<tag>
# Then you can replace "docker://justold/immunopipe:<tag>" with "images/immunopipe.sif"
Modules
- Basic TCR data analysis using
immunarch
- Clone Residency analysis if you have paired samples (i.e. Tumor vs Normal)
- V-J usage, the frequency of various V-J junctions in circos-style plots
- Clustering cells and configurale arguments to separate T and non-T cells
- Clustering T cell, markers for each cluster and enrichment analysis for the markers
- Radar plots to show the composition of cells for clusters
- (Meta-)Markers finder for selected groups/clones of cells
- Psedo-bulk GSEA analysis of two groups of cells
- Seurat cluster statistics, including:
- Basic statistics of the clusters (e.g. number of cells in each cluster)
- Gene expressions (e.g. ridge, violin, feature, dot and heatmap plots)
- Dimensional reduction plots
- TCR clustering using CDR3 sequences and the statistics of the clusters
- Cell group distribution (TCR clones/clusters) in Seurat clusters
- Clone heterogeneity (TCR clone distribution) in Seurat clusters
- Metabolic landscape analysis (Ref: Xiao, Zhengtao, Ziwei Dai, and Jason W. Locasale. "Metabolic landscape of the tumor microenvironment at single cell resolution." Nature communications 10.1 (2019): 1-12.)
Documentaion
https://pwwang.github.io/immunopipe
Example
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