Commit 51ffc550 authored by herault's avatar herault
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correcting README

parent d6ea9e28
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## Abstract
Hematopoietic stem cells (HSCs) are the guarantor of the proper functioning of hematopoiesis due to their incredible diversity of potential. During aging the heterogeneity of mouse HSCs evolves, which contributes to the deterioration of the immune system. Here we address the transcriptional plasticity of HSC upon aging at the single-cell resolution. Through the analysis of 15,000 young and aged transcriptomes, we reveal 15 clusters of HSCs unveiling rare and specific HSC potentials that change with age. Pseudotime ordering complemented with regulon analysis showed that the consecutive differentiation states of HSC are delayed upon aging. By analysing cell cycle at the single cell level we highlight an imbalance of cell cycle regulators of very immature aged HSC that may contribute to their accumulation in an undifferentiated state.
Our results therefore establish a reference map of young and old mouse HSC differentiation and reveal a potential mechanism that delay aged HSC differentiation.
Our results therefore establish a reference map of young and aged mouse HSC differentiation and reveal a potential mechanism that delay aged HSC differentiation.
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## Analysis and script
We provide in this repository the snakemake workflow (Snakefile.py) and its configuration (config/snakemake_workflow.yml) we developped to analyse the single cell RNA seq data.
It is build mainly on R scripts with [Seurat v3](https://satijalab.org/seurat/) for the sample integration and the clustering of the cells and with [Monocle v2](http://cole-trapnell-lab.github.io/monocle-release/docs/) for the pseudotime ordering of the cells. It uses [pySCENIC](https://pyscenic.readthedocs.io/en/latest/) command line tools for the SCENIC part of the workflow.
It is built mainly on R scripts with [Seurat v3](https://satijalab.org/seurat/) for the sample integration and the clustering of the cells and with [Monocle v2](http://cole-trapnell-lab.github.io/monocle-release/docs/) for the pseudotime ordering of the cells. It uses [pySCENIC](https://pyscenic.readthedocs.io/en/latest/) command line tools for the SCENIC part of the workflow.
See the material and method section of our manuscript for more details.
The final step of the workflow produce an html report with the figure shown in our publication.
See the materials and methods section of the corresponding manuscript for more details.
The final step of the workflow produce an html report with the figures shown in the publication.
Our html produced from this [Rmarkdown file](report/final_report.Rmd) can be download [here](report/our_final_report.html) and view in a html browser.
......@@ -41,14 +41,14 @@ Each matrix is stored in Market Exchange Format (MEX). It also contains TSV file
You can download all these files in a tar archive on GEO under accession code GSE147729 (supplementary data section).
To run the workflow you need to create a folder input/GEO in the downloaded repository and place the tar archive inside.
You can now activate snakemake env and then launch the workflow.
You can then activate snakemake env and then launch the workflow.
### Activate snakemake environment
conda activate snakemake
### Command line to launch the workflow
being in the downloaded repository:
Being in the downloaded repository:
snakemake -j 20 \
-nps Snakefile.py \
......@@ -57,7 +57,7 @@ being in the downloaded repository:
### command lines to launch the snakemake workflow on a cluster with slurm
First being in the downloaded repository create directory where standard output and error will be written:
First being in the downloaded repository, you have to create a directory where standard output and error will be written:
mkidr -p cluster/snakemake/
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