https://github.com/saezlab/eccb2022_sc_funcomics
Functional analysis of single-cell transcriptomics
https://github.com/saezlab/eccb2022_sc_funcomics
Last synced: 7 months ago
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Functional analysis of single-cell transcriptomics
- Host: GitHub
- URL: https://github.com/saezlab/eccb2022_sc_funcomics
- Owner: saezlab
- License: gpl-3.0
- Created: 2022-09-15T08:32:02.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-09-18T23:14:22.000Z (almost 4 years ago)
- Last Synced: 2025-01-22T07:37:37.731Z (over 1 year ago)
- Language: HTML
- Homepage:
- Size: 19.3 MB
- Stars: 7
- Watchers: 3
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Functional analysis of single-cell transcriptomics
Recent advances in omics technologies have led to a rapid increase in the popularity and applications of single-cell data-sets. Standard analyses and workflows solely focus on basic preprocessing steps followed by the identification of differentially expressed genes, and their subsequent use in cell-type annotation and characterization of biological processes. In this tutorial, we show how prior knowledge can be used to extend each of the aforementioned steps, as well as to extract clear biological insights. Furthermore, we provide an introduction to the state-of-the-art intercellular communication methods, as tools for systems-level hypothesis generation tools in single-cell data. We thus cover a diverse set of prior knowledge resources and show how these can be used to support and extend analysis steps ranging from quality control, cell-type annotation and transcription factor and cytokine activity inference. Finally, we show how advanced functional omics analyses can be used to refine cell-cell communication predictions.
## Data Availability
Please download the data folder from here:
https://figshare.com/articles/dataset/Tutorial_Data/21152242
## 1. Single-cell processing, enrichment and footprint analysis
### Instalation
To install the python dependencies run:
```
# install mamba (faster than conda)
pip install mamba
# create an environment with the necessary packages
mamba env create -f scanpy_env.yml --name scanpy
# activate the environment
conda activate scanpy
# add environment as kernel for jupyter-lab
python -m ipykernel install --user --name=scanpy --display-name='scanpy'
```
Download the raw scRNA-seq data and decompress it:
```
wget "https://figshare.com/articles/dataset/Tutorial_Data/21152242"
unzip data.zip
```
Or alternatively, just download the data from the link
Then to start working run:
```
jupyter-lab
```
## 2. Cell-cell comunication inference and linking to downstream events
### Installation
```
conda activate base
mamba env create -f seurat_env.yml --name seurat
```
Then to start working run:
```
conda activate seurat
rstudio
```
## File Description
`1_sc_analysis.ipynb` - Jupyter Note book with Part 1
`2_cell_comm.Rmd` (`2_cell_comm.html`) - R Markdown (and corresponding html) with Part 2
`scanpy_env.yml` - A yaml file with the conda environment needed for Part 1
`/src` - directory with figures and helper functions
`NicheNet_FAQ.md` - Some frequently asked questions (and answers) concerning NicheNet
`nichenet_wrapper.R` - NicheNet wrapper that will be used at the end of `2_cell_comm.Rmd`
## Author Names (Alphabetically)
Pau Badia i Mompel
Robin Browaeys
Daniel Dimitrov
Yvan Saeys
Julio Saez-Rodriguez
A join effort by Sae(ys|z) labs!
## Contact us!
Pau - @PauBadiaM; pau.badia(at)uni-heidelberg.de
Daniel - @DanielBDimitrov; daniel.dimitrov(at)uni-heldeberg.de
Robin - @RobinBrowaeys; robin.browaeys(at)irc.vib-UGent.be
## We are also looking for people. Join us!! :)
Post-doc and PhD position at UKHD
https://saezlab.org/?#jobs
4-year PhD position available at UGent
yvan.saeys(at)ugent.be