Skip to content

saezlab/eccb2022_sc_funcomics

Repository files navigation

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

About

Functional analysis of single-cell transcriptomics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published