https://github.com/lieberinstitute/treg_paper
Analysis for the TREG paper by L. A. Huuki-Myers et al, 2022
https://github.com/lieberinstitute/treg_paper
Last synced: about 1 year ago
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Analysis for the TREG paper by L. A. Huuki-Myers et al, 2022
- Host: GitHub
- URL: https://github.com/lieberinstitute/treg_paper
- Owner: LieberInstitute
- Created: 2022-02-23T20:13:01.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-09-13T19:40:33.000Z (almost 3 years ago)
- Last Synced: 2025-01-28T16:45:49.545Z (over 1 year ago)
- Language: R
- Size: 174 MB
- Stars: 1
- Watchers: 11
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
[](https://zenodo.org/badge/latestdoi/462890831)
# TREG paper
> Next-generation sequencing technologies have facilitated data-driven identification of gene sets with different features including genes with stable expression, cell-type specific expression, or spatially variable expression. Here, we aimed to define and identify a new class of "control" genes called Total RNA Expression Genes (TREGs), which correlate with total RNA abundance in heterogeneous cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single cell RNA-sequencing (RNA-seq) data, available as an R/Bioconductor package at https://bioconductor.org/packages/TREG. We demonstrated the utility of our method in the postmortem human brain using multiplex single molecule fluorescent in situ hybridization (smFISH) and compared candidate TREGs against classic housekeeping genes. We identified _AKT3_ as a top TREG across five brain regions, especially in the dorsolateral prefrontal cortex.
## Overview
In this repository we applied functions from `TREG` to find candidate TREGs in postmortem human brain snRNA-seq data from eight donors and five brain regions (Tran et al, Neuron, 2021 DOI: [10.1016/j.neuron.2021.09.001](https://doi.org/10.1016/j.neuron.2021.09.001)). We then validated top candidate TREGs (_AKT3_, _MALAT1_, and _ARID1B_) in different cell types of dorsolateral prefrontal cortex (DLPFC) using smFISH with RNAscope technology then analyzed with HALO.
## Cite this work
If you use anything in this repository please cite the following publication:
Huuki-Myers LA, Montgomery KD, Kwon SH, Page SC, Hicks SC, Maynard KR, Collado-Torres L (2022). “Data Driven Identification of Total RNA Expression Genes "TREGs" for estimation of RNA abundance in heterogeneous cell types.” _bioRxiv_. doi: [10.1101/2022.04.28.489923](https://doi.org/10.1101/2022.04.28.489923).
A BibTeX entry for LaTeX users is
```
@Article{TREGpaper,
title = {Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types},
author = {Louise A. Huuki-Myers and Kelsey D. Montgomery and Sang Ho. Kwon and Stephanie C. Page and Stephanie C. Hicks and Kristen R. Maynard and Leonardo Collado-Torres},
year = {2022},
journal = {bioRxiv},
doi = {10.1101/2022.04.28.489923},
url = {https://doi.org/10.1101/2022.04.28.489923},
}
```
## Organization
Organization of this project is guided by [R/Bioconductor-powered Team Data Science](https://lcolladotor.github.io/bioc_team_ds/organizing-your-work.html#.YkNW_DfMJfV.)
* `raw-data`: HALO output, RNAscope images, note most of these files are too big for GitHub but can be accessed via [Globus](http://research.libd.org/globus/)
* `code`: R and shell scripts
+ `01_find_tregs`: Run the TREG discovery pipeline, explore results in the snRNA-seq data
+ `02_analyze_halo`: Import, quality control, and analyze halo segmentation data from smFISH validation
* `processed-data`: Data output from analysis as `.Rdata` or `.csv` files
* `plots`: pdfs used in main & supplementary figures are separated from other exploratory plots created in this project.
## Data availability
The raw data for this project and all the results are publicly available through Globus at [jhpce#TREG_paper](http://research.libd.org/globus/jhpce_TREG_paper/index.html).
While every computer system is different, you might benefit from checking the [using Globus to transfer files](https://jhpce.jhu.edu/knowledge-base/using-globus-to-transfer-files/) tutorial for [JHPCE](https://jhpce.jhu.edu/) users.
## License
Attribution-NonCommercial: CC BY-NC
This license lets others remix, tweak, and build upon our work non-commercially as long as they acknowledge our work.
[View License Deed](https://creativecommons.org/licenses/by-nc/4.0) | [View Legal Code](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
## Internal
JHPCE location: `/dcs04/lieber/lcolladotor/deconvolution_LIBD4030/TREG_paper`.