Architecture of androgen receptor pathways amplifying glucagon-like peptide-1 insulinotropic action in male pancreatic β-cells
This repository contains coding scripts utilized for the analysis performed in the "Architecture of androgen receptor pathways amplifying glucagon-like peptide-1 insulinotropic action in male pancreatic β-cellse" publication (Xu-Qadir et. al, 2023). The purpose of providing the code here is to allow for transparency and robust data-analysis reproducibility. Most of the steps used for data analysis and visualization have been optimised for an average computing environment (for the year 2023). Some analyses however, require a high-performace computing environment (see computing environment). The methodology has already been described extensively in the manuscript. However, this analysis relies heavily on powerful scRNAseq analysis algorithms developed by the Satija lab, namely Seurat (Butler et al., 2018: Nature Biotechnology; Stuart et al., 2018: Cell) (for a complete list of dependencies and code utilized see analysis & visualization programs).
Data files utilized in this analysis have been deposited in the Gene Expression Omnibus (GEO), gene expression data repository at the NIH. Data are part of the GSE131886 high-thoroughput sequencing repository and can be found here. Contact lead author for seurat object.
We povide raw FASTQ files generated from single-cell cDNA libraries sequenced by the Illumina sequencing platform, along with unfiltered post-alignment count files generated by the Cellranger software. In addition we also provide a gene expression matrix containing data on filtered gene counts across our dataset.
These are sequencing reads generated by the Illumina sequencing platform. Files contain raw reads and sequencing efficiency information. These are the input files for the Cellranger software. and can be found here: GSE201256
This contains data outputs of Cellranger, which was run using default settings. Code used to analyze data is a part of this repository. This data contains filtered/unfiltered count files for gene expression across barcodes/cells.
Preliminary data-analyses involving n=3 ctrl + n=3 DHT Tx deidentified human cadaveric Islet samples.
- Install R
- Install Rstudio
- Once you have installed R and RStudio, you can run the script.
- If you need help understanding how commands are run in R use the [ctrl + enter] command or please visit here.
- If you run into problems, please open a new issue, you can do this by going to 'issues' and clicking on the 'new issue' icon.
- Processor: Intel core i9-9900 (16cores x 16 threads)
- RAM: 128GB DDR3
- OS: Windows 10 Enterprise (x64 bit)
- Dr. Franck Mauvais-Jarvis MD PhD. - Dept of Medicine, Tulane University - to contact please Email
- NIDDK
- VA