In this repository is presented demo files and R scripts to reproduce one of the bioinformatic pipelines (framework A) and some of the visualization available in the manuscript Multi-omic landscaping of human midbrains identifies disease-relevant molecular targets and pathways in advanced-stage Parkinson's disease, published in Clinical Translational Medicine (2022).
Particularly, here you can find functions to ellaborate differential expression analyses of RNA-seq data (total and small RNA) using the Bioconductor packages DESeq2 and TCGAbiolinks.
Demo files are provided to conduct the differential expression analysis. This can be found in the Data/ folder.
Nevertheless, please check the manuscript to access the multi-omics data used for the study.
This project was conducted in R software.
As mention previously, here we present one of the bioinformatic pipelines available in our manuscript (framework A), in the R Scripts/ folder. However, for the other bioinformatic methodology (framework B), please revert to this GitHub page.
Bear in mind, here is only provided an adaptation of the original source code.
All the necessary R package dependencies are
- ggplot2
- magrittr
- ggpubr
- readxl
- readr
- dplyr
- nortest
- tidyverse
- plyr
- ashr
- plm
These packages and dependencies should be installed a priori using the install.packages() function and complemented by the library() function to be ready to use.
Furthermore, we also leveraged packages available in the Bioconductor:
- edgeR
- limma
- biomaRt
- DESeq2
- apeglm
- vsn
- TCGAbiolinks
Similarly, to employ these packages, first install the BiocManager package using install.packages("BiocManager"), and later the packages above using BiocManager::install().
For the RNA decomposition, we used the immunedeconv R package. This can be installed using the query:
install.packages("remotes")
remotes::install_github("icbi-lab/immunedeconv")
For any inquiries related to this work, please contact me via e-mail ana.galhoz@helmholtz-muenchen.de