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 [email protected]