This is the code for manuscript titled "Analysis of the Human Kidney Transcriptome and Plasma Proteome Identifies Novel Biomarkers of Proximal Tubule Maladaptation to Injury".
Author lists:
Authors: Yumeng Wen1, Emily Su2, Leyuan Xu3, Steven Menez1, Dennis Moledina3, Paul M. Palevsky4, Lloyd Cantley3, Patrick Cahan2, Chirag R. Parikh1*, for the Kidney Precision Medicine Project (KPMP) and Translational Investigation of Biomarker Endpoint of Acute Kidney Injury (TRIBE-AKI) Consortia
Affiliations:
Department of Medicine/Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Department of Medicine/Section of Nephrology, Yale School of Medicine, New Haven, CT, USA. Renal-Electrolyte Division, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. *Corresponding author. Email: [email protected]; Telephone: 410-955-5268
Datasets can be obtained by contacting the KPMP consortium; If you have any questions about the code, please contact first author at [email protected].
The general work flow is:
- sequence alignment and library generation: cellranger v7
- remove ambient RNA using cellbender
- remove doublet using DoubletDetection
- Preprocessing of combined datasets: including RPCA for data integration. remove clusters that express marker genes of more than 2 cell types
- Select PT cluster: redo RPCA for data integration, remove one cluster that is potential doublet.
- Select TAL cluster: repeat step 5.
- DEG of PT subcluter in AKI samples only, then perform GSEA using FGSEA package.
- Select PT subcluster using Scanpy, create h5ad dataset, use Epoch to reconstruct GRN topology, and use SCENIC to reconstruct cis-regulatory network and to calculate cellular enrichment.
- Integrate DEG of PT subcluster in AKI samples with proteomics findings in TRIBE-AKI.