PascaDiMagliano-Lab / Gift-of-Life-Public-Repository

This is repo for the code used in Gift-of-Life paper from Pasca lab

This repository contains code used for single-cell and spatial transcriptomics data analysis in the

Carpenter ES, Elhossiny AM, Kadiyala P, Li J, McGue J, Griffith BD, Zhang Y, Edwards J, Nelson S, Lima F, Donahue KL, Du W, Bischoff AC, Alomari D, Watkoske HR, Mattea M, The S, Espinoza CE, Barrett M, Sonnenday CJ, Olden N, Chen CT, Peterson N, Gunchick V, Sahai V, Rao A, Bednar F, Shi J, Frankel TL, Pasca di Magliano M. Analysis of donor pancreata defines the transcriptomic signature and microenvironment of early neoplastic lesions. Cancer Discov. 2023 Apr 6:CD-23-0013. doi: 10.1158/2159-8290.CD-23-0013 .PMID: 37021392.

You can explore the single cell data of this paper using this ShinyApp (https://pascadimaglianolab.shinyapps.io/SC_Pancreas_Atlas/)

How to use this repo to reproduce the analysis done in the paper?

• data folder contains instructions for downloading data from GEO and samples metadata spreadsheet, along with other low-size input data.
• scripts folder contains sub-folders for each section of the analysis.
• results folder will be the output directory for the scripts.
• src folder contains utility functions that are called within other scripts for a cleaner code.

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Data Acquisition

Instructions for data acquisition is described under Data_Acquisition folder. Input data will be stored under data/ folder.

Data Processing

Fastq files were aligned to hg38 using CellRanger v6. Ambient RNA correction was done using SoupX as described in 01_ambient_RNA_correction.R. Samples underwent QC to remove low quality cells using 02_data_procesisng_QC.R. Samples were then integrated using the recommeded Seurat rPCA MNN integration using the 03_data_integration_script.R. Clusters were then labeled using previously publised markers using 04_clusters_annotation.R

Exploratory Analysis

Differential abundance analysis was done using miloR R Package as described in 01_differential_abundance_with_miloR.R. Pseudobulk analysis was done using 02_Pseudobulk.R. CellChat was used to perform Cell-cell communication analysis as described in 03_CellChat_analysis.R. Interactome circos plots were generated using 04_interactome_analysis.R script. KRAS and previously published gene set scoring was done utilizing AUCell as described in 05_geneset_scoring.R script. Differential gene expression analysis and UMAP plotting was done using Seurat v4 functions; FindMarkers(), DimPlot() FeaturePlot() and VlnPlot()

Spatial Transcriptomics

Spatial transcriptomics was done using the Nanostring GeoMx platform. Segments QC was done to remove the low quality segments and genes with low detection level as recommeded by Nanostring using GeoMxWorkflows R Package, then, differential gene expression was done in 01_DGE_analysis.R. Mapping of marker gene sets derived from spatial transcriptomics onto the single cell data was done using 02_mapping_markers_to_SC.R

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If you have any questions, please feel free to reach out to Marina Pasca di Maglinao ([e-mail]), Eileen Carpenter ([e-mail]), or Ahmed Elhossiny ([e-mail])