An R-based pipeline for analysing scRNAseq data from VU40T human sublingual epithelial cells and 3T3-murine fibroblast cells derived from 3D-raft cultures
README.md
VU40T_analysis/
├── Scripts/ # R and shell scripts used in the pipeline
│ ├── createscrnaseqsamplesheet.sh # automate samplesheet creation for nf-core/scrnaseq input
│ ├── runnf-corescrnaseq.slurm # slurm shell script to run nf-core/scrnaseq (v. 4.0.0) on raw FastQ data
│ ├── main.R # R pipeline wrapper script for all R parts of analysis
│ ├── species_mixing.R # loads in both human and mouse alignments and assigns species labels to cell barcodes
│ ├── pre-process_QC.R # QC and filtering both fibroblasts and epithelial cells
│ ├── DoubletFinder.R # Pre-processing cont. and single sample analysis
│ ├── pre-integration_seurat.R # initial clustering and annotations for singlets for each sample
│ ├── Integration_seurat.R # Integration of preprocessed data from each species seperately, outputs umaps, pseudobulk volcanos and GSEAs and marker dotplots.
│ ├── GSEA_plotting.R # Script to plot GSEA results from DAVID as dotplots
│ ├── epithelial_pseudotime.R # pseudotime analysis for epithelial cells with monocle3
│ ├── fibroblast_pseudotime.R # pseudotime analysis for fibroblast cells with monocle3
│ ├── runpyscenic.sh # shellscript to run pyscenic on the curated TF list with gene names mapped onto older aliases in ranking DB
│ └── cellchat.R # Ligand-receptor cellular communication analysis between epithelial and fibroblast clusters with cellchat
├── human_multiqc_report.html # from nf-core/scrnaseq pipeline run
├── Mixed_species_plots/ # Output species assignment figures
├── VU40T_species_assignment_by_reads.csv # species assignment table
├── Seperate_samples/ # figs and tables from pre-integration_seurat.R -> DoubletFinder.R for both human and mouse alignments.
│ ├── Doublet_detection/ # doublets summary from doubletfinder on human alignment
│ ├── Plots/ # Plots from seurat analysis on seperate samples using human alignment only - pre-integration
│ └── Mouse/ # plots and markers from seurat analysis on seperate samples using mouse alignment only - pre-integration
│ │ ├── Plots/ # final pre integration plots on mouse alignment only
│ │ └── QC_plots/ # diagnostic plots from pre-integration_seurat.R - mouse alignment only
└── Integrated/ # figs and tables from Integration_seurat.R for human alignments in this dir and Mouse alignments in a subdir, as well as regulon ranking tbl from pySCENIC using wilcoxon-rank test.
│ ├── CellChat # incoming and outgoing degree Centrality scores from cellchat analysis
│ │ ├── Plots/ # Plots from Cellchat analysis on joined epithelial and fibroblast seurat objects.
│ ├── Plots/ # Plots from seurat analysis on Integrated dataset using human alignment only. includes pseudobulk and bulk plots as well as pySCENIC plots.
│ ├── Pseudotime_humanOnly/ # Epithelial pseudotime DEG list which passed monocle3 QC
│ └── Mouse/ # Integrated Fibroblast (mouse alignment only) tables amd plots
│ │ └── Plots/ # Plots from seurat analysis on Integrated dataset using mouse alignment only. includes pseudobulk, bulk and pseudotime plots. pseudotime DEGs are in the Pseudotime_MouseOnly/ subdirThis analysis was run using R (v. 4.4.1) and the following R packages:
sessionInfo()
[1] clusterProfiler_4.12.0
[2] enrichplot_1.24.0
[3] msigdbr_24.1.0
[4] pagoda2_1.0.12
[5] igraph_2.1.4
[6] Matrix_1.7-3
[7] clustree_0.5.1
[8] ggraph_2.2.1
[9] patchwork_1.3.1
[10] DoubletFinder_2.0.6
[11] parallelly_1.45.0
[12] biomaRt_2.60.0
[13] ggridges_0.5.6
[14] ggplot2_3.5.2
[15] SeuratWrappers_0.4.0
[16] monocle3_1.4.26
[17] SingleCellExperiment_1.26.0
[18] SummarizedExperiment_1.34.0
[19] GenomicRanges_1.56.2
[20] GenomeInfoDb_1.42.3
[21] IRanges_2.40.1
[22] S4Vectors_0.44.0
[23] MatrixGenerics_1.16.0
[24] matrixStats_1.5.0
[25] Biobase_2.66.0
[26] BiocGenerics_0.52.0
[27] dplyr_1.1.4
[28] Seurat_5.3.0
[29] SeuratObject_5.1.0
[30] sp_2.2-0
[31] EnhancedVolcano_1.24.0
[32] ggrepel_0.9.6
[33] CellChat_2.1.2and Python (v. 4.4.1) libraries/tools:
pySCENIC v 0.12.1