scRNAseq Data Analysis Code Muscle Inflammation is Regulated by NF-kB From Multiple Cells to Control Distinct States of Wasting in Cancer Cachexia
This repository contains analysis code for the single cell RNA-seq project carried out by researchers at the Guttridge lab, MUSC and Berto Lab, MUSC
If you use anything in this repository please cite the following publication:
Pre-print URL:
Here a webapp to analyze the data:
Pryce etal SingleCell Mouse KPP Negative
Pryce etal SingleCell Mouse KPP Positive
Pryce etal SingleCell Human Cachexia Negative
Pryce etal SingleCell Human_Cachexia Positive
| directory | contents | code |
|---|---|---|
KPP_MouseData_Analysis |
Code and Figuers for Mouse KPP Data | |
KPP-output_Positive |
Output data of the initial clustering and integration of Positive Dataset - CtrlPos and KppPos | 01_Seurat_Clustering_Positive.R \ 03_Doubletting_Positive.R \ 05_FindMarkers_Positive.R \ 07_CellCycleScoring.R |
KPP-output_relabel_Positive |
Output data of Cell annotation | 08_Relable_Positive.R |
KPP-output_Negative |
Output data of the initial clustering and integration of Negative Dataset - CtrlNeg and KppNeg | 02_Seurat_Clustering_Negative.R \ 04_Doubletting_Negative.R \ 06_FindMarkers_Negative.R \ 07_CellCycleScoring.R |
KPP-output_relabel_Negative |
Output data of Cell annotation | 09_Relable_Negative.R |
KPP-output_DGE |
Output of Differential gene expression for Positive and Negative data | 10_DGE.R |
KPP-output_Figure_Positive |
Output data of final figures and FAPs subset analysis. | 11_Final_Figuers_Positive.R |
KPP-output_Figure_Negative |
Output data of final figures and Macrophage subset analysis. | 11_Final_Figuers_Negative.R |
KPP-shinyApp |
Output of the ShinyApp. | 13_PreprocessForShiny_Positive.R\ 14_ShinyApp_Positive.R\ 15_PreprocessForShiny_Negative.R \ 16_ShinyApp_Negative.R |
| directory | contents | code |
|---|---|---|
Cachexia_Human |
Code and Figuers for Cachexia Human Data | |
Cachexia_Human-output_Negative |
Output data of the initial clustering and integration of Negative Dataset - Neg_WS_PDAC, Neg_Control, Neg_C_PDAC | 01_Seurat_Clustering_Negative.R \ 03_Doubletting_Negative.R \ 05_CellCycleScoring.R \ 07_FindMarkers_Negative.R |
Cachexia_Human-output_relabel_Negative |
Output data of Cell annotation | 09_Relable_Negative.R |
Cachexia_Human-output_Positive |
Output data of the initial clustering and integration of Positive Dataset - Pos_WS_PDAC, Pos_Control, Pos_C_PDAC | 02_Seurat_Clustering_Positive.R \ 04_Doubletting_Positive.R \ 06_CellCycleScoring.R \ 08_FindMarkers_Negative.R |
Cachexia_Human-output_relabel_Positive |
Output data of Cell annotation | 10_Relable_Positive.R |
Cachexia_Human-output_DGE |
Output of Differential gene expression for Positive and Negative data | 11_DGE_Negative.R \ 12_DGE_Positive.R |
Cachexia_Human-output_Figure_Negative |
Output data of final figures and Macrophage subset analysis. | 13_Negative_FinalPlots.R |
Cachexia_Human-output_Figure_Positive |
Output data of final figures and FAPs subset analysis. | 14_Positive_FinalPlots.R |
Cachexia_Human-shinyApp |
Output of the ShinyApp. | 15_PreprocessForShiny_Negative.R\ 15_ShinyApp_Negative.R\ 16_PreprocessForShiny_Positive.R \ 16_ShinyApp_Positive.R |