load the required Library

library('Seurat')

## Loading required package: SeuratObject

## Loading required package: sp

## 'SeuratObject' was built under R 4.3.2 but the current version is
## 4.3.3; it is recomended that you reinstall 'SeuratObject' as the ABI
## for R may have changed

## 'SeuratObject' was built with package 'Matrix' 1.6.4 but the current
## version is 1.6.5; it is recomended that you reinstall 'SeuratObject' as
## the ABI for 'Matrix' may have changed

## 
## Attaching package: 'SeuratObject'

## The following object is masked from 'package:base':
## 
##     intersect

library('ggplot2')
library('plyr')
library('dplyr')

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library('viridis')

## Loading required package: viridisLite

library('scCustomize')

## scCustomize v2.1.2
## If you find the scCustomize useful please cite.
## See 'samuel-marsh.github.io/scCustomize/articles/FAQ.html' for citation info.

library('ggthemes')
library('plotly')

## 
## Attaching package: 'plotly'

## The following objects are masked from 'package:plyr':
## 
##     arrange, mutate, rename, summarise

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library('cowplot')

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggthemes':
## 
##     theme_map

load the seurat 5 RDS file as input

LMN_Final = readRDS("LMN_Final_MB_Seurat5.rds")

#Verification of cluster

Idents(LMN_Final) <- "seurat_clusters"

DimPlot(LMN_Final, reduction = "umap", label = FALSE, pt.size = 1, label.size = 0,
    group.by = "injury.status", alpha = 0.2, split.by = "injury.status", cols = c("#EB984E",
        "#7FB3D5"), order = FALSE) + NoLegend() + NoAxes()

ggsave("Figure_1_B.png", height = 4, width = 8, dpi = 300, units = "in")
DimPlot(LMN_Final, reduction = "umap", label = FALSE, pt.size = 1, label.size = 0,
    group.by = "injury.status", alpha = 0.2, cols = c("#EB984E", "#7FB3D5"), order = FALSE) +
    NoLegend() + NoAxes()

#Verification of cluster and the marker genes

library(RColorBrewer)
# Define the number of colors you need
n <- 40  # Adjust based on your requirement
LMN_Final_DotPlot2 = read.csv("LMN_Final_Dotplot2.csv")
pal <- brewer.pal(n, "Spectral")

## Warning in brewer.pal(n, "Spectral"): n too large, allowed maximum for palette Spectral is 11
## Returning the palette you asked for with that many colors

pal_inverted <- rev(pal)
Idents(LMN_Final) <- "manual.clusters.2"
DotPlot_scCustom(LMN_Final, features = LMN_Final_DotPlot2$Marker, dot.scale = 6,
    dot.min = 0.01, colors_use = pal_inverted) + theme(axis.text.x = element_text(angle = 90,
    hjust = 1), axis.text = element_text(size = 15))

## Warning: Removed 141 rows containing missing values or values outside the scale range
## (`geom_point()`).

DimPlot_scCustom(seurat_object = LMN_Final, figure_plot = TRUE, label = T)

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).

Idents(LMN_Final) <- "manual.clusters.1"
DotPlot_scCustom(LMN_Final, features = LMN_Final_DotPlot2$Marker, dot.scale = 6,
    dot.min = 0.01, colors_use = pal_inverted) + theme(axis.text.x = element_text(angle = 90,
    hjust = 1), axis.text = element_text(size = 15))

## Warning: Removed 422 rows containing missing values or values outside the scale range
## (`geom_point()`).

DimPlot_scCustom(seurat_object = LMN_Final, figure_plot = TRUE, label = T)

## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).

#DEG analysis

Idents(LMN_Final) <- "manual.clusters.2"
LMN_Final <- JoinLayers(LMN_Final)
LMN_CST_InjVUninj <- FindMarkers(LMN_Final,
                                 subset.ident = "CST",
                                 group.by = "injury.status",
                                 ident.1 = "injured",
                                 ident.2 = "uninjured",
                                 logfc.threshold = 0,
                                 min.pct = 0.01)


write.csv(x = LMN_CST_InjVUninj, 
          file = "LMN_CST_InjvUninj.csv",
          quote = FALSE)


LMN_HYP_InjVUninj <- FindMarkers(LMN_Final,
                                 subset.ident = "HYP",
                                 group.by = "injury.status",
                                 ident.1 = "injured",
                                 ident.2 = "uninjured",
                                 logfc.threshold = 0,
                                 min.pct = 0.01)


write.csv(x = LMN_HYP_InjVUninj, 
          file = "LMN_HYP_InjvUninj.csv",
          quote = FALSE)

LMN_RN_InjVUninj <- FindMarkers(LMN_Final,
                                 subset.ident = "RN",
                                 group.by = "injury.status",
                                 ident.1 = "injured",
                                 ident.2 = "uninjured",
                                 logfc.threshold = 0,
                                 min.pct = 0.01)


write.csv(x = LMN_RN_InjVUninj, 
          file = "LMN_RN_InjvUninj.csv",
          quote = FALSE)


LMN_DP_InjVUninj <- FindMarkers(LMN_Final,
                                subset.ident = "DP",
                                group.by = "injury.status",
                                ident.1 = "injured",
                                ident.2 = "uninjured",
                                logfc.threshold = 0,
                                min.pct = 0.01)


write.csv(x = LMN_DP_InjVUninj, 
          file = "LMN_DP_InjvUninj.csv",
          quote = FALSE)

LMN_MED_InjVUninj <- FindMarkers(LMN_Final,
                                subset.ident = "MED",
                                group.by = "injury.status",
                                ident.1 = "injured",
                                ident.2 = "uninjured",
                                logfc.threshold = 0,
                                min.pct = 0.01)


write.csv(x = LMN_MED_InjVUninj, 
          file = "LMN_MED_InjvUninj.csv",
          quote = FALSE)

LMN_ALL_InjVUninj <- FindMarkers(LMN_Final,
                                 group.by = "injury.status",
                                 ident.1 = "injured",
                                 ident.2 = "uninjured",
                                 logfc.threshold = 0,
                                 min.pct = 0.01)


write.csv(x = LMN_ALL_InjVUninj, 
          file = "LMN_ALL_InjvUninj.csv",
          quote = FALSE)

LMN Injury

Manoj

2024-05-01

load the required Library

load the seurat 5 RDS file as input