How to analyze DEGs in scRNA-seq data?

Question

[kayah]

Hello I'm graduate student who try to analyze single cell. I analyze this data for finding aging markers. But there's no one to help me....

So I don't know whats wrong in my code Please help me. So my final goal is find aging markers but results showed there's no aging markers in my data.

But the Dimplot(res=0.6) shows me there's definite difference between Young and Old type...

I don't know how can handle this problem

I'm so desperate.

Thank you!

young1 <-readRDS("~/Desktop/GSE137869/GSE137869_aging/WAT_M_Y.rds")
young2 <-readRDS("~/Desktop/GSE137869/GSE137869_aging//WAT_F_Y/WAT_F_Y.rds")
old1 <- readRDS("~/Desktop/GSE137869/GSE137869_aging/WAT_M_O/WAT_M_O.rds")
old2 <- readRDS("~/Desktop/GSE137869/GSE137869_aging//WAT_F_O/WAT_F_O.rds")

BiocManager::install('glmGamPoi')
library(glmGamPoi)

WAT <- merge(young1, y = c(young2, old1, old2), 
             add.cell.ids = c("young1", "young2", "old1", "old2"),
             project = "WAT") %>%
  NormalizeData() %>%
  FindVariableFeatures(selection.method = "vst", nfeatures = 2000)%>%
  ScaleData()%>%
  SCTransform(vars.to.regress = c("percent.mt"))

WAT <- RunPCA(WAT, assay = "SCT", npcs = 50)


[e-mail]$type <- c(rep("Young", ncol(young1)),
                        rep("Young", ncol(young2)),
                        rep("Old", ncol(old1)),
                        rep("Old", ncol(old2)))

View([e-mail])


harmonized_WAT <- RunHarmony(WAT,
                             group.by = "type",
                             reduction ="pca",
                             assay.use = "SCT",
                             reduction.save = "harmony")

harmonized_WAT <- RunUMAP(harmonized_WAT, reduction = "harmony",
                          assay = "SCT", dims = 1:30)

harmonized_WAT <- FindNeighbors(object = harmonized_WAT, reduction = "harmony")
#harmonized_WAT <- FindClusters(harmonized_WAT, resolution = c(0.6))
harmonized_WAT <- FindClusters(harmonized_WAT, resolution = c(0.2, 0.4, 0.6, 0.8, 1.0, 1.2))

View([e-mail])


Idents(object = harmonized_WAT) <- "SCT_snn_res.0.6"
DimPlot(harmonized_WAT, 
        label = TRUE, 
        split.by = "type")


harmonized_WAT <- PrepSCTFindMarkers(harmonized_WAT)

devtools::install_github('immunogenomics/presto')

View([e-mail])

WAT_aging_markers <- FindAllMarkers(object = harmonized_WAT,
                          only.pos = TRUE,
                          assay = "SCT",
                          group.by = "type",
                          logfc.threshold = 0.5,
                          min.pct = 0.05,
                          test.use = "wilcox")

This is my dataset

This is Dimplot I draw

[GenoMax]

Looks like your second image was not properly attached. Please upload again.

[kayah]

thank you!! I upload it again

Answer 1

[swbarnes2]

There was a reason why this experiment was done? Right? That should guide what questions you are asking.

Off hand, I'd start by examining the gene markers in each cluster, figure out what they all are. Especially figure out clusters like 4 and 8, which are kind of gone in one age or the other.

Then find DE genes between the young and old cells of the same cluster.