analysis

1 Functions

library(stringi)
source_from_github(repositoy = "DEG_functions",version = "0.2.47")
source_from_github(repositoy = "cNMF_functions",version = "0.4.0",script_name = "cnmf_functions_V3.R")
source_from_github(repositoy = "sc_general_functions",version = "0.1.28",script_name = "functions.R")

2 Data

source_from_github(repositoy = "DEG_functions",version = "0.2.47")

ℹ SHA-1 hash of file is f5bb1cd741d13bded83fe3b6fd43169e29731216

3 ACC all cells UMAP

DimPlot(acc_all)

4 Neuronal signatures

DimPlot(acc_all)

Warning in grSoftVersion() : unable to load shared object ‘/usr/local/lib/R/modules//R_X11.so’: libXt.so.6: cannot open shared object file: No such file or directory

for (neural_name in colnames(neuronal_signatures)) {
  genes = neuronal_signatures[,neural_name,drop=T]
  # Assuming df is your data frame

  
  pathways_scores = FetchData(object = acc_all,vars = genes,slot = "data") %>% 
    mutate_all(~ 2^.)%>% mutate_all(~ .-1) %>%  #covert log(TPM+1) to TPM
    rowwise() %>% mutate(score = mean(c_across(everything()))) #mean
  acc_all  %<>% AddMetaData(metadata = pathways_scores$score,col.name = neural_name)
}

Warning in FetchData.Seurat(object = acc_all, vars = genes, slot = "data") :
  The following requested variables were not found: SNHG29, NOP53
Warning in FetchData.Seurat(object = acc_all, vars = genes, slot = "data") :
  The following requested variables were not found: NOP53
Warning in FetchData.Seurat(object = acc_all, vars = genes, slot = "data") :
  The following requested variables were not found: NOP53

5 CSF3

Single-cell RNA sequencing reveals intratumoral heterogeneity and potential mechanisms of malignant progression in prostate cancer with perineural invasion

6 Neuronal pathways in ACC cancer cells

6.1 dim reduction

VlnPlot(object = acc_all,features = "CSF3",group.by = "patient.ident")+ ylab("log2 (TPM+1)")

DimPlot(acc_cancer,group.by = "patient.ident")

acc_cancer <- RunPCA(acc_cancer, features = VariableFeatures(object = acc_cancer),verbose = F)
ElbowPlot(acc_cancer)

acc_cancer <- FindNeighbors(acc_cancer, dims = 1:10,verbose = F) %>%  FindClusters(resolution = 0.5) %>%  RunUMAP(dims = 1:10,verbose = F)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 951
Number of edges: 28888

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.8456
Number of communities: 7
Elapsed time: 0 seconds

6.2 Scores UMAP

for (pathway_name in names(neuronal_pathways)) {
  genes = neuronal_pathways[[pathway_name]]
  pathways_scores = FetchData(object = acc_cancer,vars = genes,slot = "data") %>% 
    mutate_all(~ 2^.)%>% mutate_all(~ .-1) %>%  #covert log(TPM+1) to TPM
    rowwise() %>% mutate(score = mean(c_across(everything()))) #mean
  acc_cancer  %<>% AddMetaData(metadata = pathways_scores$score,col.name = pathway_name)
}

Warning in FetchData.Seurat(object = acc_cancer, vars = genes, slot = "data") :
  The following requested variables were not found: ADORA3
Warning in FetchData.Seurat(object = acc_cancer, vars = genes, slot = "data") :
  The following requested variables were not found: ADORA3
Warning in FetchData.Seurat(object = acc_cancer, vars = genes, slot = "data") :
  The following requested variables were not found: ASCL1, PHOX2B
Warning in FetchData.Seurat(object = acc_cancer, vars = genes, slot = "data") :
  The following requested variables were not found: ASCL1, PHOX2B
Warning in FetchData.Seurat(object = acc_cancer, vars = genes, slot = "data") :
  The following requested variables were not found: ARX, HOXB1, ASCL1, PRLH, PHOX2B, ISX
Warning in FetchData.Seurat(object = acc_cancer, vars = genes, slot = "data") :
  The following requested variables were not found: HOXB1, PHOX2B
Warning in FetchData.Seurat(object = acc_cancer, vars = genes, slot = "data") :
  The following requested variables were not found: DRD3, MIR1-1, MIR133A1

6.3 Scores violin

plt = FeaturePlot(object = acc_cancer,features = names(neuronal_pathways))
for (i in 1:(length(plt$patches$plots)+1)) {
  plt[[i]] = plt[[i]] + theme(plot.title = element_text(size=10.5))+ labs(color='TPM') 
}
print(plt)

7 ACC2 DEG

 plt = VlnPlot(object = acc_cancer,features = names(neuronal_pathways),group.by = "patient.ident")+theme(plot.title = element_text(size = 3))
 
for (i in 1:(length(plt$patches$plots)+1)) {
  plt[[i]] = plt[[i]] + theme(plot.title = element_text(size=9.5))
  if (i %in% c(1,4,7)) {
    plt[[i]] = plt[[i]]+ylab("TPM")
  }
}
print(plt)

7.1 GSEA- canonical pathways

up = up in ACC2

7.2 GSEA- GO BP

library(hypeR)
genesets <- msigdb_download("Homo sapiens",category="H") %>% append( msigdb_download("Homo sapiens",category="C2",subcategory = "CP"))
all_genes = markers  %>%  arrange(desc(avg_log2FC)) %>% select("avg_log2FC") 
ranked_list   <- setNames(all_genes$avg_log2FC, rownames(all_genes))
hyp_obj <- hypeR_fgsea(signature = ranked_list,genesets =  genesets,up_only = F)
hyp_dots(hyp_obj)

$up

$dn

8 Neuronal pathways in ACC primary cancer cells

genesets <- msigdb_download("Homo sapiens",category="H") %>% append( msigdb_download("Homo sapiens",category="C5",subcategory = "GO:BP"))
all_genes = markers  %>%  arrange(desc(avg_log2FC)) %>% select("avg_log2FC") 
ranked_list   <- setNames(all_genes$avg_log2FC, rownames(all_genes))
hyp_obj <- hypeR_fgsea(signature = ranked_list,genesets =  genesets,up_only = F)

Warning in fgsea::fgseaMultilevel(stats = signature, pathways = gsets.obj$genesets,  :
  For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.

hyp_dots(hyp_obj)

$up

$dn

lum_score = FetchData(acc_cancer_pri,"luminal_over_myo")
lum_score  %<>% mutate (lum_or_myo = case_when(
         luminal_over_myo > 1 ~ "luminal",
         luminal_over_myo < (-1) ~ "myo",
         TRUE ~ "unknown"))
acc_cancer_pri  %<>% AddMetaData(metadata = lum_score$lum_or_myo,col.name = "lum_or_myo")

for (pathway_name in names(neuronal_pathways)) {
  genes = neuronal_pathways[[pathway_name]]
 pathways_scores = FetchData(object = acc_cancer_pri,vars = genes,slot = "data") %>% 
    mutate_all(~ 2^.)%>% mutate_all(~ .-1) %>%  #covert log(TPM+1) to TPM
    rowwise() %>% mutate(score = mean(c_across(everything()))) #mean
  acc_cancer_pri  %<>% AddMetaData(metadata = pathways_scores$score,col.name = pathway_name)
}

Warning in FetchData.Seurat(object = acc_cancer_pri, vars = genes, slot = "data") :
  The following requested variables were not found: ADORA3
Warning in FetchData.Seurat(object = acc_cancer_pri, vars = genes, slot = "data") :
  The following requested variables were not found: ADORA3
Warning in FetchData.Seurat(object = acc_cancer_pri, vars = genes, slot = "data") :
  The following requested variables were not found: ASCL1, PHOX2B
Warning in FetchData.Seurat(object = acc_cancer_pri, vars = genes, slot = "data") :
  The following requested variables were not found: ASCL1, PHOX2B
Warning in FetchData.Seurat(object = acc_cancer_pri, vars = genes, slot = "data") :
  The following requested variables were not found: ARX, HOXB1, ASCL1, PRLH, PHOX2B, ISX
Warning in FetchData.Seurat(object = acc_cancer_pri, vars = genes, slot = "data") :
  The following requested variables were not found: HOXB1, PHOX2B
Warning in FetchData.Seurat(object = acc_cancer_pri, vars = genes, slot = "data") :
  The following requested variables were not found: DRD3, MIR1-1, MIR133A1

9 Lum vs Myo

plt = FeaturePlot(object = acc_cancer_pri,features =  names(neuronal_pathways))

for (i in 1:(length(plt$patches$plots)+1)) {
  plt[[i]] = plt[[i]] + theme(plot.title = element_text(size=10.5))+ labs(color='TPM') 
}
print(plt)