ACC1 analysis V6 integrated

1 Functions

source_from_github(repositoy = "DEG_functions",version = "0.2.53")
source_from_github(repositoy = "HMSC_functions",version = "0.1.14",script_name = "functions.R")
source_from_github(repositoy = "cNMF_functions",version = "0.3.9",script_name = "cnmf_function_Harmony.R")
source_from_github(repositoy = "sc_general_functions",version = "0.1.34",script_name = "functions.R")

toc_tabs_level =3

2 Data

acc1_cancer_cells = readRDS("./Data/acc1_cancer_cells_2500features_integrated_V5.RDS")

all_acc_cancer_cells = readRDS("./Data/acc_cancer_cells_V5_integrated_primary.RDS")
acc_all_cells = readRDS("./Data/acc_tpm_nCount_mito_no146_15k_with_ACC1_.RDS")
acc_cancerCells_noACC1 = subset(all_acc_cancer_cells,subset = patient.ident != "HMSC")

luminal_pathways = c("CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_DN","CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_UP","CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_DN","CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_UP","HUPER_BREAST_BASAL_VS_LUMINAL_DN","LIM_MAMMARY_LUMINAL_PROGENITOR_UP","SMID_BREAST_CANCER_LUMINAL_B_UP" )

# add luminal pathwaysx 
luminal_gs = msigdbr(species = "Homo sapiens") %>% as.data.frame() %>% dplyr::filter(gs_name %in% luminal_pathways)%>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()

3 HMSC vs ACC

3.1 UMAP

DimPlot(all_acc_cancer_cells,group.by = "patient.ident",label = T)

FeaturePlot(object = all_acc_cancer_cells,features = c("MYB"),pt.size = 2)

FeaturePlot(object = all_acc_cancer_cells,features = c("kaye_acc_score"),pt.size = 1)

pdf("./Figures/kaye_acc_score_AllCancerCells.pdf")
FeaturePlot(object = all_acc_cancer_cells,features = c("kaye_acc_score"),pt.size = 1)
dev.off()

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3.2 enrichment analysis

enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells),fdr_Cutoff = 0.01,ident.1 = "HMSC",ident.2 = "ACC",show_by = 1)

3.3 Cell cycle score

hallmark_name = "GO_MITOTIC_CELL_CYCLE"
genesets  =getGmt("./Data/h.all.v7.0.symbols.pluscc.gmt")


acc_cancerCells_noACC1 = ScaleData(object = acc_cancerCells_noACC1,features = VariableFeatures(acc_cancerCells_noACC1,assay = "integrated"))
geneIds= genesets[[hallmark_name]]@geneIds %>% intersect(VariableFeatures(acc_cancerCells_noACC1,assay = "integrated")) 
score <- apply(acc_cancerCells_noACC1@assays$integrated@scale.data[geneIds,],2,mean)
acc_cancerCells_noACC1=AddMetaData(acc_cancerCells_noACC1,score,hallmark_name)

acc1_cancer_cells = FindVariableFeatures(object = acc1_cancer_cells,nfeatures = 15000)
acc1_cancer_cells = ScaleData(object = acc1_cancer_cells,features = VariableFeatures(acc1_cancer_cells))
geneIds= genesets[[hallmark_name]]@geneIds %>% intersect(VariableFeatures(acc1_cancer_cells)) 
score <- apply(acc1_cancer_cells@assays$integrated@scale.data[geneIds,],2,mean)
acc1_cancer_cells=AddMetaData(acc1_cancer_cells,score,hallmark_name)

acc_cc_scores = FetchData(object = acc_cancerCells_noACC1,vars = "GO_MITOTIC_CELL_CYCLE")
hmsc_cc_scores = FetchData(object = acc1_cancer_cells,vars = "GO_MITOTIC_CELL_CYCLE")

distributions_plt = ggplot() +
  geom_density(aes(GO_MITOTIC_CELL_CYCLE, fill = "ACC"), alpha = .2, data = acc_cc_scores) +
  geom_density(aes(GO_MITOTIC_CELL_CYCLE, fill = "HMSC"), alpha = .2, data = hmsc_cc_scores) +
  scale_fill_manual(name = "Dataset", values = c(ACC = "red", HMSC = "green"))+ geom_vline(aes(xintercept=0.3),
            color="blue", linetype="dashed", size=1) +ggtitle("'GO_MITOTIC_CELL_CYCLE'  score distribution")

print_tab(plt = distributions_plt,title = "score distribution",subtitle_num = 3)

score distribution

NA

hmsc_cc_cells = (sum(acc1_cancer_cells@meta.data[[hallmark_name]]> 0.3) /ncol(acc1_cancer_cells)) %>% round(digits = 3)*100
acc_cc_cells = (sum(acc_cancerCells_noACC1@meta.data[[hallmark_name]]> 0.3)/ncol(acc_cancerCells_noACC1)) %>% round(digits = 3)*100
df = data.frame(Dataset = c("HMSC","ACC"), cycling_cells_percentage = c(hmsc_cc_cells,acc_cc_cells))
cycling_cells_plt = ggplot(data=df, aes(x=Dataset, y=cycling_cells_percentage)) +
  geom_text(aes(label=cycling_cells_percentage), vjust=0, color="black", size=3.5)+
  geom_bar(stat="identity")+ylab(" % cycling cells")+
  geom_bar(stat="identity", fill="steelblue")+
  theme_minimal() + ggtitle("Cycling cells count")

print_tab(plt = cycling_cells_plt,title = "# cycling cells",subtitle_num = 3)

# cycling cells

NA

pdf(file = "./Figures/CC_distributions.pdf")
distributions_plt
dev.off()

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pdf(file = "./Figures/cycling_cells.pdf")

cycling_cells_plt
dev.off()

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print_tab(plt = 
            FeaturePlot(all_acc_cancer_cells,features = c("MKI67","CDK1","MCM2","CDC20"))
          ,title = "CC genes",subtitle_num = 3)

CC genes

NA

3.4 Cycling cells filtering

#add score to all acc cancer cells
# geneIds= genesets[[hallmark_name]]@geneIds %>% intersect(VariableFeatures(all_acc_cancer_cells,assay = "integrated")) 
# score <- apply(all_acc_cancer_cells@assays$integrated@scale.data[geneIds,],2,mean)

#add score to all acc cancer cells
cc_all = c(acc_cancerCells_noACC1$GO_MITOTIC_CELL_CYCLE, acc1_cancer_cells$GO_MITOTIC_CELL_CYCLE) %>% as.data.frame()
all_acc_cancer_cells=AddMetaData(all_acc_cancer_cells,cc_all,hallmark_name)

#filter:
all_acc_cancer_cells_ccFiltered=all_acc_cancer_cells[,all_acc_cancer_cells@meta.data[[hallmark_name]]< 0.3]


min_threshold = min(all_acc_cancer_cells$GO_MITOTIC_CELL_CYCLE)
max_threshold = max(all_acc_cancer_cells$GO_MITOTIC_CELL_CYCLE)

library(viridis)

Loading required package: viridisLite

print_tab(plt = FeaturePlot(object = all_acc_cancer_cells,features = hallmark_name) + ggtitle("Before cc filtering") &
            scale_color_gradientn(colours = plasma(n = 10, direction = -1), limits = c(min_threshold, max_threshold))
          ,title = "Before CC filtering",subtitle_num = 3)

Before CC filtering

Scale for ‘colour’ is already present. Adding another scale for ‘colour’, which will replace the existing scale.

print_tab(plt = 
            FeaturePlot(object = all_acc_cancer_cells_ccFiltered,features = hallmark_name) + ggtitle("After cc filtering") &
            scale_color_gradientn(colours = plasma(n = 10, direction = -1), limits = c(min_threshold, max_threshold))
          ,title = "After CC filtering" ,subtitle_num = 3)

After CC filtering

Scale for ‘colour’ is already present. Adding another scale for ‘colour’, which will replace the existing scale.

NA

3.5 DEG

all_acc_cancer_cells_ccFiltered = SetIdent(all_acc_cancer_cells_ccFiltered, value ="patient.ident")
acc_deg <-
  FindMarkers(
    all_acc_cancer_cells_ccFiltered,
    ident.1 = "HMSC",
    features = VariableFeatures(all_acc_cancer_cells_ccFiltered),
    densify = T,
    verbose = T,
    slot = "data",
    mean.fxn = function(x) {
      return(log(rowMeans(x) + 1,base = 2)) # change func to calculate logFC in log space data (default to exponent data)
    },
    assay = "RNA"
  )

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print_tab(plt = enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells_ccFiltered),fdr_Cutoff = 0.01,ident.1 =
                                      "HMSC",ident.2 = "ACC",show_by = 1)
          ,title = "Enrichment after filtering",subtitle_num = 3)

###   Enrichment after filtering {.unnumbered }  

NA

library(hypeR)
genesets <- msigdb_download("Homo sapiens",category="H") %>% append( msigdb_download("Homo sapiens",category="C2",subcategory = "CP:KEGG"))
ranked_vec = acc_deg[,"avg_log2FC"]%>% setNames(rownames(acc_deg)) %>% na.omit() # make named vector

hyp_obj <-hypeR_fgsea(signature = ranked_vec,genesets = genesets,up_only = F)

plt = hyp_dots(hyp_obj,merge = F)
plt1 = plt$up+ aes(size=nes)+ggtitle("up in HMSC")
plt2 = plt$dn+ aes(size=abs(nes))+ggtitle("up in ACC")
plt3 = plt1+plt2

plt3

pdf(file = "./Figures/ACC_vs_HMSC_GSEA.pdf",width = 13,height = 6)
plt3
dev.off()

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volcano_plot(de_genes = acc_deg,fdr_cutoff = 0.05,fc_cutoff = 2, ident1 = "HMSC",ident2 = "ACC",top_genes_text = 4)

all_acc_cancer_cells_ccFiltered = SetIdent(all_acc_cancer_cells_ccFiltered, value ="patient.ident")
acc_deg <-
  FindMarkers(
    all_acc_cancer_cells_ccFiltered,
    ident.1 = "HMSC",
    features = VariableFeatures(all_acc_cancer_cells_ccFiltered),
    densify = T,
    verbose = T,
    slot = "data",
    mean.fxn = function(x) {
      return(rowMeans(x) + 1) # change func to calculate logFC in log space data (default to exponent data)
    },
    assay = "RNA"
  )

volcano_plot(de_genes = acc_deg,fdr_cutoff = 0.05,fc_cutoff = 2, ident1 = "HMSC",ident2 = "ACC",top_genes_text = 4)+xlab("avg diff")

pdf("./Figures/volcano_plot_ACC_VS_HMSC.pdf")
volcano_plot(de_genes = acc_deg,fdr_cutoff = 0.05,fc_cutoff = 2, ident1 = "HMSC",ident2 = "ACC",top_genes_text = 4)
dev.off()

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pdf("./Figures/Enrichment_analysis_ACC_VS_HMSC.pdf")

enrichment_analysis(acc_deg,background = VariableFeatures(all_acc_cancer_cells_ccFiltered),fdr_Cutoff = 0.01,ident.1 =
                                      "HMSC",ident.2 = "ACC",show_by = 1)
dev.off()

null device 
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top_hmsc_genes = acc_deg %>% dplyr::filter(avg_log2FC > 0) %>%  slice_min(n = 10,order_by = p_val_adj) %>% rownames()
top_acc_genes = acc_deg %>% dplyr::filter(avg_log2FC < 0) %>%  slice_min(n = 10,order_by = p_val_adj) %>% rownames()
all_top_deg = c(top_hmsc_genes,top_acc_genes)

all_acc_cancer_cells_ccFiltered$cancer_type = all_acc_cancer_cells_ccFiltered$patient.ident %>% gsub(pattern = "ACC.*",replacement = "ACC")
cancer_type = FetchData(object = all_acc_cancer_cells_ccFiltered, vars = "cancer_type")
# col_list = list(circlize::colorRamp2(c(0, 1), c("white", "red"))); names(col_list) = colnames(all_metagenes)[i]
column_ha = HeatmapAnnotation(df = cancer_type)

data = FetchData(object = all_acc_cancer_cells_ccFiltered,vars = all_top_deg,slot = "scale.data") %>% t()

print(ComplexHeatmap::Heatmap(data,show_column_names = F,row_names_gp = grid::gpar(fontsize = 7),cluster_rows = F, ,name = "Z-score expression",cluster_columns = F,top_annotation = column_ha))

NA
NA

3.6 CNV plot

# create expression matrix of acc + normal cells + HMSC seurat integrated
# acc_all_cells_noAcc1 = subset(acc_all_cells, subset = patient.ident != "ACC1")
# acc_expr = acc_all_cells_noAcc1@assays$RNA@data %>% as.data.frame()
# hmsc_expr  = acc.combined@assays$integrated@data %>% as.data.frame()
# acc_expr = acc_expr [ rownames(hmsc_expr),]
# all_expr = cbind(acc_expr,hmsc_expr)
# 
# # create annotation 
# acc_annotation_integrated  = as.data.frame(acc_all_cells@meta.data[,"cell.type",drop = F])
# acc_annotation_integrated = acc_annotation_integrated[colnames(all_expr),,drop = F]
# acc_annotation_integrated = acc_annotation_integrated %>% rownames_to_column("orig.ident") 

# #write expression and annotation
# write.table(acc_annotation_integrated, "./Data/inferCNV/acc_annotation_integrated.txt", append = FALSE, 
#             sep = "\t", dec = ".",row.names = FALSE, col.names = F)
# 
# 
# write.table(all_expr, "./Data/inferCNV/all.4icnv_integrated.txt", append = FALSE, 
#             sep = "\t", dec = ".",row.names = T, col.names = T)

trace(infercnv::run,edit = T) # to skip normalization, change to skip_past = 4 (https://github.com/broadinstitute/infercnv/issues/346)

Tracing function "run" in package "infercnv"

[1] "run"

infercnv_obj = CreateInfercnvObject(raw_counts_matrix="./Data/inferCNV/all.4icnv_integrated.txt", 
                                    annotations_file="./Data/inferCNV/acc_annotation_integrated.txt",
                                    delim="\t",gene_order_file="./Data/inferCNV/gencode_v19_gene_pos.txt"
                                    ,ref_group_names=c("CAF", "Endothelial", "WBC")) #groups of normal cells

infercnv_obj_default = infercnv::run(infercnv_obj, cutoff=1, out_dir='./Data/inferCNV/infercnv_intergrated_output',
                                     cluster_by_groups=T, plot_steps=FALSE,
                                     denoise=TRUE, HMM=FALSE, no_prelim_plot=TRUE,
                                     png_res=300)
untrace(infercnv::run)

trace(infercnv:::get_group_color_palette ,edit = T) # change "Set3" to "Set1" for more distinguishable colors
plot_cnv(infercnv_obj_default, output_format = "png",  write_expr_matrix = FALSE,out_dir = "./Data/inferCNV/infercnv_intergrated_output",png_res    =800,obs_title = "Malignant cells",ref_title = "Normal cells",contig_cex = 2, title = "Copy number variation")
untrace(infercnv:::get_group_color_palette)

print_tab(plt = knitr::include_graphics("./Data/inferCNV/infercnv_intergrated_output/infercnv.png")
          ,title = "CNV plot",subtitle_num = 3)

CNV plot

NA

library(limma)
smoothed=apply(infercnv_obj_default@expr.data,2,tricubeMovingAverage, span=0.01)
cnsig=sqrt(apply((smoothed-1)^2,2,mean))

acc_all_cells <- AddMetaData(object = acc_all_cells, metadata = cnsig, col.name = "copynumber")
acc_all_cells = SetIdent(object = acc_all_cells,value = "cell.type")

print_tab(plt = FeaturePlot(acc_all_cells, "copynumber",pt.size = 1,label = T,repel = T)+
            scale_colour_gradientn(colours=c("white","lightblue","orange","red","darkred"))
          ,title = "CNV UMAP",subtitle_num = 3)

CNV UMAP

Scale for ‘colour’ is already present. Adding another scale for ‘colour’, which will replace the existing scale.

NA

4 HMSC analysis

4.1 UMAP

acc1_cancer_cells = FindClusters(object = acc1_cancer_cells,verbose = F,resolution = 0.5)
DimPlot(object = acc1_cancer_cells,pt.size = 2)

4.2 Scores

FeaturePlot(object = acc1_cancer_cells,features = c("MYB","JAG1"),pt.size = 2)+
DimPlot(object = acc1_cancer_cells,group.by  = c("hpv33_positive"),pt.size = 2)

4.3 DEG

SetIdent(object = acc1_cancer_cells,value = "seurat_clusters")

An object of class Seurat 80474 features across 134 samples within 2 assays Active assay: integrated (40237 features, 15000 variable features) 1 other assay present: RNA 2 dimensional reductions calculated: pca, umap

deg = FindAllMarkers(object = acc1_cancer_cells,features = VariableFeatures(acc1_cancer_cells),densify = T,verbose = F)
for (cluster in unique(deg$cluster)) {
  print_tab(plt = deg[deg$cluster == cluster,],title = "DEG" ,subtitle_num = toc_tabs_level)
}

DEG

DEG

NA

for (cluster in unique(deg$cluster)) {
  deg_of_cluster = deg[deg$cluster == cluster,] 
  #  print(deg_of_cluster %in% original_myo_genes %>% which())
  # print(deg_of_cluster %in% original_lum_genes %>% which())
  print_tab(plt = 
              enrichment_analysis(deg_of_cluster,background =VariableFeatures(acc1_cancer_cells),fdr_Cutoff = 0.01,ident.1 =
                                   paste("Cluster",cluster),ident.2 =  paste("Cluster",cluster),show_by = 1)
            ,title = cluster,subtitle_num = toc_tabs_level)
}

0

1

NA

4.4 NMF

reticulate::repl_python()
from cnmf import cNMF
import pickle
nfeatures = "2K"
f = open('./Data/cNMF/HMSC_cNMF_harmony_2Kvargenes/cnmf_obj.pckl', 'rb')
cnmf_obj = pickle.load(f)
f.close()

quit
knitr::include_graphics("./Data/cNMF/HMSC_cNMF_harmony_2Kvargenes/HMSC_cNMF_harmony_2Kvargenes.k_selection.png")

reticulate::repl_python()
selected_k = 3
density_threshold = 0.1
cnmf_obj.consensus(k=selected_k, density_threshold=density_threshold,show_clustering=True)
usage_norm, gep_scores, gep_tpm, topgenes = cnmf_obj.load_results(K=selected_k, density_threshold=density_threshold)

quit
gep_scores = py$gep_scores
gep_tpm = py$gep_tpm

all_metagenes= py$usage_norm

4.5 Harmony results

# Make metagene names
for (i in 1:ncol(all_metagenes)) {
  colnames(all_metagenes)[i] = "metagene." %>% paste0(i)
}

#add each metagene to metadata
for (i in 1:ncol(all_metagenes)) {
  metage_metadata = all_metagenes %>% select(i)
  acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = metage_metadata)
}

Note: Using an external vector in selections is ambiguous. ℹ Use all_of(i) instead of i to silence this message. ℹ See https://tidyselect.r-lib.org/reference/faq-external-vector.html. This message is displayed once per session.

print_tab(plt = 
            FeaturePlot(object = acc1_cancer_cells,features = colnames(all_metagenes),combine = T),
          title = "metagenes expression",subtitle_num = toc_tabs_level)

metagenes expression

NA

4.6 Enrichment analysis by top 200 genes of each program

canonical_pathways = msigdbr(species = "Homo sapiens", category = "C2") %>% dplyr::filter(gs_subcat != "CGP") %>%  dplyr::distinct(gs_name, gene_symbol)

plt_list = list()
for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),200) #take top top_genes_num
  res = genes_vec_enrichment(genes = top,background = rownames(gep_scores),homer = T,title = 
                    i,silent = T,return_all = T,custom_pathways = canonical_pathways)
   
  plt_list[[i]] = res$plt
}
gridExtra::grid.arrange(grobs = plt_list)

for (i in 1:ncol(gep_scores)) {
  ranked_vec = gep_scores %>% pull(i) %>%  setNames(rownames(gep_scores))
  hyp_obj <-hypeR_fgsea(signature = ranked_vec,genesets = genesets,up_only = T)

  plt = hyp_dots(hyp_obj,merge = F)+ aes(size=abs(nes))
  print(plt)
}

library(ComplexHeatmap)
acc1_cancer_cells = SetIdent(object = acc1_cancer_cells,value = "seurat_clusters")
for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(desc(gep_scores[i])) #sort by score a
  top = head(rownames(top_genes),50) #take top top_genes_num
  data = FetchData(object = acc1_cancer_cells,vars = top)%>% scale() %>% t() 
  metagene_data = FetchData(object = acc1_cancer_cells,vars = colnames(all_metagenes)[i])
  col_list = list(circlize::colorRamp2(c(0, 1), c("white", "red"))); names(col_list) = colnames(all_metagenes)[i]
  column_ha = HeatmapAnnotation(df = metagene_data,col = col_list)
  print(ComplexHeatmap::Heatmap(data,show_column_names = F,row_names_gp = grid::gpar(fontsize = 7),cluster_rows = F, top_annotation = 
                                  column_ha,name = "Z-score expression"))
    
  pdf(paste0("./Figures/NMF_top_genes_program",i,".pdf"))
  print(ComplexHeatmap::Heatmap(data,show_column_names = F,row_names_gp = grid::gpar(fontsize = 7),cluster_rows = F, top_annotation = 
                                  column_ha,name = "Z-score expression"))
  dev.off()
}

4.7 Lum Myo score

original_myo_genes = c( "TP63", "TP73", "CAV1", "CDH3", "KRT5", "KRT14", "ACTA2", "TAGLN", "MYLK", "DKK3")
original_lum_genes = c("KIT", "EHF", "ELF5", "KRT7", "CLDN3", "CLDN4", "CD24", "LGALS3", "LCN2", "SLPI" )

FeaturePlot(acc1_cancer_cells,features = original_myo_genes)

FeaturePlot(acc1_cancer_cells,features = original_lum_genes)

acc_cancerCells_noACC1 = SetIdent(acc_cancerCells_noACC1,value = "patient.ident")
calculate_score(dataset = acc_cancerCells_noACC1,myo_genes = original_myo_genes,lum_genes = original_lum_genes)

correlation of lum score and myo score: -0.51
correlation of lum score and original lum score: 1
correlation of myo score and original myo score: 1

4.8 Original score of ACC1

calculate_score(dataset = acc1_cancer_cells,myo_genes = original_myo_genes,lum_genes = original_lum_genes,lum_threshold = 0,myo_threshold = 0)

correlation of lum score and myo score: -0.08
correlation of lum score and original lum score: 1
correlation of myo score and original myo score: 1

4.8.1 Myo genes

myo_protein_markers = c("CNN1", "TP63","ACTA2")
top_myo  = top_correlated(dataset = acc1_cancer_cells, genes = myo_protein_markers,threshold = 0.35)
print("Number of genes = " %>% paste(length(top_myo)))
message("Names of genes:")
top_myo %>% head(30)
message("Genes that also apeared in the original score:")
base::intersect(top_myo,original_myo_genes) 

myo_enrich_res = genes_vec_enrichment(genes = top_myo,background = rownames(acc1_cancer_cells),homer = T,title = "myo top enrichment",custom_pathways = luminal_gs)
myo_enrich_res

4.8.2 Lum genes

lum_protein_markers = c("KIT")
top_lum  = top_correlated(dataset = acc1_cancer_cells, genes = lum_protein_markers,threshold = 0.30,n_vargenes = 5000)
print("Number of genes = " %>% paste(length(top_lum)))
message("Names of genes:")
top_lum %>% head(30)
message("Genes that also apeared in the original score:")
base::intersect(top_lum,original_lum_genes) 

lum_enrich_res = genes_vec_enrichment(genes = top_lum,background = rownames(acc1_cancer_cells),homer = T,title = "lum top enrichment",custom_pathways = luminal_gs)
lum_enrich_res

rownames(myo_enrich_res) = myo_enrich_res$pathway_name
myo_enriched_genes = myo_enrich_res[1,"geneID"] %>% strsplit(split = "/") %>% .[[1]] %>% c(.,myo_protein_markers) #add original markers
myoscore=FetchData(object =acc1_cancer_cells,vars =  myo_enriched_genes,slot = "data") %>% rowMeans()
acc1_cancer_cells=AddMetaData(acc1_cancer_cells,myoscore,"myo_score")


myoscore=FetchData(object =acc1_cancer_cells,vars =  top_myo,slot = "data") %>% rowMeans()
acc1_cancer_cells=AddMetaData(acc1_cancer_cells,myoscore,"myo_score")

4.9 HPV

4.9.1 HPV UMAP

HPV33_P3 = fread("./Data/HPV33_P3.txt",col.names = c("plate","reads")) %>% as.data.frame()
HPV33_P3.df = HPV33_P3 %>% mutate(
  plate = gsub(x =HPV33_P3$plate, replacement = "",pattern = "_.*$") 
  %>% gsub(pattern = "-P",replacement = ".P") 
  %>% gsub(pattern = "-",replacement = "_",)
  )
HPV33_P3.df = HPV33_P3.df %>% dplyr::filter(HPV33_P3.df$plate %in% colnames(acc1_cancer_cells))
rownames(HPV33_P3.df)  <- HPV33_P3.df$plate
HPV33_P3.df$plate = NULL


HPV33_P2 = fread("./Data/HPV33_P2.txt",col.names = c("plate","reads")) %>% as.data.frame()
HPV33_P2.df = HPV33_P2 %>% mutate(
  plate = gsub(x =HPV33_P2$plate, replacement = "",pattern = "_.*$") 
  %>% gsub(pattern = "plate2-",replacement = "plate2_",)
  %>% gsub(pattern = "-",replacement = "\\.",)
  )
HPV33_P2.df = HPV33_P2.df %>% dplyr::filter(HPV33_P2.df$plate %in% colnames(acc1_cancer_cells))
rownames(HPV33_P2.df)  <- HPV33_P2.df$plate
HPV33_P2.df$plate = NULL

HPV33 = rbind(HPV33_P3.df,HPV33_P2.df)
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = HPV33,col.name = "HPV33.reads")
FeaturePlot(acc1_cancer_cells,features = "HPV33.reads",max.cutoff = 10)

data = FetchData(object = acc1_cancer_cells,vars = "HPV33.reads")

data = data %>% mutate("0 reads" = if_else(condition = HPV33.reads == 0,true = 1,false = 0))
data = data %>% mutate("1 reads" = if_else(condition = HPV33.reads == 1,true = 1,false = 0))
data = data %>% mutate("2 reads" = if_else(condition = HPV33.reads == 2,true = 1,false = 0))
data = data %>% mutate("3-23 reads" = if_else(condition = HPV33.reads >=3 &HPV33.reads  <24,true = 1,false = 0))
data = data %>% mutate("24+ reads" = if_else(condition = HPV33.reads >=24,true = 1,false = 0))
data = colSums(data[,2:ncol(data)]) %>% as.data.frame()
names(data)[1] = "count"
data = rownames_to_column(data,var = "bin")
data
ggplot(data=data, aes(x=factor(bin,levels = c("0 reads","1 reads","2 reads","3-23 reads","24+ reads")), y=count)) +
  geom_bar(stat="identity", fill="steelblue") + xlab("HPV Reads")+ theme_minimal()+
  geom_text(aes(label=count), vjust=-0.5, color="black", size=3.5)

hpv33_positive = HPV33 %>% dplyr::mutate(hpv33_positive = case_when(reads >= 10 ~ "positive",
                                                                    reads < 10 ~ "negative")
)



hpv33_positive$reads = NULL
acc1_cancer_cells = AddMetaData(object = acc1_cancer_cells,metadata = hpv33_positive)

DimPlot(object = acc1_cancer_cells,group.by  = c("hpv33_positive"),pt.size = 2)

library(biomaRt)
ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")

4.9.2 DEG integrated wilcox

acc_deg

acc_deg[c("MYB","JAG1"),]

NA

4.9.3 DEG LR latent vars plate

acc1_cancer_cells = FindVariableFeatures(acc1_cancer_cells,nfeatures = 15000)
acc1_cancer_cells = SetIdent(acc1_cancer_cells, value ="hpv33_positive")
features = VariableFeatures(acc1_cancer_cells) 
acc_deg <-
  FindMarkers(
    acc1_cancer_cells,
    ident.1 = "positive",
    ident.2 = "negative",
    features = features,
    densify = T,
    assay = "RNA",
    test.use = "LR",
    latent.vars = "plate",
    logfc.threshold = 0.25,
    min.pct = 0.15,
    mean.fxn = function(x) {
      return(log(rowMeans(x) + 1, base = 2)) # change func to calculate logFC in log space data (default to exponent data)
    }
  )
acc_deg$fdr<-p.adjust(p = as.vector(acc_deg$p_val) ,method = "fdr" )

ranked_vec = acc_deg[,"avg_log2FC"]%>% setNames(rownames(acc_deg)) %>% na.omit() # make named vector

hyp_obj <-hypeR_fgsea(signature = ranked_vec,genesets = genesets,up_only = F)
hyp_dots(hyp_obj,merge = F)

acc_deg

acc_deg[c("MYB","JAG1"),]

NA

volcano plot log2(mean logTPM HPV+) - log2(mean logTPM HPV-)

volcano_plot(de_genes = acc_deg,fc_cutoff = 1.3, fdr_cutoff = 0.1,show_gene_names = c("MYB","JAG1"),ident1 = "HPV33 positive",ident2 = "HPV33 negative",top_genes_text = 5)

volcano plot log2(mean logTPM HPV+) - log2(mean logTPM HPV-)

# library("biomaRt")
# mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
# all_coding_genes <- getBM(attributes = c( "hgnc_symbol"), filters = c("biotype"), values = list(biotype="protein_coding"), mart = mart)

acc1_cancer_cells = FindVariableFeatures(acc1_cancer_cells,nfeatures = 15000)
acc1_cancer_cells = SetIdent(acc1_cancer_cells, value ="hpv33_positive")
features = VariableFeatures(acc1_cancer_cells) 
acc_deg <-
  FindMarkers(
    acc1_cancer_cells,
    ident.1 = "positive",
    ident.2 = "negative",
    features = features,
    densify = T,
    assay = "RNA",
    test.use = "LR",
    latent.vars = "plate",
    logfc.threshold = 0.25,
    min.pct = 0.15,
    mean.fxn = function(x) {
      return(rowMeans(x) + 1) # change func to calculate logFC in log space data (default to exponent data)
    }
  )
acc_deg$fdr<-p.adjust(p = as.vector(acc_deg$p_val) ,method = "fdr" )

acc_deg
acc_deg[c("MYB","JAG1"),]

volcano plot (mean logTPM HPV+) - (mean logTPM HPV-)

volcano_plot(de_genes = acc_deg,fc_cutoff = 1.3, fdr_cutoff = 0.1,show_gene_names = c("MYB","JAG1"),ident1 = "HPV33 positive",ident2 = "HPV33 negative",top_genes_text = 5)+xlab("avg diff")

4.9.4 HPV vs genes

notch_genes = c("JAG1","JAG2","NOTCH3","NOTCH2","NOTCH1","DLL1","MYB","HES4","HEY1","HEY2","NRARP")
for (gene  in notch_genes) {
  myb_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c("hpv33_positive",gene)) 
  myb_vs_hpv$hpv33_positive = paste("HPV33",myb_vs_hpv$hpv33_positiv)

  p = ggboxplot(myb_vs_hpv, x = "hpv33_positive", y = gene,
            palette = "jco",
            add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("HPV33 positive","HPV33 negative")))+ stat_summary(fun.data = function(x) data.frame(y=max(x)*1.2, label = paste("Mean=",round(mean(x),digits = 2))), geom="text") +ylab("log2(gene)")+ggtitle(gene)
  print_tab(p,title = gene)
}

##   JAG1 {.unnumbered }  

 

##   JAG2 {.unnumbered }  

 

##   NOTCH3 {.unnumbered }  

 

##   NOTCH2 {.unnumbered }  

 

##   NOTCH1 {.unnumbered }  

 

##   DLL1 {.unnumbered }  

 

##   MYB {.unnumbered }  

 

##   HES4 {.unnumbered }  

 

##   HEY1 {.unnumbered }  

 

##   HEY2 {.unnumbered }  

 

##   NRARP {.unnumbered }  

NA

notch_targets = c("NOTCH3","HES4","HEY1","HEY2","NRARP") 
notch_ligand = c("JAG1","JAG2","DLL1")
notch_genes = list(notch_targets = notch_targets,notch_ligand = notch_ligand)
for (i  in 1:length(notch_genes)) {
  genes = notch_genes[[i]]
  name = names( notch_genes)[i]
  myb_vs_hpv = FetchData(object = acc1_cancer_cells,vars = c(genes)) %>% rowMeans()
  myb_vs_hpv = myb_vs_hpv %>% cbind(FetchData(object = acc1_cancer_cells,vars = c("hpv33_positive")))
  colnames(myb_vs_hpv)[1] = "gene_set"
  myb_vs_hpv$hpv33_positive = paste("HPV33",myb_vs_hpv$hpv33_positiv)

  p = ggboxplot(myb_vs_hpv, x = "hpv33_positive", y = "gene_set",
            palette = "jco",
            add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("HPV33 positive","HPV33 negative")))+ stat_summary(fun.data = function(x) data.frame(y=max(x)*1.2, label = paste("Mean=",round(mean(x),digits = 2))), geom="text") +ylab("log2(gene)")+ggtitle(name)
 print(p)
}

  cor_data = FetchData(object = acc1_cancer_cells,vars = c("MYB","myo_score"))
ggplot(cor_data, aes(x=MYB, y=myo_score)) + 
    stat_cor(method = "pearson")+
    geom_smooth(method=lm)  +
  geom_point()


  cor_data = FetchData(object = acc1_cancer_cells,vars = c("JAG1","myo_score"))
ggplot(cor_data, aes(x=JAG1, y=myo_score)) + 
    stat_cor(method = "pearson")+
    geom_smooth(method=lm)  +
  geom_point()

  cor_data = FetchData(object = acc1_cancer_cells,vars = c("JAG2","myo_score"))
ggplot(cor_data, aes(x=JAG2, y=myo_score)) + 
    stat_cor(method = "pearson")+
    geom_smooth(method=lm)  +
  geom_point()

  cor_data = FetchData(object = acc1_cancer_cells,vars = c("DLL1","myo_score"))
ggplot(cor_data, aes(x=DLL1, y=myo_score)) + 
    stat_cor(method = "pearson")+
    geom_smooth(method=lm)  +
  geom_point()


  cor_data = FetchData(object = acc1_cancer_cells,vars = notch_targets) %>% rowMeans()
  cor_data = cor_data %>% cbind(FetchData(object = acc1_cancer_cells,vars = c("myo_score")))
  colnames(cor_data)[1] = "notch_targets"

  ggplot(cor_data, aes(x=notch_targets, y=myo_score)) + 
    stat_cor(method = "pearson")+
    geom_smooth(method=lm)  +
  geom_point()
  
  
    cor_data = FetchData(object = acc1_cancer_cells,vars = notch_ligand) %>% rowMeans()
  cor_data = cor_data %>% cbind(FetchData(object = acc1_cancer_cells,vars = c("myo_score")))
  colnames(cor_data)[1] = "notch_ligand"

  ggplot(cor_data, aes(x=notch_ligand, y=myo_score)) + 
    stat_cor(method = "pearson")+
    geom_smooth(method=lm)  +
  geom_point()
  

notch_score = FetchData(object = all_acc_cancer_cells,vars = notch_targets) %>% rowMeans()
all_acc_cancer_cells  = AddMetaData(object = all_acc_cancer_cells,metadata = notch_score,col.name = "notch_score")
FeaturePlot(object = all_acc_cancer_cells,features = "notch_score" )

myo_markers = c("TP63", "TP73", "KRT14", "CDH3")
score = FetchData(object = acc1_cancer_cells,vars = myo_markers) %>% rowMeans()
acc1_cancer_cells  = AddMetaData(object = acc1_cancer_cells,metadata = score,col.name = "myo_markers_score")
FeaturePlot(object = acc1_cancer_cells,features = "myo_markers_score",pt.size = 2 )


markers = c("CLDN3", "ANXA8", "EHF", "KIT")
score = FetchData(object = acc1_cancer_cells,vars = markers) %>% rowMeans()
acc1_cancer_cells  = AddMetaData(object = acc1_cancer_cells,metadata = score,col.name = "lum_markers_score")
FeaturePlot(object = acc1_cancer_cells,features = "lum_markers_score" ,pt.size = 2 )