cnmf_programs_in_ACC

all_acc_cancer_cells = readRDS("./Data/acc_cancer_cells_V3.RDS")

  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")
  notch_targets_genes = c("NRARP", "NOTCH3", "HES4", "HEY1", "HEY2")
  notch_ligands = c("DLL1", "JAG1", "JAG2", "HEY1")

  orig_myoscore=apply(all_acc_cancer_cells@assays[["RNA"]][original_myo_genes,],2,mean)
  orig_lescore=apply(all_acc_cancer_cells@assays[["RNA"]][original_lum_genes,],2,mean)
  notch_score=apply(all_acc_cancer_cells@assays[["RNA"]][notch_targets_genes,],2,mean)
  notch_ligands_score=apply(all_acc_cancer_cells@assays[["RNA"]][notch_ligands,],2,mean)

  all_acc_cancer_cells = AddMetaData(object = all_acc_cancer_cells,metadata = orig_lescore-orig_myoscore,col.name = "lum_over_myo")
    all_acc_cancer_cells = AddMetaData(object = all_acc_cancer_cells,metadata = orig_myoscore,col.name = "myo_score")
    all_acc_cancer_cells = AddMetaData(object = all_acc_cancer_cells,metadata = orig_lescore,col.name = "lum_score")
    all_acc_cancer_cells = AddMetaData(object = all_acc_cancer_cells,metadata = notch_score,col.name = "notch_score")
    
  all_acc_cancer_cells = AddMetaData(object = all_acc_cancer_cells,metadata = notch_ligands_score,col.name = "notch_ligands_score_score")

Load object

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

selected_k = 4
density_threshold = 0.1
cnmf_obj.consensus(k=selected_k, density_threshold=density_threshold,show_clustering=True)

/sci/labs/yotamd/lab_share/avishai.wizel/python_envs/miniconda/envs/cnmf_env_6/lib/python3.7/site-packages/scanpy/preprocessing/_simple.py:843: UserWarning: Received a view of an AnnData. Making a copy.
  view_to_actual(adata)

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

no_neg <- function(x) {
  x = x + abs(min(x))
  x
}

sum_2_one <- function(x) {
  x =x/sum(x)
  x
}

gep_scores_norm = gep_scores
# gep_scores_norm = scale(gep_scores_norm) %>% as.data.frame()

#or:
gep_scores_norm = apply(gep_scores, MARGIN = 2, FUN = no_neg)%>% as.data.frame()
# 
gep_scores_norm = sum2one(gep_scores_norm)
all_metagenes = expression_mult(gep_scores = gep_scores_norm,dataset = all_acc_cancer_cells,top_genes = T,z_score = F)
# normalize metagenes:
all_metagenes = apply(all_metagenes, MARGIN = 2, FUN = no_neg)  %>% as.data.frame()
all_metagenes = apply(all_metagenes, 1, sum_2_one) %>% t() %>% as.data.frame()

Programs enrichments

#Hallmarks:
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)
   
  plt_list[[i]] = res$plt
}
p = ggarrange(plotlist  = plt_list)
print_tab(plt = p,title = "HALLMARK enrichment")

HALLMARK enrichment

#Canonical:
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
}
p = ggarrange(plotlist  = plt_list)
print_tab(plt = p,title = "canonical pathway enrichment")

canonical pathway enrichment

NA

luminal and myo genes in score

# lum genes in metagenes
message("lum genes in metagenes:")

lum genes in metagenes:

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
  cat(paste0("metagene ",i,": "))
  print(original_lum_genes[original_lum_genes %in% top])

}

metagene 1: [1] "KRT7" "SLPI"
metagene 2: [1] "CLDN4"
metagene 3: character(0)
metagene 4: [1] "KRT7"   "LGALS3" "LCN2"   "SLPI"  

cat("\n")

# myo genes in metagenes
message("myo genes in metagenes:")

myo genes in metagenes:

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
  cat(paste0("metagene ",i,": "))
  print(original_myo_genes[original_myo_genes %in% top])

}

metagene 1: [1] "KRT14" "ACTA2" "DKK3" 
metagene 2: character(0)
metagene 3: character(0)
metagene 4: character(0)

cat("\n")

notch_genes = c("JAG1","JAG2","NOTCH3","NOTCH2","NOTCH1","DLL1","MYB","HES4","HEY1","HEY2","NRARP")
# notch genes in metagenes
message("notch genes in metagenes:")

notch genes in metagenes:

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
  cat(paste0("metagene ",i,": "))
  print(notch_genes[notch_genes %in% top])

}

metagene 1: character(0)
metagene 2: character(0)
metagene 3: character(0)
metagene 4: character(0)

cat("\n")

Metagenes on ACC

# 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)
  all_acc_cancer_cells = AddMetaData(object = all_acc_cancer_cells,metadata = metage_metadata)
}

FeaturePlot(object = all_acc_cancer_cells,features = colnames(all_metagenes),max.cutoff = 100)

all_acc_cancer_cells = program_assignment(dataset = all_acc_cancer_cells,larger_by = 1.25,program_names = colnames(all_metagenes))

UMAPS

colors =  rainbow(all_acc_cancer_cells$program.assignment %>% unique() %>% length()-1)
colors = c(colors,"grey")
print_tab(plt = DimPlot(object = all_acc_cancer_cells,group.by = "program.assignment",cols =colors),title = "program.assignment")

program.assignment

print_tab(plt = DimPlot(object = all_acc_cancer_cells,group.by = "patient.ident"),title = "patient.ident")

patient.ident

print_tab(plt = FeaturePlot(object = all_acc_cancer_cells,features = "lum_score"),title = "lum_score")

lum_score

print_tab(plt = FeaturePlot(object = all_acc_cancer_cells,features = "myo_score"),title = "myo_score")

myo_score

NA

metagenes by lum_over_myo

acc_cancerCells_noACC1 = subset(all_acc_cancer_cells,subset = patient.ident!= "ACC1")

lumScore_vs_program = FetchData(object = acc_cancerCells_noACC1,vars = c("lum_over_myo","program.assignment"))
lumScore_vs_program$program.assignment <- factor(lumScore_vs_program$program.assignment, levels = c("metagene.1","metagene.2","metagene.4"))

lumScore_vs_program = lumScore_vs_program %>% dplyr::filter(program.assignment %in% c("metagene.1","metagene.2","metagene.4"))
ggboxplot(lumScore_vs_program, x = "program.assignment", y = "lum_over_myo",
          palette = "jco",
          add = "jitter")+ stat_compare_means(method = "wilcox.test",comparisons = list(c("metagene.1","metagene.2"),c("metagene.2","metagene.4")))

Metagenes correlation with myo score

for (metagene in c("metagene.1","metagene.2","metagene.4")) {
   lum_score_vs_programScore = FetchData(object = acc_cancerCells_noACC1,vars = c(metagene,"myo_score"))
  print_tab(plt = 
    ggscatter(lum_score_vs_programScore, x = metagene, y = "myo_score", 
            add = "reg.line", conf.int = TRUE, 
            cor.coef = TRUE, cor.method = "pearson")
  ,title = metagene)
}

metagene.1

geom_smooth() using formula ‘y ~ x’

metagene.2

geom_smooth() using formula ‘y ~ x’

metagene.4

geom_smooth() using formula ‘y ~ x’

NA

NA

Metagenes correlation with lum score

for (metagene in c("metagene.1","metagene.2","metagene.4")) {
   lum_score_vs_programScore = FetchData(object = acc_cancerCells_noACC1,vars = c(metagene,"lum_score"))
  print_tab(plt = 
    ggscatter(lum_score_vs_programScore, x = metagene, y = "lum_score", 
            add = "reg.line", conf.int = TRUE, 
            cor.coef = TRUE, cor.method = "pearson")
  ,title = metagene)
}

metagene.1

geom_smooth() using formula ‘y ~ x’

metagene.2

geom_smooth() using formula ‘y ~ x’

metagene.4

geom_smooth() using formula ‘y ~ x’

NA

NA

Notch score

for (metagene in c("metagene.1","metagene.2","metagene.4")) {
   lum_score_vs_programScore = FetchData(object = acc_cancerCells_noACC1,vars = c(metagene,"notch_score"))
  print_tab(plt = 
    ggscatter(lum_score_vs_programScore, x = metagene, y = "notch_score", 
            add = "reg.line", conf.int = TRUE, 
            cor.coef = TRUE, cor.method = "pearson")
  ,title = metagene)
}

metagene.1

geom_smooth() using formula ‘y ~ x’

metagene.2

geom_smooth() using formula ‘y ~ x’

metagene.4

geom_smooth() using formula ‘y ~ x’

NA

NA

Notch ligands score

for (metagene in c("metagene.1","metagene.2","metagene.4")) {
   lum_score_vs_programScore = FetchData(object = acc_cancerCells_noACC1,vars = c(metagene,"notch_ligands_score_score"))
  print_tab(plt = 
    ggscatter(lum_score_vs_programScore, x = metagene, y = "notch_ligands_score_score", 
            add = "reg.line", conf.int = TRUE, 
            cor.coef = TRUE, cor.method = "pearson")
  ,title = metagene)
}

metagene.1

geom_smooth() using formula ‘y ~ x’

metagene.2

geom_smooth() using formula ‘y ~ x’

metagene.4

geom_smooth() using formula ‘y ~ x’

NA

NA

metagene 1 with myo genes

for (gene in original_myo_genes) {
  
  lum_score_vs_programScore = FetchData(object = acc_cancerCells_noACC1,vars = c("metagene.1",gene))
p = ggscatter(lum_score_vs_programScore, x = "metagene.1", y = gene, 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson")
print_tab(plt = p,title = gene)
}

TP63

geom_smooth() using formula ‘y ~ x’

TP73

geom_smooth() using formula ‘y ~ x’

CAV1

geom_smooth() using formula ‘y ~ x’

CDH3

geom_smooth() using formula ‘y ~ x’

KRT5

geom_smooth() using formula ‘y ~ x’

KRT14

geom_smooth() using formula ‘y ~ x’

ACTA2

geom_smooth() using formula ‘y ~ x’

TAGLN

geom_smooth() using formula ‘y ~ x’

MYLK

geom_smooth() using formula ‘y ~ x’

DKK3

geom_smooth() using formula ‘y ~ x’

NA

metagene 2 with myo genes

for (gene in original_myo_genes) {
  
  lum_score_vs_programScore = FetchData(object = acc_cancerCells_noACC1,vars = c("metagene.2",gene))
p = ggscatter(lum_score_vs_programScore, x = "metagene.2", y = gene, 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson")
print_tab(plt = p,title = gene)
}

TP63

geom_smooth() using formula ‘y ~ x’

TP73

geom_smooth() using formula ‘y ~ x’

CAV1

geom_smooth() using formula ‘y ~ x’

CDH3

geom_smooth() using formula ‘y ~ x’

KRT5

geom_smooth() using formula ‘y ~ x’

KRT14

geom_smooth() using formula ‘y ~ x’

ACTA2

geom_smooth() using formula ‘y ~ x’

TAGLN

geom_smooth() using formula ‘y ~ x’

MYLK

geom_smooth() using formula ‘y ~ x’

DKK3

geom_smooth() using formula ‘y ~ x’

NA

metagene 2 with acc genes

notch_genes = c("JAG1","JAG2","NOTCH3","NOTCH2","NOTCH1","DLL1","MYB","HES4","HEY1","HEY2","NRARP")

acc_cancerCells_noACC1 = subset(all_acc_cancer_cells,subset = patient.ident!= "ACC1")
for (gene in notch_genes) {
  
  lum_score_vs_programScore = FetchData(object = acc_cancerCells_noACC1,vars = c("metagene.2",gene))
p = ggscatter(lum_score_vs_programScore, x = "metagene.2", y = gene, 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson")
print_tab(plt = p,title = gene)
}

JAG1

geom_smooth() using formula ‘y ~ x’

JAG2

geom_smooth() using formula ‘y ~ x’

NOTCH3

geom_smooth() using formula ‘y ~ x’

NOTCH2

geom_smooth() using formula ‘y ~ x’

NOTCH1

geom_smooth() using formula ‘y ~ x’

DLL1

geom_smooth() using formula ‘y ~ x’

MYB

geom_smooth() using formula ‘y ~ x’

HES4

geom_smooth() using formula ‘y ~ x’

HEY1

geom_smooth() using formula ‘y ~ x’

HEY2

geom_smooth() using formula ‘y ~ x’

NRARP

geom_smooth() using formula ‘y ~ x’

NA