R Notebook

f = open('../Data/cnmf/patients_5Kvargenes_cnmf_obj.pckl', 'rb')
cnmf_obj = pickle.load(f)
f.close()

selected_k = 5
cnmf_obj.consensus(k=selected_k, density_threshold=0.1)
usage_norm, gep_scores, gep_tpm, topgenes = cnmf_obj.load_results(K=selected_k, density_threshold=0.1)

#Add to seurat metadata:

library(reticulate)
all_metagenes= py$usage_norm
#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)
  lung = AddMetaData(object = lung,metadata = metage_metadata)
}

FeaturePlot(object = lung,features = colnames(all_metagenes))

Enrichment analysis by top 200 genes of each program

gep_scores = py$gep_scores
# saveRDS(object = gep_scores,file = "../Data/models_harmony_gep_scores.rds")
#or:
# gep_scores = readRDS(file = "../Data/models_harmony_gep_scores.rds")

for (i in 1:ncol(gep_scores)) {
  top_genes = gep_scores  %>%  arrange(gep_scores[i]) #sort by score a
   top = head(rownames(top_genes),200) #take top top_genes_num
   genes_vec_enrichment(genes = top,background = rownames(gep_scores),gene_sets = "homer_hallmark",title = i,convert_background = T,add_msigdb_to_set = T,add_bg = F)
}

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Enrichment analysis by selecting genes using “max” method

gep_scores = py$gep_scores

library(NMF)
top_features = extractFeatures(object = gep_scores %>% data.matrix(),method ="max")
for (i in 1:length(top_features)) {
  top_features[[i]]= rownames(gep_scores)[top_features[[i]]]
}

for (i in 1:ncol(gep_scores)) {
top = top_features[i] %>% unlist()
try({ res = genes_vec_enrichment(genes = top,background = rownames(gep_scores),gene_sets = "homer_hallmark",title = i,convert_background = T,add_msigdb_to_set = T,add_bg = F)}, silent=TRUE)
print(res)
}