get_ipython().run_cell_magic( 'R', '-i metadata_filename -i project_id -i base_dir -i local_dir -i num_runs', '\nsource(paste0(base_dir, \'/generic_expression_patterns_modules/DE_analysis.R\'))\n\n# Files created: "<local_dir>/DE_stats/DE_stats_simulated_data_SRP012656_<n>.txt"\nfor (i in 0:(num_runs-1)){\n simulated_data_filename <- paste(local_dir, \n "pseudo_experiment/selected_simulated_data_",\n project_id,\n "_", \n i,\n ".txt",\n sep = "")\n \n get_DE_stats_limma(metadata_filename,\n project_id, \n simulated_data_filename,\n "simulated",\n local_dir,\n i)\n}' ) # ### Rank genes # In[14]: analysis_type = "DE" template_DE_stats, simulated_DE_summary_stats = ranking.process_and_rank_genes_pathways( template_DE_stats_filename, local_dir, num_runs, project_id, analysis_type, col_to_rank_genes, logFC_name, pvalue_name, ) # ### Gene summary table # In[15]: summary_gene_ranks = ranking.generate_summary_table( template_DE_stats_filename, template_DE_stats, simulated_DE_summary_stats, col_to_rank_genes, local_dir, 'gene', params) summary_gene_ranks.head()
# In[13]: analysis_type = "GSA" template_GSEA_stats_filename = os.path.join( local_dir, "GSA_stats", f"{enrichment_method}_stats_template_data_{project_id}_real.txt" ) template_GSEA_stats, simulated_GSEA_summary_stats = ranking.process_and_rank_genes_pathways( template_GSEA_stats_filename, local_dir, num_runs, project_id, analysis_type, col_to_rank_pathways, enrichment_method ) # ## Pathway summary table # In[14]: # Create intermediate file: "<local_dir>/gene_summary_table_<col_to_rank_pathways>.tsv" summary_pathway_ranks = ranking.generate_summary_table( template_GSEA_stats_filename, template_GSEA_stats,