<filename>gene_analysis_pipeline.py
import os
import glob
import re
import time
from pyprojroot import here


class ParamSet:
	def __init__(self, base_directory, extractedDataDir, plotsDir, refsDir,
				 deseq_qval = 0.05, deseq_min_log2_fold_change = 2,
				 addPredFcDiffMin_integration_histogram =   0, minTpmDiff_integration_histogram = 0,
				 addPredFcDiffMin_cValByDoseGeneSetPlot =   1, minTpmDiff_cValByDoseGeneSetPlot = 2,
				 use_default_param_string = False):
		self.base_directory      	 	            = base_directory  # this should be the analysis root directory, which contains folders like "extractedData" and "plotScripts"
		self.sample_metadata_file		            = '"{0}"'.format(base_directory + os.sep + "sampleMetadata_SI2-SI4.txt")
		self.extractedDataDir 			            = extractedDataDir
		self.plotsDir       			            = plotsDir
		self.refsDir                                = refsDir
		self.deseq_qval 				            = deseq_qval
		self.deseq_min_log2_fold_change             = deseq_min_log2_fold_change
		self.addPredFcDiffMin_integration_histogram = addPredFcDiffMin_integration_histogram
		self.minTpmDiff_integration_histogram       = minTpmDiff_integration_histogram
		self.addPredFcDiffMin_cValByDoseGeneSetPlot = addPredFcDiffMin_cValByDoseGeneSetPlot
		self.minTpmDiff_cValByDoseGeneSetPlot       = minTpmDiff_cValByDoseGeneSetPlot

		param_summary_string = "qval{0}_minlfc{1}".format(deseq_qval, deseq_min_log2_fold_change)
		if use_default_param_string:
			param_summary_string = "defaultParams"

		# directories that need to exist for saving results
		self.rna_seq_matrix_dir  = os.sep.join([extractedDataDir, "rnaSeqMatrixFormatted"])
		self.bee_swarm_plots_dir = os.sep.join([plotsDir, "beeSwarmPlots"])
		self.integration_summary_plots_dir = os.sep.join([plotsDir, "gene_integration_summary_plots"])
		self.secondary_cval_peak_plots_dir = os.sep.join([plotsDir, "secondary_c_value_peak_analysis_plots"])
		self.integration_summary_null_distribution_plots_dir = os.sep.join([plotsDir, "gene_integration_summary_plots", "null_distributions"])
		self.subdirectories = [plotsDir, extractedDataDir, refsDir, self.rna_seq_matrix_dir, self.bee_swarm_plots_dir, self.integration_summary_plots_dir,
							   self.integration_summary_null_distribution_plots_dir, self.secondary_cval_peak_plots_dir]

		# paths to input and output files
		self.rnaseq_pipeline_counts_output_file      = '"{0}"'.format(os.sep.join([extractedDataDir, "si2-si4_RNA-seq-pipeline-output-counts.tsv"]))
		self.gene_counts_file_with_normalized_values = '"{0}"'.format(os.sep.join([extractedDataDir, "si2-si4_RNA-seq-pipeline-output-normalized.tsv"]))
		self.gene_counts_matrix_for_deseq            = '"{0}"'.format(os.sep.join([self.rna_seq_matrix_dir, "counts.RNA-seq-matrix.min-count-filtered.rds"]))
		self.deseq_output_table                      = '"{0}"'.format(os.sep.join([extractedDataDir, "DeSeqOutputAllConds.tsv"]))
		self.annotated_deseq_output_table            = '"{0}"'.format(os.sep.join([extractedDataDir, "DeSeqOutputAllConds.annotated.tsv"]))
		self.upregulated_genes_table                 = '"{0}"'.format(os.sep.join([extractedDataDir, "DeSeqOutputAllConds.annotated.upregulatedGeneSet.tsv"]))
		self.cValChangesWithDosePlot                 = '"{0}"'.format(os.sep.join([plotsDir, "cvals"]))

		# paths to reference files
		self.hg38_gtf_file                      = '"{0}"'.format(os.sep.join([refsDir, "hg38.gtf"]))                   # gtf file version: Homo_sapiens.GRCh38.90.gtf.gz
		self.ensg_to_hgnc_symbol_mapping        = '"{0}"'.format(os.sep.join([refsDir, "EnsgHgncSymbolMapping.tsv"]))  # we use the gene name from the gtf file if there's no matching HGNC symbol
		self.ensg_to_hg38_canonical_tss_mapping = '"{0}"'.format(os.sep.join([refsDir, "EnsgToHg38CanonicalTssMapping.tsv"])) 

		# paths to scripts, we need to add quotes around them to pass as command line arguments because dropbox added a space to its own folder and we're using os.system to run commands
		self.path_to_normalizePipelineCountsOutputAndAddGeneSymbol = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "normalizePipelineCountsOutputAndAddGeneSymbol.R"]))
		self.makeGeneExpressionMatrixWithMinCounts                 = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "makeGeneExpressionMatrixWithMinCounts.R"]))
		self.path_to_runDESeqOnConditionSet                        = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "runDESeqOnConditionSet.R"]))
		self.path_to_addIntegrationMetricsToDeGenes                = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "addIntegrationMetricsToDeGenes.R"]))
		self.path_to_createMasterSetOfUpregulatedGenes             = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "createMasterSetOfUpregulatedGenes.R"]))
		self.path_to_makeGeneBeeswarmPlot                          = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "makeGeneBeeswarmPlot.R"]))
		self.path_to_geneIntegrationSummaryPieChartsAndHistograms  = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "geneIntegrationSummaryPieChartsAndHistograms.R"]))
		self.path_to_makeNullDistributionCorDvalue                 = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "makeNullDistributionCorDvalue.R"]))
		self.path_to_createCvalChangesWithDosePlot                 = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "createCvalChangesWithDosePlot.R"]))
		self.path_to_subtractSimAdditiveFromObservedCvalHistogram  = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "subtractSimulatedAdditiveFromObservedCvalHistogram.R"]))



	def __str__(self):
		obj_string = "peak_merge_distance = {0}\n" \
					 "initial_peak_width = {1}\n" \
					 "final_diffpeak_algorithm_min_normalized_fragments = {2}\n" \
					 "final_diffpeak_algorithm_min_fold_change = {3}\n".format(self.peak_merge_distance, 
					 										self.initial_peak_width, 
					 										self.final_diffpeak_algorithm_min_normalized_fragments, 
					 										self.final_diffpeak_algorithm_min_fold_change)
		return(obj_string)

def run_command(cmd):
	time.sleep(1)  # but in this buffer to help avoid the problem where a new process tries to run before its output file is fully written
	print(cmd)
	os.system(cmd)


def main(param_obj, run_all_steps = False):
	# make sub-directories if they don't already exist
	for dirname in param_obj.subdirectories:
		if not os.path.exists(dirname):
			os.mkdir(dirname)

	if run_all_steps or not os.path.exists(param_obj.gene_counts_file_with_normalized_values[1:-1]):
		cmd = 'Rscript {0} {1} {2} {3} {4} {5}'.format(param_obj.path_to_normalizePipelineCountsOutputAndAddGeneSymbol,
													   param_obj.rnaseq_pipeline_counts_output_file,
													   param_obj.hg38_gtf_file,
													   param_obj.ensg_to_hgnc_symbol_mapping,
													   param_obj.ensg_to_hg38_canonical_tss_mapping,
													   param_obj.gene_counts_file_with_normalized_values)
		run_command(cmd)

	matrix_output_files = glob.glob(param_obj.rna_seq_matrix_dir + '/*.rds')
	if run_all_steps or not len(matrix_output_files) == 3:
		cmd = 'Rscript {0} {1} {2}'.format(param_obj.makeGeneExpressionMatrixWithMinCounts,
										   param_obj.gene_counts_file_with_normalized_values,
										   '"{0}"'.format(param_obj.rna_seq_matrix_dir))
		run_command(cmd)
	
	if run_all_steps or not os.path.exists(param_obj.deseq_output_table[1:-1]):
		cmd = 'Rscript {0} {1} {2} {3} {4}'.format(param_obj.path_to_runDESeqOnConditionSet,
												   param_obj.gene_counts_matrix_for_deseq,
												   param_obj.sample_metadata_file,
												   param_obj.gene_counts_file_with_normalized_values,
												   param_obj.deseq_output_table)
		run_command(cmd)

	if run_all_steps or not os.path.exists(param_obj.annotated_deseq_output_table[1:-1]):
		cmd = 'Rscript {0} {1} {2} {3}'.format(param_obj.path_to_addIntegrationMetricsToDeGenes,
											   param_obj.deseq_output_table,
											   param_obj.sample_metadata_file,
											   param_obj.annotated_deseq_output_table)
		run_command(cmd)

	if run_all_steps or not os.path.exists(param_obj.upregulated_genes_table[1:-1]):
		cmd = 'Rscript {0} {1} {2}'.format(param_obj.path_to_createMasterSetOfUpregulatedGenes,
										   param_obj.annotated_deseq_output_table,
										   param_obj.upregulated_genes_table)
		run_command(cmd)

	bee_swarm_plot_paths = glob.glob(param_obj.bee_swarm_plots_dir + '/*.svg')
	if run_all_steps or len(bee_swarm_plot_paths) == 0:
		cmd = 'Rscript {0} {1} {2} {3}'.format(param_obj.path_to_makeGeneBeeswarmPlot,
											   param_obj.annotated_deseq_output_table,
											   param_obj.sample_metadata_file,
											   '"{0}"'.format(param_obj.bee_swarm_plots_dir))
		run_command(cmd)

	integration_summary_plot_paths = glob.glob(param_obj.integration_summary_plots_dir + '/*.svg')
	if run_all_steps or len(integration_summary_plot_paths) == 0:
		cmd = 'Rscript {0} {1} {2} {3} {4}'.format(param_obj.path_to_geneIntegrationSummaryPieChartsAndHistograms,
												   param_obj.upregulated_genes_table,
												   param_obj.addPredFcDiffMin_integration_histogram,
												   param_obj.minTpmDiff_integration_histogram,
												   '"{0}"'.format(param_obj.integration_summary_plots_dir))
		run_command(cmd)

	secondary_peak_cval_analysis_plot_paths = glob.glob(param_obj.secondary_cval_peak_plots_dir + '/*.svg')
	if run_all_steps or len(secondary_peak_cval_analysis_plot_paths) == 0:
		cmd = 'Rscript {0} {1} {2} {3}'.format(param_obj.path_to_subtractSimAdditiveFromObservedCvalHistogram,
											   param_obj.upregulated_genes_table,
											   '"{0}"'.format(param_obj.secondary_cval_peak_plots_dir),
											   '"{0}"'.format(param_obj.integration_summary_plots_dir))
		run_command(cmd)	

	# commented out Oct 5 2020 since it is more efficient to calculate these null distributions with the (new) preceding step
	# integration_summary_null_histogram_paths = glob.glob(param_obj.integration_summary_null_distribution_plots_dir + '/*.svg')
	# if run_all_steps or len(integration_summary_null_histogram_paths) == 0:
	# 	cmd = 'Rscript {0} {1} {2} {3} {4} {5}'.format(param_obj.path_to_makeNullDistributionCorDvalue,
	# 													   param_obj.upregulated_genes_table,
	# 													   param_obj.addPredFcDiffMin_integration_histogram,
	# 													   param_obj.minTpmDiff_integration_histogram,
	# 													   "genes",
	# 													   '"{0}"'.format(param_obj.integration_summary_null_distribution_plots_dir))
	# 	run_command(cmd)

	if run_all_steps or not os.path.exists(param_obj.cValChangesWithDosePlot[1:-1] + "EachDoseForSetOfGenes.svg"):
		cmd = 'Rscript {0} {1} {2} {3} {4}'.format(param_obj.path_to_createCvalChangesWithDosePlot,
												   param_obj.upregulated_genes_table,
												   param_obj.addPredFcDiffMin_cValByDoseGeneSetPlot,
												   param_obj.minTpmDiff_cValByDoseGeneSetPlot,
												   param_obj.cValChangesWithDosePlot)
		run_command(cmd)
	

if __name__ == '__main__':
	do_parameter_sweep = False
	run_all_steps      = False

	if do_parameter_sweep:
		pass
	else:
		# this parameter object stores the default parameters that we chose after parameter sweeps
		basedir = str(here())
		extractedDataDir = os.sep.join([basedir, "extractedData"])
		plotsDir = os.sep.join([basedir, "plots"])
		refsDir  = os.sep.join([basedir, "refs"])

		param_obj = ParamSet(basedir, 
							 extractedDataDir,
							 plotsDir,
							 refsDir,
							 deseq_qval = 0.05, 
							 deseq_min_log2_fold_change = 2, 
							 use_default_param_string = True,
							 addPredFcDiffMin_integration_histogram = 0, 
							 minTpmDiff_integration_histogram = 0,)
		main(param_obj, run_all_steps = run_all_steps)

	





<filename>gene_analysis_pipeline.py
import os
import glob
import re
import time
from pyprojroot import here


class ParamSet:
	def __init__(self, base_directory, extractedDataDir, plotsDir, refsDir,
				 deseq_qval = 0.05, deseq_min_log2_fold_change = 2,
				 addPredFcDiffMin_integration_histogram =   0, minTpmDiff_integration_histogram = 0,
				 addPredFcDiffMin_cValByDoseGeneSetPlot =   1, minTpmDiff_cValByDoseGeneSetPlot = 2,
				 use_default_param_string = False):
		self.base_directory      	 	            = base_directory  # this should be the analysis root directory, which contains folders like "extractedData" and "plotScripts"
		self.sample_metadata_file		            = '"{0}"'.format(base_directory + os.sep + "sampleMetadata_SI2-SI4.txt")
		self.extractedDataDir 			            = extractedDataDir
		self.plotsDir       			            = plotsDir
		self.refsDir                                = refsDir
		self.deseq_qval 				            = deseq_qval
		self.deseq_min_log2_fold_change             = deseq_min_log2_fold_change
		self.addPredFcDiffMin_integration_histogram = addPredFcDiffMin_integration_histogram
		self.minTpmDiff_integration_histogram       = minTpmDiff_integration_histogram
		self.addPredFcDiffMin_cValByDoseGeneSetPlot = addPredFcDiffMin_cValByDoseGeneSetPlot
		self.minTpmDiff_cValByDoseGeneSetPlot       = minTpmDiff_cValByDoseGeneSetPlot

		param_summary_string = "qval{0}_minlfc{1}".format(deseq_qval, deseq_min_log2_fold_change)
		if use_default_param_string:
			param_summary_string = "defaultParams"

		# directories that need to exist for saving results
		self.rna_seq_matrix_dir  = os.sep.join([extractedDataDir, "rnaSeqMatrixFormatted"])
		self.bee_swarm_plots_dir = os.sep.join([plotsDir, "beeSwarmPlots"])
		self.integration_summary_plots_dir = os.sep.join([plotsDir, "gene_integration_summary_plots"])
		self.secondary_cval_peak_plots_dir = os.sep.join([plotsDir, "secondary_c_value_peak_analysis_plots"])
		self.integration_summary_null_distribution_plots_dir = os.sep.join([plotsDir, "gene_integration_summary_plots", "null_distributions"])
		self.subdirectories = [plotsDir, extractedDataDir, refsDir, self.rna_seq_matrix_dir, self.bee_swarm_plots_dir, self.integration_summary_plots_dir,
							   self.integration_summary_null_distribution_plots_dir, self.secondary_cval_peak_plots_dir]

		# paths to input and output files
		self.rnaseq_pipeline_counts_output_file      = '"{0}"'.format(os.sep.join([extractedDataDir, "si2-si4_RNA-seq-pipeline-output-counts.tsv"]))
		self.gene_counts_file_with_normalized_values = '"{0}"'.format(os.sep.join([extractedDataDir, "si2-si4_RNA-seq-pipeline-output-normalized.tsv"]))
		self.gene_counts_matrix_for_deseq            = '"{0}"'.format(os.sep.join([self.rna_seq_matrix_dir, "counts.RNA-seq-matrix.min-count-filtered.rds"]))
		self.deseq_output_table                      = '"{0}"'.format(os.sep.join([extractedDataDir, "DeSeqOutputAllConds.tsv"]))
		self.annotated_deseq_output_table            = '"{0}"'.format(os.sep.join([extractedDataDir, "DeSeqOutputAllConds.annotated.tsv"]))
		self.upregulated_genes_table                 = '"{0}"'.format(os.sep.join([extractedDataDir, "DeSeqOutputAllConds.annotated.upregulatedGeneSet.tsv"]))
		self.cValChangesWithDosePlot                 = '"{0}"'.format(os.sep.join([plotsDir, "cvals"]))

		# paths to reference files
		self.hg38_gtf_file                      = '"{0}"'.format(os.sep.join([refsDir, "hg38.gtf"]))                   # gtf file version: Homo_sapiens.GRCh38.90.gtf.gz
		self.ensg_to_hgnc_symbol_mapping        = '"{0}"'.format(os.sep.join([refsDir, "EnsgHgncSymbolMapping.tsv"]))  # we use the gene name from the gtf file if there's no matching HGNC symbol
		self.ensg_to_hg38_canonical_tss_mapping = '"{0}"'.format(os.sep.join([refsDir, "EnsgToHg38CanonicalTssMapping.tsv"])) 

		# paths to scripts, we need to add quotes around them to pass as command line arguments because dropbox added a space to its own folder and we're using os.system to run commands
		self.path_to_normalizePipelineCountsOutputAndAddGeneSymbol = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "normalizePipelineCountsOutputAndAddGeneSymbol.R"]))
		self.makeGeneExpressionMatrixWithMinCounts                 = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "makeGeneExpressionMatrixWithMinCounts.R"]))
		self.path_to_runDESeqOnConditionSet                        = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "runDESeqOnConditionSet.R"]))
		self.path_to_addIntegrationMetricsToDeGenes                = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "addIntegrationMetricsToDeGenes.R"]))
		self.path_to_createMasterSetOfUpregulatedGenes             = '"{0}"'.format(os.sep.join([base_directory, "extractionScripts", "createMasterSetOfUpregulatedGenes.R"]))
		self.path_to_makeGeneBeeswarmPlot                          = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "makeGeneBeeswarmPlot.R"]))
		self.path_to_geneIntegrationSummaryPieChartsAndHistograms  = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "geneIntegrationSummaryPieChartsAndHistograms.R"]))
		self.path_to_makeNullDistributionCorDvalue                 = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "makeNullDistributionCorDvalue.R"]))
		self.path_to_createCvalChangesWithDosePlot                 = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "createCvalChangesWithDosePlot.R"]))
		self.path_to_subtractSimAdditiveFromObservedCvalHistogram  = '"{0}"'.format(os.sep.join([base_directory, "plotScripts", "subtractSimulatedAdditiveFromObservedCvalHistogram.R"]))



	def __str__(self):
		obj_string = "peak_merge_distance = {0}\n" \
					 "initial_peak_width = {1}\n" \
					 "final_diffpeak_algorithm_min_normalized_fragments = {2}\n" \
					 "final_diffpeak_algorithm_min_fold_change = {3}\n".format(self.peak_merge_distance, 
					 										self.initial_peak_width, 
					 										self.final_diffpeak_algorithm_min_normalized_fragments, 
					 										self.final_diffpeak_algorithm_min_fold_change)
		return(obj_string)

def run_command(cmd):
	time.sleep(1)  # but in this buffer to help avoid the problem where a new process tries to run before its output file is fully written
	print(cmd)
	os.system(cmd)


def main(param_obj, run_all_steps = False):
	# make sub-directories if they don't already exist
	for dirname in param_obj.subdirectories:
		if not os.path.exists(dirname):
			os.mkdir(dirname)

	if run_all_steps or not os.path.exists(param_obj.gene_counts_file_with_normalized_values[1:-1]):
		cmd = 'Rscript {0} {1} {2} {3} {4} {5}'.format(param_obj.path_to_normalizePipelineCountsOutputAndAddGeneSymbol,
													   param_obj.rnaseq_pipeline_counts_output_file,
													   param_obj.hg38_gtf_file,
													   param_obj.ensg_to_hgnc_symbol_mapping,
													   param_obj.ensg_to_hg38_canonical_tss_mapping,
													   param_obj.gene_counts_file_with_normalized_values)
		run_command(cmd)

	matrix_output_files = glob.glob(param_obj.rna_seq_matrix_dir + '/*.rds')
	if run_all_steps or not len(matrix_output_files) == 3:
		cmd = 'Rscript {0} {1} {2}'.format(param_obj.makeGeneExpressionMatrixWithMinCounts,
										   param_obj.gene_counts_file_with_normalized_values,
										   '"{0}"'.format(param_obj.rna_seq_matrix_dir))
		run_command(cmd)
	
	if run_all_steps or not os.path.exists(param_obj.deseq_output_table[1:-1]):
		cmd = 'Rscript {0} {1} {2} {3} {4}'.format(param_obj.path_to_runDESeqOnConditionSet,
												   param_obj.gene_counts_matrix_for_deseq,
												   param_obj.sample_metadata_file,
												   param_obj.gene_counts_file_with_normalized_values,
												   param_obj.deseq_output_table)
		run_command(cmd)

	if run_all_steps or not os.path.exists(param_obj.annotated_deseq_output_table[1:-1]):
		cmd = 'Rscript {0} {1} {2} {3}'.format(param_obj.path_to_addIntegrationMetricsToDeGenes,
											   param_obj.deseq_output_table,
											   param_obj.sample_metadata_file,
											   param_obj.annotated_deseq_output_table)
		run_command(cmd)

	if run_all_steps or not os.path.exists(param_obj.upregulated_genes_table[1:-1]):
		cmd = 'Rscript {0} {1} {2}'.format(param_obj.path_to_createMasterSetOfUpregulatedGenes,
										   param_obj.annotated_deseq_output_table,
										   param_obj.upregulated_genes_table)
		run_command(cmd)

	bee_swarm_plot_paths = glob.glob(param_obj.bee_swarm_plots_dir + '/*.svg')
	if run_all_steps or len(bee_swarm_plot_paths) == 0:
		cmd = 'Rscript {0} {1} {2} {3}'.format(param_obj.path_to_makeGeneBeeswarmPlot,
											   param_obj.annotated_deseq_output_table,
											   param_obj.sample_metadata_file,
											   '"{0}"'.format(param_obj.bee_swarm_plots_dir))
		run_command(cmd)

	integration_summary_plot_paths = glob.glob(param_obj.integration_summary_plots_dir + '/*.svg')
	if run_all_steps or len(integration_summary_plot_paths) == 0:
		cmd = 'Rscript {0} {1} {2} {3} {4}'.format(param_obj.path_to_geneIntegrationSummaryPieChartsAndHistograms,
												   param_obj.upregulated_genes_table,
												   param_obj.addPredFcDiffMin_integration_histogram,
												   param_obj.minTpmDiff_integration_histogram,
												   '"{0}"'.format(param_obj.integration_summary_plots_dir))
		run_command(cmd)

	secondary_peak_cval_analysis_plot_paths = glob.glob(param_obj.secondary_cval_peak_plots_dir + '/*.svg')
	if run_all_steps or len(secondary_peak_cval_analysis_plot_paths) == 0:
		cmd = 'Rscript {0} {1} {2} {3}'.format(param_obj.path_to_subtractSimAdditiveFromObservedCvalHistogram,
											   param_obj.upregulated_genes_table,
											   '"{0}"'.format(param_obj.secondary_cval_peak_plots_dir),
											   '"{0}"'.format(param_obj.integration_summary_plots_dir))
		run_command(cmd)	

	# commented out Oct 5 2020 since it is more efficient to calculate these null distributions with the (new) preceding step
	# integration_summary_null_histogram_paths = glob.glob(param_obj.integration_summary_null_distribution_plots_dir + '/*.svg')
	# if run_all_steps or len(integration_summary_null_histogram_paths) == 0:
	# 	cmd = 'Rscript {0} {1} {2} {3} {4} {5}'.format(param_obj.path_to_makeNullDistributionCorDvalue,
	# 													   param_obj.upregulated_genes_table,
	# 													   param_obj.addPredFcDiffMin_integration_histogram,
	# 													   param_obj.minTpmDiff_integration_histogram,
	# 													   "genes",
	# 													   '"{0}"'.format(param_obj.integration_summary_null_distribution_plots_dir))
	# 	run_command(cmd)

	if run_all_steps or not os.path.exists(param_obj.cValChangesWithDosePlot[1:-1] + "EachDoseForSetOfGenes.svg"):
		cmd = 'Rscript {0} {1} {2} {3} {4}'.format(param_obj.path_to_createCvalChangesWithDosePlot,
												   param_obj.upregulated_genes_table,
												   param_obj.addPredFcDiffMin_cValByDoseGeneSetPlot,
												   param_obj.minTpmDiff_cValByDoseGeneSetPlot,
												   param_obj.cValChangesWithDosePlot)
		run_command(cmd)
	

if __name__ == '__main__':
	do_parameter_sweep = False
	run_all_steps      = False

	if do_parameter_sweep:
		pass
	else:
		# this parameter object stores the default parameters that we chose after parameter sweeps
		basedir = str(here())
		extractedDataDir = os.sep.join([basedir, "extractedData"])
		plotsDir = os.sep.join([basedir, "plots"])
		refsDir  = os.sep.join([basedir, "refs"])

		param_obj = ParamSet(basedir, 
							 extractedDataDir,
							 plotsDir,
							 refsDir,
							 deseq_qval = 0.05, 
							 deseq_min_log2_fold_change = 2, 
							 use_default_param_string = True,
							 addPredFcDiffMin_integration_histogram = 0, 
							 minTpmDiff_integration_histogram = 0,)
		main(param_obj, run_all_steps = run_all_steps)

	





# this should be the analysis root directory, which contains folders like "extractedData" and "plotScripts" # directories that need to exist for saving results # paths to input and output files # paths to reference files # gtf file version: Homo_sapiens.GRCh38.90.gtf.gz # we use the gene name from the gtf file if there's no matching HGNC symbol # paths to scripts, we need to add quotes around them to pass as command line arguments because dropbox added a space to its own folder and we're using os.system to run commands # but in this buffer to help avoid the problem where a new process tries to run before its output file is fully written # make sub-directories if they don't already exist # commented out Oct 5 2020 since it is more efficient to calculate these null distributions with the (new) preceding step # integration_summary_null_histogram_paths = glob.glob(param_obj.integration_summary_null_distribution_plots_dir + '/*.svg') # if run_all_steps or len(integration_summary_null_histogram_paths) == 0: # 	cmd = 'Rscript {0} {1} {2} {3} {4} {5}'.format(param_obj.path_to_makeNullDistributionCorDvalue, # 													   param_obj.upregulated_genes_table, # 													   param_obj.addPredFcDiffMin_integration_histogram, # 													   param_obj.minTpmDiff_integration_histogram, # 													   "genes", # 													   '"{0}"'.format(param_obj.integration_summary_null_distribution_plots_dir)) # 	run_command(cmd) # this parameter object stores the default parameters that we chose after parameter sweeps