def write_summary(out_path, genes_by_merged_signature, meta_genes,
global_variables, mde_dict):
# append total genes and z scores to data
summary_data = {}
counter = 1
for key in meta_genes:
meta_gene_values = meta_genes[key]
total_genes = str(len(genes_by_merged_signature[key]))
summary_data[counter] = total_genes + "\t" + "\t".join(
str(x) for x in meta_gene_values) + "\n"
counter += 1
# sort the summary data by total genes
summary_data_sorted_on_number_of_genes = sorted(
summary_data,
key=lambda key: int(summary_data[key].split("\t")[0]),
reverse=True)
summary_data_sorted = {}
for i in range(0, len(summary_data_sorted_on_number_of_genes)):
sig_id = "signature_" + str(i + 1) + "\t"
summary_data_sorted[i + 1] = sig_id + summary_data[
summary_data_sorted_on_number_of_genes[i]]
# create header
sample_names = get_samples_ordered_by_order_list(
mde_dict["order_list"], global_variables["samples_by_sample_groups"])
summary_data_sorted[0] = "signature\tsignature_size\t" + "\t".join(
sample_names) + "\n"
# writes the summary
with open(os.path.join(out_path, "Signature_summary.csv"), "w+") as f:
for k, v in summary_data_sorted.iteritems():
f.write(v)
def add_ne_specific_parameters(global_variables, pr_dictionary):
pr_dictionary = add_subsection_r(
os.path.join(pr_dictionary["r_bin_path"], "section_header",
"ne_workflow.txt"), "subsection_r_workflow_type",
"section_header/ne_workflow.txt", pr_dictionary)
# gets the various samples and sample group lists for ne:
order_list = global_variables["sample_groups_default_order"]
sample_groups_by_column = global_variables["sample_groups_by_column"]
samples_by_sample_groups = global_variables["samples_by_sample_groups"]
sample_sheet_column_names = global_variables["sample_sheet_column_names"]
samples_ordered = get_samples_ordered_by_order_list(
order_list[0:len(sample_groups_by_column[0])],
samples_by_sample_groups)
# gets the various R code strings:
samples_r_string = "c(\"" + "\",\"".join(samples_ordered) + "\")"
sample_groups_r_string = "c(\"" + "\",\"".join(order_list) + "\")"
sample_groupings_r_string = get_r_string_sample_groupings(
order_list, samples_by_sample_groups)
samples_by_sample_group_r_string = get_r_string_samples_by_sample_group(
order_list, samples_by_sample_groups)
sample_groups_by_SS_column_r_string = get_r_string_sample_groups_by_SS_column(
order_list, sample_groups_by_column)
sample_groupings_by_SS_column_r_string = get_r_string_sample_groupings_by_SS_column(
order_list, sample_groups_by_column, samples_by_sample_groups)
default_samples_colours_by_SS_column_r_string = get_r_string_default_samples_colours_by_SS_column(
samples_by_sample_groups, order_list, sample_groups_by_column)
default_sample_group_colours_by_SS_column_r_string = get_r_string_default_sample_group_colours_by_SS_column(
samples_by_sample_groups, order_list, sample_groups_by_column)
sample_sheet_column_names_r_string = "c(\"" + "\",\"".join(
sample_sheet_column_names) + "\")"
# updates the pr dictionary
pr_dictionary["workflow_ID"] = "Normalised Expression"
pr_dictionary["sample_sheet_column_names"] = sample_sheet_column_names
pr_dictionary["order_list"] = order_list
pr_dictionary["samples_ordered"] = samples_ordered
pr_dictionary["samples_r_string"] = samples_r_string
pr_dictionary["sample_groups_r_string"] = sample_groups_r_string
pr_dictionary["sample_groupings_r_string"] = sample_groupings_r_string
pr_dictionary[
"samples_by_sample_group_r_string"] = samples_by_sample_group_r_string
pr_dictionary[
"sample_groups_by_SS_column_r_string"] = sample_groups_by_SS_column_r_string
pr_dictionary[
"sample_groupings_by_SS_column_r_string"] = sample_groupings_by_SS_column_r_string
pr_dictionary[
"default_sample_colours_by_SS_column_r_string"] = default_samples_colours_by_SS_column_r_string
pr_dictionary[
"default_sample_group_colours_by_SS_column_r_string"] = default_sample_group_colours_by_SS_column_r_string
pr_dictionary[
"sample_sheet_column_names_r_string"] = sample_sheet_column_names_r_string
return pr_dictionary
def differential_expression_signature(global_variables, infile, out_path, pde_IDs, mpde_dict):
# open data file
data = open(infile).readlines()
# gets a dictionary of genes by signature
genes_by_signature,signatures_by_gene = get_genes_by_signature(data, pde_IDs)
# adds the zscores to the genes by signatures
sample_list = get_samples_ordered_by_order_list(mpde_dict["order_list"], global_variables["samples_by_sample_groups"])
genes_by_signature = get_expression_data(data,sample_list,genes_by_signature,signatures_by_gene)
# iteratively merges signatures
genes_by_merged_signature, meta_genes = merge_signatures(genes_by_signature, mpde_dict, sample_list)
# gets the number of signatures (for the report)
mpde_dict["de_signatures"] = range(1,len(genes_by_merged_signature)+1)
# write data out
write_data(out_path, genes_by_merged_signature)
write_summary(out_path, genes_by_merged_signature, meta_genes, global_variables, mpde_dict)
# returns the updated mpde disct
return mpde_dict
def add_Mde_specific_parameters(global_variables, pr_dictionary,
workflow_parameter_dict):
pr_dictionary = add_subsection_r(
os.path.join(pr_dictionary["r_bin_path"], "section_header",
"mde_workflow.txt"), "subsection_r_workflow_type",
"section_header/mde_workflow.txt", pr_dictionary)
# gets the various samples and sample group lists for Mde:
order_list = workflow_parameter_dict["order_list"]
samples_by_sample_groups = global_variables["samples_by_sample_groups"]
samples_ordered = get_samples_ordered_by_order_list(
order_list, samples_by_sample_groups)
comparisons = workflow_parameter_dict["de_IDs"]
# gets the number of signatures
de_signatures = workflow_parameter_dict["de_signatures"]
# gets the various R code strings:
samples_r_string = get_r_string_samples(samples_ordered)
sample_groups_r_string = get_r_string_sample_groups(order_list)
sample_groupings_r_string = get_r_string_sample_groupings(
order_list, samples_by_sample_groups)
samples_by_sample_group_r_string = get_r_string_samples_by_sample_group(
order_list, samples_by_sample_groups)
default_samples_colours_r_string = get_r_string_default_sample_colours(
order_list, samples_by_sample_groups)
comparisons_r_string = "c(\"" + "\",\"".join(comparisons) + "\")"
# updates the pr_dictionary
pr_dictionary["workflow_ID"] = workflow_parameter_dict["mde_ID"]
pr_dictionary["signatures_scc"] = workflow_parameter_dict["signatures_scc"]
pr_dictionary["order_list"] = order_list
pr_dictionary["samples_ordered"] = samples_ordered
pr_dictionary["comparisons"] = comparisons
pr_dictionary["comparisons_r_string"] = comparisons_r_string
pr_dictionary["samples_by_sample_groups"] = samples_by_sample_groups
pr_dictionary["sample_groups_r_string"] = sample_groups_r_string
pr_dictionary["samples_r_string"] = samples_r_string
pr_dictionary["sample_groupings_r_string"] = sample_groupings_r_string
pr_dictionary[
"samples_by_sample_group_r_string"] = samples_by_sample_group_r_string
pr_dictionary[
"default_samples_colours_r_string"] = default_samples_colours_r_string
pr_dictionary["de_signatures"] = de_signatures
# gets the hypergeometric gene set types
if global_variables["ora_flag"]:
hypergeom_gene_set_types = []
hypergeom_gene_set_min_set_sizes = []
hypergeom_gene_set_max_set_sizes = []
hypergeom_gene_set_p_thresholds = []
hypergeom_gene_set_fold_thresholds = []
hypergeom_gene_set_network_overlap_ratios = []
parsed_hypergeom_gene_sets_parameters = global_variables[
"ora_parameters"]
for hypergeom_gene_set_parameter_dict in parsed_hypergeom_gene_sets_parameters:
hypergeom_gene_set_types.append(
hypergeom_gene_set_parameter_dict["type"])
hypergeom_gene_set_min_set_sizes.append(
hypergeom_gene_set_parameter_dict["min_set_size"])
hypergeom_gene_set_max_set_sizes.append(
hypergeom_gene_set_parameter_dict["max_set_size"])
hypergeom_gene_set_p_thresholds.append(
hypergeom_gene_set_parameter_dict["p_threshold"])
hypergeom_gene_set_fold_thresholds.append(
hypergeom_gene_set_parameter_dict["fold_threshold"])
hypergeom_gene_set_network_overlap_ratios.append(
hypergeom_gene_set_parameter_dict["network_overlap_ratio"])
pr_dictionary["hypergeom_gene_set_types"] = hypergeom_gene_set_types
pr_dictionary[
"hypergeom_gene_set_min_set_sizes"] = hypergeom_gene_set_min_set_sizes
pr_dictionary[
"hypergeom_gene_set_max_set_sizes"] = hypergeom_gene_set_max_set_sizes
pr_dictionary[
"hypergeom_gene_set_p_thresholds"] = hypergeom_gene_set_p_thresholds
pr_dictionary[
"hypergeom_gene_set_fold_thresholds"] = hypergeom_gene_set_fold_thresholds
pr_dictionary[
"hypergeom_gene_set_network_overlap_ratios"] = hypergeom_gene_set_network_overlap_ratios
return pr_dictionary
def add_de_specific_parameters(global_variables, pr_dictionary,
workflow_parameter_dict):
# adds the r subsection workflow type
pr_dictionary = add_subsection_r(
os.path.join(pr_dictionary["r_bin_path"], "section_header",
"de_workflow.txt"), "subsection_r_workflow_type",
"section_header/de_workflow.txt", pr_dictionary)
# gets the various samples and sample group lists for de:
order_list = workflow_parameter_dict["order_list"]
samples_by_sample_groups = global_variables["samples_by_sample_groups"]
samples_ordered = get_samples_ordered_by_order_list(
order_list, samples_by_sample_groups)
# gets the various R code strings:
samples_r_string = get_r_string_samples(samples_ordered)
sample_groups_r_string = get_r_string_sample_groups(order_list)
sample_groupings_r_string = get_r_string_sample_groupings(
order_list, samples_by_sample_groups)
samples_by_sample_group_r_string = get_r_string_samples_by_sample_group(
order_list, samples_by_sample_groups)
default_samples_colours_r_string = get_r_string_default_sample_colours(
order_list, samples_by_sample_groups)
comparisons_r_string = "c(\"" + workflow_parameter_dict["de_ID"] + "\")"
# gets the list of chromosomes
chromosomes_list = get_chromosome_list(
os.path.join(pr_dictionary["workflow_outpath"], "data",
"de_annotated.csv"))
# updates the pr_dictionary
pr_dictionary["workflow_ID"] = workflow_parameter_dict["de_ID"]
pr_dictionary["de_p_threshold"] = workflow_parameter_dict["p_threshold"]
pr_dictionary["de_fold_threshold"] = workflow_parameter_dict[
"fold_threshold"]
pr_dictionary["de_numerator_group"] = workflow_parameter_dict[
"numerator_group"]
pr_dictionary["de_denominator_group"] = workflow_parameter_dict[
"denominator_group"]
pr_dictionary["de_file_path"] = workflow_parameter_dict["de_file_path"]
pr_dictionary[
"differential_expression_set_size"] = workflow_parameter_dict[
"differential_expression_set_size"]
pr_dictionary["order_list"] = order_list
pr_dictionary["samples_ordered"] = samples_ordered
pr_dictionary["comparisons_r_string"] = comparisons_r_string
pr_dictionary["samples_by_sample_groups"] = samples_by_sample_groups
pr_dictionary["sample_groups_r_string"] = sample_groups_r_string
pr_dictionary["samples_r_string"] = samples_r_string
pr_dictionary["sample_groupings_r_string"] = sample_groupings_r_string
pr_dictionary[
"samples_by_sample_group_r_string"] = samples_by_sample_group_r_string
pr_dictionary[
"default_samples_colours_r_string"] = default_samples_colours_r_string
pr_dictionary["chromosome_list"] = chromosomes_list
# gets the hypergeometric gene set types
if global_variables["ora_flag"]:
hypergeom_gene_set_types = []
hypergeom_gene_set_min_set_sizes = []
hypergeom_gene_set_max_set_sizes = []
hypergeom_gene_set_p_thresholds = []
hypergeom_gene_set_fold_thresholds = []
hypergeom_gene_set_network_overlap_ratios = []
parsed_hypergeom_gene_sets_parameters = global_variables[
"ora_parameters"]
for hypergeom_gene_set_parameter_dict in parsed_hypergeom_gene_sets_parameters:
hypergeom_gene_set_types.append(
hypergeom_gene_set_parameter_dict["type"])
hypergeom_gene_set_min_set_sizes.append(
hypergeom_gene_set_parameter_dict["min_set_size"])
hypergeom_gene_set_max_set_sizes.append(
hypergeom_gene_set_parameter_dict["max_set_size"])
hypergeom_gene_set_p_thresholds.append(
hypergeom_gene_set_parameter_dict["p_threshold"])
hypergeom_gene_set_fold_thresholds.append(
hypergeom_gene_set_parameter_dict["fold_threshold"])
hypergeom_gene_set_network_overlap_ratios.append(
hypergeom_gene_set_parameter_dict["network_overlap_ratio"])
pr_dictionary["hypergeom_gene_set_types"] = hypergeom_gene_set_types
pr_dictionary[
"hypergeom_gene_set_min_set_sizes"] = hypergeom_gene_set_min_set_sizes
pr_dictionary[
"hypergeom_gene_set_max_set_sizes"] = hypergeom_gene_set_max_set_sizes
pr_dictionary[
"hypergeom_gene_set_p_thresholds"] = hypergeom_gene_set_p_thresholds
pr_dictionary[
"hypergeom_gene_set_fold_thresholds"] = hypergeom_gene_set_fold_thresholds
pr_dictionary[
"hypergeom_gene_set_network_overlap_ratios"] = hypergeom_gene_set_network_overlap_ratios
# gets the ipa ureg types
if global_variables["ura_flag"]:
ura_types = []
ura_min_set_sizes = []
ura_max_set_sizes = []
ura_zscore_thresholds = []
ura_p_thresholds = []
ura_fold_thresholds = []
ura_overlap_ratios = []
parsed_ura_parameters = global_variables["ura_parameters"]
for ura_parameters_dict in parsed_ura_parameters:
ura_types.append(ura_parameters_dict["type"])
ura_min_set_sizes.append(ura_parameters_dict["min_set_size"])
ura_max_set_sizes.append(ura_parameters_dict["max_set_size"])
ura_zscore_thresholds.append(
ura_parameters_dict["zscore_threshold"])
ura_p_thresholds.append(ura_parameters_dict["p_threshold"])
ura_fold_thresholds.append(ura_parameters_dict["fold_threshold"])
ura_overlap_ratios.append(
ura_parameters_dict["network_overlap_ratio"])
pr_dictionary["ura_types"] = ura_types
pr_dictionary["ura_min_set_sizes"] = ura_min_set_sizes
pr_dictionary["ura_max_set_sizes"] = ura_max_set_sizes
pr_dictionary["ura_zscore_thresholds"] = ura_zscore_thresholds
pr_dictionary["ura_p_thresholds"] = ura_p_thresholds
pr_dictionary["ura_fold_thresholds"] = ura_fold_thresholds
pr_dictionary["ura_overlap_ratios"] = ura_overlap_ratios
return pr_dictionary