def next_state_base(self, text):
self.write_variable("throw_away", False)
file_str = self.read_variable("loaded_file").content
success, headers = check_file_header(file_str)
self.context['headers'] = headers
format_header = util.prettify_data(headers)
if not success or self.force_ask:
state_machine = []
if self.force_ask:
state_machine.append(
sm.Assign(
"throw_away",
t.YesNo(
"In that case, would you like to throw away the first line of data?",
yes=True,
no=False)))
state_machine.append(
sm.Print(
["Okay, how would you like to generate the header?"]))
else:
state_machine.append(
sm.Print(["This file does not appear to have a header."]))
state_machine.append(
t.Select(
options={
"Generate the values automatically": GenerateHeaders(),
"Enter the headers manually": AskForHeaders(),
"Use first line as header:": FirstLineHeader()
}))
return sm.DoAll(state_machine).when_done(
self.get_when_done_state())
return FirstLineHeader().when_done(self.get_when_done_state())
class DrugGenes(IrisCommand):
title = "How does {drug} affect these list of {genes}? "
examples = []
argument_types = {
"drug":
t.String(
"Okay, a couple more questions to set up this task. For confirmation: What is the drug you want to analyze?"
),
"genes":
t.List(
"What genes do you want to analyze? (enter NCBI gene names separated by commas)"
),
"bool_image":
t.YesNo("Would you like to visualize the results as a diagram?",
yes=True,
no=False),
"bool_pubmed":
t.YesNo("Would you like to get the list of pubmed IDs for reference?",
yes=True,
no=False),
"bool_other_disease":
t.YesNo(
"Would you like to know other diseases that can be affected by the given drug?",
yes=True,
no=False)
}
# core logic of the command
def command(self, drug, genes, bool_image, bool_pubmed,
bool_other_disease):
print('BEFORE QUERY!!!')
# create options structure
options = Options(gen_image=bool_image,
gene_list=genes,
gen_pubmed=bool_pubmed)
answer = Q2_query(drug, None, options) # disease is none
print('ran Q2 query!!!')
# Error handling in disease or drug is not found
if isinstance(answer, str):
answer_str = answer
answer = {}
answer['error'] = answer_str
# if want to find other indications
if bool_other_disease:
answer["other_disease"] = run_main.find_drug_indications(drug)
answer['drug'] = drug.strip()
return answer
def explanation(self, result):
# Components of result are in dictionary form
if "error" in result:
return result['error']
query_name = result['drug'][:min(len(result['drug']), 3)] + "_GENE"
query_name = '_'.join(query_name.split(' '))
query_name = "_" + query_name.lower()
print(query_name, "== query name")
# Print out genes associated with drug
query_statement = 'How does ' + result[
'drug'] + ' affect: ' + ', '.join(result['disease_genes']) + '?'
result_array = ['Here are your results for: %s' % query_statement]
result_array.append(
'Top genes found to be targetted by %s are below. Full dataset saved as drug_genes_{drug_disease}'
% result['drug'])
drug_gene_term_object = iris_objects.IrisDataframe(
data=result['drug_genes'])
self.iris.add_to_env('drug_genes' + query_name, drug_gene_term_object)
drug_gene_term_object_short = iris_objects.IrisDataframe(
data=result['drug_genes_short'])
result_array.append(drug_gene_term_object_short)
print('added genes')
# result_array.append("Full dataset saved as drug_associated_genes")
# # Print out genes associated with disease
# disease_gene_term_object = iris_objects.IrisDataframe(data=result['disease_genes'])
# self.iris.add_to_env('disease_genes' + query_name, disease_gene_term_object)
# disease_gene_term_object_short = iris_objects.IrisDataframe(data=result['disease_genes_short'])
# result_array.append(disease_gene_term_object_short)
# Print out signficant go terms
result_array.append(
'Top significant GO terms associated with the drug-disease interaction are shown. Full dataset saved as go_terms_{drug_disease}'
)
go_term_object = iris_objects.IrisDataframe(data=result['GOENRICH'])
self.iris.add_to_env('go_terms' + query_name, go_term_object)
go_term_object_short = iris_objects.IrisDataframe(
data=result['GOENRICH_short'])
result_array.append(go_term_object_short)
result_array.append('No significant GO terms found')
# result_array.append("Full dataset saved as drug_disease_go_terms")
print('added GO terms')
# get tissue = disease
if 'tissue_df_dis' in result:
result_array.append(
'The most relevant tissues, in which disease genes are differentially expressed, are shown. Full dataset saved as tissues_{drug_disease} '
)
tissue_object_dis = iris_objects.IrisDataframe(
data=result['tissue_df_dis'])
tissue_object_dis_short = iris_objects.IrisDataframe(
data=result['tissue_df_dis_short'])
self.iris.add_to_env('tissues_disease' + query_name,
tissue_object_dis)
result_array.append(tissue_object_dis_short)
else:
result_array.append(
'No differential tissue expression in disease state detected.')
if "pubmed" in result:
if isinstance(result["pubmed"], str):
result_array.append(result["pubmed"])
else:
result_array.append(
"Following are PMIDs that support the interaction: Full dataset saved as pmid_{drug_disease}."
)
pmid_df_short = iris_objects.IrisDataframe(
data=result["pubmed_short"])
pmid_df = iris_objects.IrisDataframe(data=result["pubmed"])
self.iris.add_to_env('pmid' + query_name, pmid_df)
result_array.append(pmid_df_short)
# result_array.append("Full dataset saved as pmid_ids")
print('added Pubmed terms')
# get other possible disease
if "other_disease" in result:
ph_genes_str, drug = result["other_disease"]
ph_genes_arr = ph_genes_str.split('\t') # prb, BH, ph, sig_genes
ph_genes_array_all = [
ph_genes_arr[x:x + 4] for x in range(0, len(ph_genes_arr), 4)
]
if len(ph_genes_arr) >= 4:
# add explanation
multi_answer_line = [
'Top hits of diseases potentially impacted by %s. Full dataset saved as drug_indications_{drug_disease}.'
% result['drug'],
'We queried the gene neighborhood of drug targets and found the following phenotypes to be significant. Here we list significant phenotypes in order of probability. Column headings are phenotype, probability, significance level cutoff, and a list of genes that support the relationship'
]
result_array = result_array + multi_answer_line
ph_genes_array_all_iris = iris_objects.IrisDataframe(
column_names=[
"Phenotype", "probability",
"Benjamin Hochberg significance cutoff",
"list of genes"
],
column_types=["Text", "Text", "Text", "Text"],
data=ph_genes_array_all)
self.iris.add_to_env('drug_indications' + query_name,
ph_genes_array_all_iris)
ph_genes_array_short = [
ph_genes_arr[x:x + 4]
for x in range(0, min(5 * 4, len(ph_genes_arr)), 4)
]
ph_genes_array_short_iris = iris_objects.IrisDataframe(
column_names=[
"Phenotype", "Probability",
"Benjamin Hochberg significance cutoff",
"list of genes"
],
column_types=["Text", "Text", "Text", "Text"],
data=ph_genes_array_short)
result_array.append(ph_genes_array_short_iris)
else:
result_array.append('No other drug indications found')
# result_array.append("Full dataset saved as drug_indications")
print('added other drug indications')
# display image
if "image_file" in result:
result_array.append('Diagram stored in: %s' % result["image_file"])
os.system("open " + result["image_file"])
print('added image')
result_array.append(
"Full dataframes are available for viewing using the command: print {dataframe_name}. See right side panel for more information."
)
result_array.append(
"The suffix for the drug-disease interaction pair is: %s" %
query_name)
result_array.append("Results are also stored in: %s" % results_dir)
return result_array
class DrugDiseaseCSV(IrisCommand):
# what iris will call the command + how it will appear in a hint
title = "Can you find the mechanism of action of these drug-disease pairs from {file}"
# give an example for iris to recognize the command
examples = [
"multiple drugs and diseases from file",
"file of drug disease queries", "loaded drug disease match",
"find the mechanism of action of drug-disease pairs from {file} "
]
# type annotations for each command argument, to help Iris cosllect missing values from a user
argument_types = {
"file":
t.File(
"Enter your tab-delimited file containing drug-disease pairs of connections do you want to analyze. Drugs should be in the first column and conditions in the second column"
),
"bool_image":
t.YesNo(
"Would you like to save visual representations of the drug-disease combination?",
yes=True,
no=False),
"bool_pubmed":
t.YesNo(
"Would you like to get the list of pubmed IDs for reference for each query?",
yes=True,
no=False),
"bool_other_disease":
t.YesNo(
"Would you like to know other diseases that can be affected by the given drug?",
yes=True,
no=False),
"bool_display":
t.YesNo(
"Would you like to display results within the Iris GUI. Please only do so if you have < 10 comparisons.",
yes=True,
no=False)
}
# core logic of the command
def command(self, file, bool_image, bool_pubmed, bool_other_disease,
bool_display):
import pandas as pd
panda_df = pd.read_csv(file.path, sep='\t')
iris_df = iris_objects.IrisDataframe(data=panda_df)
# print('read in file')
self.iris.add_to_env('drug_disease_list', iris_df)
# generate options object
task_dir = os.path.join(
results_dir,
datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
options = Options(gen_image=bool_image,
gen_pubmed=bool_pubmed,
outPath=task_dir)
# get list of drugs and conditions
drug_list = list(panda_df.ix[:, 0])
disease_list = list(panda_df.ix[:, 1])
# store within the directory as a list
answer_arr = [task_dir]
for drug, disease in zip(drug_list, disease_list):
answer = Q2_query(drug, disease, options)
if isinstance(answer, str):
answer_str = answer
answer = {}
answer['error'] = answer_str
if bool_other_disease:
answer["other_disease"] = run_main.find_drug_indications(drug)
answer['drug'] = drug.strip()
answer['disease'] = disease.strip()
answer_arr.append(answer)
if bool_display:
return answer_arr
else:
return ' '.join(("written results to:", task_dir))
# wrap the output of a command to display to user
# by default this will be an identity function
# each element of the list defines a separate chat bubble
#### TO DO MAKE THIS A SUBCLASS OF DRUGDISEASEMULTI####
def explanation(self, results):
if isinstance(results, str):
return results
else:
task_dir = results.pop(0)
explanation_array = [
'Result tables for each query are stored in the right side bar as variables. You can view a table using the command: print {dataframe_name}_{suffix}.'
]
explanation_array.append(
'Diagrams (if requested) and other results can be found in the results directory: %s'
% task_dir)
explanation_array.append(
'Suffix and variable information is displayed below')
# iterate through every drug-disease pair
drug_arr = []
disease_arr = []
worked_arr = []
suffix_arr = []
assoc_variables = []
for result in results:
print('results', result['drug'], result['disease'])
drug_arr.append(result['drug'])
disease_arr.append(result['disease'])
if 'error' in result:
worked_arr.append(result['error'])
suffix_arr.append('')
assoc_variables.append('')
else:
worked_arr.append('SUCCESS')
# get suffix information
query_name = result[
'drug'][:min(len(result['drug']), 3)] + "_" + result[
'disease'][:min(len(result['disease']), 3)]
query_name = ''.join(query_name.split(' '))
query_name = "_" + query_name.lower()
suffix_arr.append(query_name)
# get associated drug genes
drug_gene_term_object = iris_objects.IrisDataframe(
data=result['drug_genes'])
self.iris.add_to_env('drug_genes' + query_name,
drug_gene_term_object)
# get genes associated with disease
disease_gene_term_object = iris_objects.IrisDataframe(
data=result['disease_genes'])
self.iris.add_to_env('disease_genes' + query_name,
disease_gene_term_object)
# get out signficant go terms
go_term_object = iris_objects.IrisDataframe(
data=result['GOENRICH'])
self.iris.add_to_env('go_terms' + query_name,
go_term_object)
variable_info = [
'drug_genes' + query_name,
'disease_genes' + query_name, 'go_terms' + query_name
]
# get tissue = disease
if 'tissue_df_dis' in result:
variable_info.append('tissues_disease' + query_name)
tissue_object_dis = iris_objects.IrisDataframe(
data=result['tissue_df_dis'])
self.iris.add_to_env('tissues_disease' + query_name,
tissue_object_dis)
if "pubmed" in result:
if not isinstance(result["pubmed"], str):
variable_info.append('pmid' + query_name)
pmid_df = iris_objects.IrisDataframe(
data=result["pubmed"])
self.iris.add_to_env('pmid' + query_name, pmid_df)
# get other possible disease
if "other_disease" in result:
ph_genes_str, drug = result["other_disease"]
ph_genes_arr = ph_genes_str.split(
'\t') # prb, BH, ph, sig_genes
if len(ph_genes_arr) >= 4:
ph_genes_array_all = [
ph_genes_arr[x:x + 4]
for x in range(0, len(ph_genes_arr), 4)
]
ph_genes_array_all_iris = iris_objects.IrisDataframe(
column_names=[
"Phenotype", "probability",
"Benjamin Hochberg significance cutoff",
"list of genes"
],
column_types=["Text", "Text", "Text", "Text"],
data=ph_genes_array_all)
self.iris.add_to_env(
'drug_indications' + query_name,
ph_genes_array_all_iris)
variable_info.append('drug_indications' +
query_name)
assoc_variables.append(', '.join(variable_info))
# Save info as an iris dataframe
info_data = [
list(x) for x in zip(drug_arr, disease_arr, worked_arr,
suffix_arr, assoc_variables)
]
info_df = iris_objects.IrisDataframe(
column_names=[
"Drug", "Disease", "Query Status", "Suffix",
"Associated Variables"
],
column_types=["Text", "Text", "Text", "Text", "Text"],
data=info_data)
explanation_array.append(info_df)
return explanation_array
class DrugDiseaseMulti(IrisCommand):
# what iris will call the command + how it will appear in a hint
title = "Can you find the mechanism of action of this list of drug-disease combinations?"
# give an example for iris to recognize the command
examples = [
"multiple drugs and diseases", "how list of drugs works",
"how multiple drug works on disease",
"how do these drugs affect these diseases",
"how does this list of {drug_list} affect {disease_list}"
]
# type annotations for each command argument, to help Iris cosllect missing values from a user
argument_types = {
"drug_list":
t.List(
"What drugs do you want to analyze? Please enter in drugs separated by commas."
),
"disease_list":
t.List(
"What diseases do you want to analyze? Every pairwise combination of drugs and diseases will be computed."
),
"bool_image":
t.YesNo(
"Would you like to save visual representations of the drug-disease combination?",
yes=True,
no=False),
"bool_pubmed":
t.YesNo(
"Would you like to get the list of pubmed IDs for reference for each query?",
yes=True,
no=False),
"bool_other_disease":
t.YesNo(
"Would you like to know other diseases that can be affected by the given drug?",
yes=True,
no=False)
}
# core logic of the command
def command(self, drug_list, disease_list, bool_image, bool_pubmed,
bool_other_disease):
# generate options object
options = Options(gen_image=bool_image, gen_pubmed=bool_pubmed)
# store the answers to each drug-disease combination as a list of dictionaries
answer_arr = []
for drug in drug_list:
for disease in disease_list:
answer = Q2_query(drug, disease, options)
if isinstance(answer, str):
answer_str = answer
answer = {}
answer['error'] = answer_str
if bool_other_disease:
answer["other_disease"] = run_main.find_drug_indications(
drug)
answer['drug'] = drug.strip()
answer['disease'] = disease.strip()
answer_arr.append(answer)
return answer_arr
# wrap the output of a command to display to user
# by default this will be an identity function
# each element of the list defines a separate chat bubble
def explanation(self, results):
explanation_array = [
'Result tables for each query are stored in the right side bar as variables. You can view a table using the command: print {dataframe_name}_{suffix}.'
]
explanation_array.append(
'Diagrams (if requested) and other results can be found in the results directory: %s'
% results_dir)
explanation_array.append(
'Suffix and variable information is displayed below')
# iterate through every drug-disease pair
drug_arr = []
disease_arr = []
worked_arr = []
suffix_arr = []
assoc_variables = []
for i, result in enumerate(results):
print('results', result['drug'], result['disease'])
drug_arr.append(result['drug'])
disease_arr.append(result['disease'])
if 'error' in result:
worked_arr.append(result['error'])
suffix_arr.append('')
assoc_variables.append('')
else:
worked_arr.append('SUCCESS')
# get suffix information
query_name = result['drug'][:min(
len(result['drug']), 3
)] + "_" + result['disease'][:min(len(result['disease']), 3)]
query_name = ''.join(query_name.split(' '))
query_name = "_" + query_name.lower() + "_" + str(i)
suffix_arr.append(query_name)
# get associated drug genes
drug_gene_term_object = iris_objects.IrisDataframe(
data=result['drug_genes'])
self.iris.add_to_env('drug_genes' + query_name,
drug_gene_term_object)
# get genes associated with disease
disease_gene_term_object = iris_objects.IrisDataframe(
data=result['disease_genes'])
self.iris.add_to_env('disease_genes' + query_name,
disease_gene_term_object)
# get out signficant go terms
go_term_object = iris_objects.IrisDataframe(
data=result['GOENRICH'])
self.iris.add_to_env('go_terms' + query_name, go_term_object)
variable_info = [
'drug_genes' + query_name, 'disease_genes' + query_name,
'go_terms' + query_name
]
# get tissue = disease
if 'tissue_df_dis' in result:
variable_info.append('tissues_disease' + query_name)
tissue_object_dis = iris_objects.IrisDataframe(
data=result['tissue_df_dis'])
self.iris.add_to_env('tissues_disease' + query_name,
tissue_object_dis)
if "pubmed" in result:
if not isinstance(result["pubmed"], str):
variable_info.append('pmid' + query_name)
pmid_df = iris_objects.IrisDataframe(
data=result["pubmed"])
self.iris.add_to_env('pmid' + query_name, pmid_df)
# get other possible disease
if "other_disease" in result:
ph_genes_str, drug = result["other_disease"]
ph_genes_arr = ph_genes_str.split(
'\t') # prb, BH, ph, sig_genes
if len(ph_genes_arr) >= 4:
ph_genes_array_all = [
ph_genes_arr[x:x + 4]
for x in range(0, len(ph_genes_arr), 4)
]
ph_genes_array_all_iris = iris_objects.IrisDataframe(
column_names=[
"Phenotype", "probability",
"Benjamin Hochberg significance cutoff",
"list of genes"
],
column_types=["Text", "Text", "Text", "Text"],
data=ph_genes_array_all)
self.iris.add_to_env('drug_indications' + query_name,
ph_genes_array_all_iris)
variable_info.append('drug_indications' + query_name)
assoc_variables.append(', '.join(variable_info))
# Save info as an iris dataframe
info_data = [
list(x) for x in zip(drug_arr, disease_arr, worked_arr, suffix_arr,
assoc_variables)
]
info_df = iris_objects.IrisDataframe(
column_names=[
"Drug", "Disease", "Query Status", "Suffix",
"Associated Variables"
],
column_types=["Text", "Text", "Text", "Text", "Text"],
data=info_data)
explanation_array.append(info_df)
return explanation_array