class GotermEnrichment(IrisCommand):
title = "compute GO term enrichment on {gene_list}"
examples = [ "compute GO term enrichment on a list of gene symbols" , "describe the function of a list of genes"]
help_text = [
"Gene Ontology (GO) Consortium contains functional and structure annotations for genes. Given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented using annotations for a list of genes. \nCurrenlty, Iris takes in Gene Symbols (e.g. CTSK) and corrects p-values with FDR"
]
argument_types = {
"gene_list": t.List("What is the list of gene symbols (e.g. CTSK) you want to analyze?"),
"n_top": t.Int("How many top results you want to see?")
}
argument_help = {
"gene_list": "A list of gene symbols of interest",
"n_top": "Number of the top enrichment GO terms you want to see",
}
def command(self, gene_list, n_top):
from gprofiler import GProfiler
import numpy as np
gp = GProfiler("")
r0 = gp.gprofile(gene_list,correction_method=GProfiler.THR_FDR,ordered=True)
r0 = np.array(r0)
r0 = r0[r0[:,9]=='MF']
name_out = r0[0:n_top,-3]
p_out = r0[0:n_top,2]
data = np.array([x for x in zip(name_out, p_out)])
dataframe = pd.DataFrame(data=data, columns=["Name", "p-values"])
return dataframe
def explanation(self, results):
iris_df = iris_objects.IrisDataframe(data=results)
return [ "These are the enriched molecular function GO terms with corrected p-values", iris_df]
class ChanceDisease(IrisCommand):
title = "What are the chances that a person with conditions gets disease?"
examples = [
"What is the epidemiological risk {conditions} for {disease} and what are their effects on prognosis?",
"What is my likelihood of getting {disease}",
"How does {conditions} change my chances of getting {disease}?"
]
argument_types = {
"conditions":
t.List(
"What conditions (i.e. age, gender, comorbidities, etc.) do you want to factor into this calculation? Please enter them in separated by commas."
),
"disease":
t.String("What disease are you analyzing?")
}
def command(self, conditions, disease):
return "Currently not implemented:( But we'll get on it as soon as possible."
def explanation(self, result):
return result
class RiskFactorsToDisease(IrisCommand):
title = "What are the risks (environmental, genetic, etc.) that can lead to disease and why?"
examples = [
"Why is this risk bad for {disease}",
"Why are these risks bad for {disease}", "Why should I not do {risks}",
"Why should I stopping doing {risks}",
"What are the {risks} (environmental, genetic, etc.) that can lead to {disease} and why?"
]
argument_types = {
"risks":
t.List(
"What risks do you want to know more about? Enter them in separated by commas"
),
"disease":
t.String("What disease are you analyzing?")
}
def command(self, risks, disease):
return "Currently not implemented, but that sure is an important question."
def explanation(self, result):
return result
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 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