geneticScreening.py

import streamlit as st
import pandas as pd
import requests
from st_aggrid import AgGrid

st.set_page_config(
    page_title="Genetic Screening",
    page_icon="random",
    layout="wide",
    initial_sidebar_state="collapsed",
    # menu_items={
    #     'Get Help': 'url',
    #     'Report a bug': "url",
    #     'About': "# This is a header. This is an *extremely* cool app!"
    # }
)


@st.cache
def findPopulationDxImplications(conditionCode):
    url = 'https://fhir-gen-ops.herokuapp.com/population-operations/phenotype-operations/$find-population-dx-implications?conditions='+conditionCode+'&includePatientList=true'
    headers = {'Accept': 'application/json'}
    r = requests.get(url, headers=headers)
    return r.json()


@st.cache
def findSubjectDxImplications(subject, conditionCode):
    url = 'https://fhir-gen-ops.herokuapp.com/subject-operations/phenotype-operations/$find-subject-dx-implications?subject='+subject+'&conditions='+conditionCode
    headers = {'Accept': 'application/json'}
    r = requests.get(url, headers=headers)
    return r.json()


resultList = []
patientList = []
conditionList = []
clinSigList = []
evidenceList = []
variantList = []

st.title("Genetic Screening")
st.markdown("This app illustrates [FHIR Genomics Operations](http://build.fhir.org/ig/HL7/genomics-reporting/operations.html) **find-population-dx-implications** and **find-subject-dx-implications**. Select a condition from dropdown. The population is screened for this condition, and each associated variant in each patient is returned.")

condition = st.selectbox("", (
    'Hemochromatosis',
    'Familial hypercholesterolemia',
    'Hereditary Breast and Ovarian Cancer Syndrome',
    'Hereditary Paraganglioma-Pheochromocytoma Syndrome',
    'Hypertrophic cardiomyopathy',
    'Lynch syndrome',
    'Multiple endocrine neoplasia',
    'Peutz-Jeghers syndrome'))

if condition == "Familial hypercholesterolemia":
    conditionCode = "https://www.ncbi.nlm.nih.gov/medgen|C0020445"
elif condition == "Hereditary Breast and Ovarian Cancer Syndrome":
    conditionCode = "https://www.ncbi.nlm.nih.gov/medgen|C0677776"
elif condition == "Hereditary Paraganglioma-Pheochromocytoma Syndrome":
    conditionCode = "https://www.ncbi.nlm.nih.gov/medgen|C1708353"
elif condition == "Hypertrophic cardiomyopathy":
    conditionCode = "https://www.ncbi.nlm.nih.gov/medgen|C0878544"
elif condition == "Lynch syndrome":
    conditionCode = "https://www.ncbi.nlm.nih.gov/medgen|C4552100"
elif condition == "Multiple endocrine neoplasia":
    conditionCode = "https://www.ncbi.nlm.nih.gov/medgen|C0027662"
elif condition == "Peutz-Jeghers syndrome":
    conditionCode = "https://www.ncbi.nlm.nih.gov/medgen|C0031269"
elif condition == "Hemochromatosis":
    conditionCode = "https://www.ncbi.nlm.nih.gov/medgen|C3469186"

for i in findPopulationDxImplications(conditionCode)["parameter"][0]["part"]:
    if i["name"] == "subject":
        resultList.append(i["valueString"])

for i in resultList:
    patient = i
    variants = []
    dxImplications = findSubjectDxImplications(i, conditionCode)

    for j in dxImplications["parameter"]:
        if j["name"] == "variant":
            # extract variant
            for l in j["resource"]["component"]:
                if l["code"]["coding"][0]["code"] == "81252-9":
                    variant = l["valueCodeableConcept"]["coding"][0]["display"]
                    if variant in variants:
                        variant = ""
                    else:
                        variants.append(variant)
        elif j["name"] == "implication":
            # extract condition, clinical significance, evidence
            for l in j["resource"]["component"]:
                if l["code"]["coding"][0]["code"] == "53037-8":
                    try:
                        clinSig = l["valueCodeableConcept"]["coding"][0]["display"]
                    except KeyError:
                        clinSig = l["valueCodeableConcept"]["text"]
                elif l["code"]["coding"][0]["code"] == "81259-4":
                    condition = l["valueCodeableConcept"]["coding"][0]["display"]
                elif l["code"]["coding"][0]["code"] == "93044-6":
                    evidence = l["valueCodeableConcept"]["text"]

    if variant != "":
        patientList.append(patient)
        variantList.append(variant)
        clinSigList.append(clinSig)
        conditionList.append(condition)
        evidenceList.append(evidence)


data = (pd.DataFrame({
    'Patient': patientList,
    'Condition': conditionList,
    'Variant': variantList,
    'Clinical Significance': clinSigList,
    'Evidence': evidenceList}))
data_t = data.T

AgGrid(data, enable_enterprise_modules=True, update_mode="value_changed", allow_unsafe_jscode=True)

st.download_button("Download table", data_t.to_json())