Phenofhy

Quickstart ​

Phenofhy helps you extract, process, and summarize OFH phenotype data inside the TRE.

Prerequisites ​

  • You are running inside the OFH TRE with the dx toolkit available.
  • config.json is available in /mnt/project/helpers (Phenofhy uses it to resolve files).
  • If you have not uploaded Phenofhy to the TRE yet, see the Installation page.

Download Phenofhy into your JupyterLab session ​

Before running the workflow, download the Phenofhy package into your JupyterLab Spark instance using dx. Replace project_name with your TRE project name:

python
!dx download "project_name:/applets/phenofhy/" -r

This assumes Phenofhy was uploaded under the applets folder and contains the beta source code (i.e., Python modules).

Minimal workflow ​

python
from phenofhy import extract, process, calculate, profile, utils

# 1) Extract a small set of fields
extract.fields(
    output_file="outputs/raw/phenos.csv",
    fields=[
        "participant.registration_year",
        "participant.registration_month",
        "participant.birth_year",
        "participant.birth_month",
        "participant.demog_sex_2_1",
        "questionnaire.smoke_status_2_1",
    ],
)

# 2) Process participant data (derives age, sex, age_group)
df = process.participant_fields("outputs/raw/phenos.csv")

# 3) Summaries
summary = calculate.summary(
    df,
    traits=["derived.age_at_registration", "derived.sex"],
    stratify="derived.sex",
)

# 4) Profile report
report = profile.phenotype_profile(
    df,
    phenotype="derived.age_at_registration",
    output="outputs/reports/age_profile.pdf",
)

# 5) Upload your results
report = utils.upload_files(
   files="outputs/reports/age_profile.pdf",
   dx_target="results"
)

Next steps ​

  • Use calculate.prevalence() for prevalence tables.
  • Use icd.match_icd_traits() to match ICD codes to traits.
  • Use process.questionnaire_fields() or process.clinic_measurements_fields() to work by entity.