PheTK 0.1.27

PheTK - Phenotype Toolkit

PheTK - The Phenotype Toolkit

The official repository of PheTK.

1. INSTALLATION

PheTK can be installed using pip install command in a terminal (Python 3.7 or newer):

pip install PheTK

or Jupyter Notebook (restart kernel after installation and prior to importing):

!pip install PheTK

2. SYSTEM REQUIREMENTS

As PheTK utilizes multithreading to speed up computational processes, the more CPUs/cores the system has, the faster it would run. Memory requirement would vary based on user actual workflow and how efficient it is.

In our test, a PheWAS for a cohort of 400,000+ participants and 12 covariates successfully completed on a MacBook Pro M2 Pro 12 CPU cores 32GB RAM in ; PheWAS for the same cohort took ~12 minutes with a cloud instance of 96 CPUs 370GB RAM (memory choice here is mainly for redundancy and for comparison with other tests that we did).

CPU/RAM configurations and pipeline runtime also vary by computing platforms and operating systems.

In short, the more CPUs the better and RAM should be adequate for user workflow.

3. 1-MINUTE PHEWAS DEMO

User can run the quick 1-minute PheWAS demo with the following command in a terminal:

python3 -m PheTK.Demo

Or in Jupyter Notebook:

from PheTK import Demo

Demo.run()

4. DESCRIPTIONS

PheTK is a fast and efficient python library for Phenome Wide Association Studies (PheWAS) and other analyses utilizing both phecode 1.2 and phecodeX 1.0.

4.1. PheWAS workflow and PheTK modules

Standard PheWAS workflow. Green texts are PheTK module names. Black and gray components are steps supported or not supported by PheTK currently.

4.2. PheTK module descriptions

This table will be updated as we update PheTK.

Module	Class	Method(s)	Platform	Requirements/Notes
Cohort	Cohort	by_genotype	All of Us	None
			Other	Variant data stored in Hail matrix table
		add_covariate	All of Us	None
			Other	Google BigQuery OMOP database
				Required tables: person, condition_occurrence, observation, death, & concept
Phecode	Phecode	count_phecode	All of Us	None
			Other	User provided cohort ICD code data
				User can use custom ICD-to-phecode mapping table.
PheWAS	PheWAS	all methods	Any	None
Plot	Plot	all methods	Any	None
Demo		all methods	Any	None

All of Us: the All of Us Research Program (https://allofus.nih.gov/)

5. USAGE

5.1. Cohort module

Cohort module can be used for generating genetic cohort and add certain covariates to a cohort.

5.1.1. by_genotype

This function takes genetic variant information as input, and generates cohort with matching genotypes as an output csv file.

For example, we generate cohort for CFTR variant chr7-117559590-ATCT-A with heterozygous (0/1 genotype) participants as cases and homozygous reference (0/0 genotype) participants as controls.

Jupyter Notebook example for All of US Researcher Workbench:

For All of Us data version 7, the default Hail matrix table is the ACAF (common variant) table. User can use a different table by providing table location in the mt_path parameter.

from PheTK.Cohort import Cohort

# instantiate class Cohort object for All of Us database version 7
cohort = Cohort(platform="aou", aou_db_version=7)

# generate cohort by genotype
cohort.by_genotype(
    chromosome_number=7,
    genomic_position=117559590,
    ref_allele="ATCT",
    alt_allele="A",
    case_gt="0/1",
    control_gt="0/0",
    reference_genome="GRCh38",
    mt_path=None,
    output_file_name="cftr_cohort.csv"
)

Jupyter Notebook example for other platforms:

For other platforms, user need to provide the location of Hail matrix table file for mt_path parameter.

from PheTK.Cohort import Cohort

# instantiate class Cohort object for All of Us database version 7
cohort = Cohort(platform="custom")

# generate cohort by genotype
cohort.by_genotype(
    chromosome_number=7,
    genomic_position=117559590,
    ref_allele="ATCT",
    alt_allele="A",
    case_gt="0/1",
    control_gt="0/0",
    reference_genome="GRCh38",
    mt_path="/path/to/hail_matrix_table.mt",
    output_file_name="cftr_cohort.csv"
)

5.1.2. add_covariates

This function is currently customized for the All of Us Research Platform. t takes a cohort csv file and covariate selection as input, and generate a new cohort csv file with covariate data added as output. Input cohort data which must have at least "person_id" column.

In this example, we are adding age at last diagnosis event, sex at birth and 10 genetic PCs (provided by All of Us). These options were set to True (or 10 in case of first_n_pcs).

The covariates shown in this example are currently supported by PheTK. Users should only change parameter value to True for covariates to be used in subsequent PheWAS. All parameters are set to False by default, i.e., user only need to specify parameters of interest.

Jupyter Notebook example for All of US Researcher Workbench:

# user can skip the import and instantiation steps if running continuously 
# from previous by_genotype example, i.e., skip directly to add covariates step.
from PheTK.Cohort import Cohort

# instantiate class Cohort object for All of Us database version 7
cohort = Cohort(platform="aou", aou_db_version=7)

# RUN EITHER LONG OR SHORT VERSION BELOW
# add covariates - long version, including all currently supported covariate options
cohort.add_covariates(
    cohort_csv_path="aou_chr7_117559590_ATCT_A.csv",
    natural_age=False,
    age_at_last_event=True,
    sex_at_birth=True,
    ehr_length=False,
    dx_code_occurrence_count=False,
    dx_condition_count=False,
    genetic_ancestry=False,
    first_n_pcs=10,
    drop_nulls=True,
    output_file_name="cohort_with_covariates.csv"
)

# add covariates - short version, i.e., users do not need to list unused covariates
cohort.add_covariates(
    cohort_csv_path="aou_chr7_117559590_ATCT_A.csv",
    age_at_last_event=True,
    sex_at_birth=True,
    first_n_pcs=10,
    drop_nulls=True,
    output_file_name="cohort_with_covariates.csv"
)

5.2. Phecode module

Phecode module is used to retrieve ICD code data of participants, map ICD codes to phecode 1.2 or phecodeX 1.0, and aggregate the counts for each phecode of each participant.

The ICD code retrieval is done automatically for All of Us platform when users instantiate class Phecode. For other platforms, users must provide your own ICD code data.

Example of ICD code data:

• Each row must be unique, i.e., there should not be 2 instances of 1 ICD codes in the same day.
• Data must have these exact column names. Date column is not used for later ICD-to-phecode mapping, but should be included to make sure ICD events occurred on unique dates.

person_id	date	vocabulary_id	ICD
13579	1-11-2010	ICD9CM	786.2
13579	1-31-2010	ICD9CM	786.2
13579	12-4-2017	ICD10CM	R05.1
24680	3-12-2012	ICD9CM	659.2
24680	4-18-2018	ICD10CM	R50

Jupyter Notebook example for All of Us:

from PheTK.Phecode import Phecode

phecode = Phecode(platform="aou")
phecode.count_phecode(
    phecode_version="X", 
    icd_version="US",
    phecode_map_file_path=None, 
    output_file_name="my_phecode_counts.csv"
)

Jupyter Notebook example for other platforms

from PheTK.Phecode import Phecode

phecode = Phecode(platform="custom", icd_file_path="/path/to/my_icd_data.csv")
phecode.count_phecode(
    phecode_version="X", 
    icd_version="US", 
    phecode_map_file_path=None,
    output_file_name="my_phecode_counts.csv"
)

In both examples, users can provide their own phecode mapping file by adding a csv file path to phecode_map_file_path.

5.3. PheWAS module

It is recommended to run Demo example above and have a look at example cohort and phecode counts file to be familiar with input data format. The example files should be generated in user's current working directory.

PheWAS class is instantiated with paths to csv files of cohort data and phecode counts data, in addition to other parameters as shown in the examples below. It can be used in both Linux command line interface (CLI) and any Python environment, e.g., Jupyter Notebook/Lab.

CLI example:

python3 -m PheTK.PheWAS \
--phecode_version X \
--cohort_csv_path example_cohort.csv \
--phecode_count_csv_path example_phecode_counts.csv \
--sex_at_birth_col sex \
--covariates age sex pc1 pc2 pc3 
--independent_variable_of_interest independent_variable_of_interest \
--min_case 50 \
--min_phecode_count 2 \
--output_file_name example_phewas_results.csv

Jupyter Notebook example:

from PheTK.PheWAS import PheWAS

example_phewas = PheWAS(
    phecode_version="X",
    phecode_count_csv_path="example_phecode_counts.csv",
    cohort_csv_path="example_cohort.csv",
    sex_at_birth_col="sex",
    covariate_cols=["age", "sex", "pc1", "pc2", "pc3"],
    independent_variable_of_interest="independent_variable_of_interest",
    min_cases=50,
    min_phecode_count=2,
    output_file_name="example_phewas_results.csv"
)
example_phewas.run()

5.4. Plot module

Plot class is instantiated with path to PheWAS result csv file. After that, a plot type method can be called to generate a plot, e.g., calling manhattan() method to make Manhattan plot.

Jupyter Notebook example:

from PheTK.Plot import Plot

p = Plot("example_phewas_results.csv")
p.manhattpan(label_values="p_value", label_count=1, save_plot=True)

The above code example generates this Manhattan plot figure:

6. CONTACT

[e-mail]

7. CITATION

TBD