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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import synapseclient\n", | ||
"\n", | ||
"import great_expectations as gx\n", | ||
"\n", | ||
"context = gx.get_context(project_root_dir='../src/agoradatatools/great_expectations')\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Create Expectation Suite for RNA Distribution Data" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Get Example Data File" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"syn = synapseclient.Synapse()\n", | ||
"syn.login()\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"rna_distribution_data_file = syn.get(\"syn28094691\").path\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Create Validator Object on Data File" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"validator = context.sources.pandas_default.read_json(\n", | ||
" rna_distribution_data_file\n", | ||
")\n", | ||
"validator.expectation_suite_name = \"rna_distribution_data\"\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Add Expectations to Validator Object For Each Column" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# model\n", | ||
"validator.expect_column_values_to_be_of_type(\"model\", \"str\")\n", | ||
"validator.expect_column_values_to_not_be_null(\"model\")\n", | ||
"validator.expect_column_values_to_be_in_set(\"model\", [\"AD Diagnosis (males and females)\", \"AD Diagnosis x AOD (males and females)\",\"AD Diagnosis x Sex (females only)\", \"AD Diagnosis x Sex (males only)\"])" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# tissue\n", | ||
"validator.expect_column_values_to_be_of_type(\"tissue\", \"str\")\n", | ||
"validator.expect_column_values_to_not_be_null(\"tissue\")\n", | ||
"validator.expect_column_values_to_be_in_set(\"tissue\", [\"CBE\", \"DLPFC\", \"FP\", \"IFG\", \"PHG\", \"STG\", \"TCX\", \"ACC\", \"PCC\"])" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# min\n", | ||
"validator.expect_column_values_to_be_of_type(\"min\", \"float\")\n", | ||
"validator.expect_column_values_to_not_be_null(\"min\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# max\n", | ||
"validator.expect_column_values_to_be_of_type(\"max\", \"float\")\n", | ||
"validator.expect_column_values_to_not_be_null(\"max\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# median\n", | ||
"validator.expect_column_values_to_be_of_type(\"median\", \"float\")\n", | ||
"validator.expect_column_values_to_not_be_null(\"median\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# first_quartile\n", | ||
"validator.expect_column_values_to_be_of_type(\"first_quartile\", \"float\")\n", | ||
"validator.expect_column_values_to_not_be_null(\"first_quartile\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# third_quartile\n", | ||
"validator.expect_column_values_to_be_of_type(\"third_quartile\", \"float\")\n", | ||
"validator.expect_column_values_to_not_be_null(\"third_quartile\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# multi-field\n", | ||
"validator.expect_column_pair_values_a_to_be_greater_than_b(\"max\", \"third_quartile\")\n", | ||
"validator.expect_column_pair_values_a_to_be_greater_than_b(\"third_quartile\", \"median\")\n", | ||
"validator.expect_column_pair_values_a_to_be_greater_than_b(\"median\", \"first_quartile\")\n", | ||
"validator.expect_column_pair_values_a_to_be_greater_than_b(\"first_quartile\", \"min\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Save Expectation Suite" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"validator.save_expectation_suite(discard_failed_expectations=False)\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Create Checkpoint and View Results" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"checkpoint = context.add_or_update_checkpoint(\n", | ||
" name=\"agora-test-checkpoint\",\n", | ||
" validator=validator,\n", | ||
")\n", | ||
"checkpoint_result = checkpoint.run()\n", | ||
"context.view_validation_result(checkpoint_result)\n" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"## Build Data Docs - Click on Expectation Suite to View All Expectations" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"context.build_data_docs()\n", | ||
"context.open_data_docs()\n" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.9.13" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |
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"""Function for transforming proteomics data. This function is called on all three proteomics | ||
data sets, although currently it only affects the LFQ data set as it is the only one with "CON__" | ||
entries. | ||
""" | ||
|
||
import pandas as pd | ||
|
||
|
||
def transform_proteomics(df: pd.DataFrame) -> pd.DataFrame: | ||
"""Filters out rows that have "CON__" in their uniqid. This label indicates that the protein | ||
is a known contaminant and should be removed from the final data set. Rows with an NA uniqid | ||
are also removed. | ||
Args: | ||
df (pd.DataFrame]): pandas DataFrame containing proteomics data. Must contain a column | ||
called "uniqid". | ||
Returns: | ||
pd.DataFrame: a DataFrame that is identical to the input DataFrame but with rows containing | ||
"CON__" in the uniqid removed. | ||
""" | ||
# Using "na=True" causes rows with NA uniqids to be set to True so they get removed | ||
remove_rows = df["uniqid"].str.contains("CON__", na=True) | ||
df = df.drop(df.index[remove_rows]) | ||
return df |
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