{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Comparing_protein_expression_from_different_pipelines-CPTAC.ipynb", "provenance": [], "authorship_tag": "ABX9TyNJd7XL60y8Ii62smyvibJM", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "kctcxZRlLi-x" }, "source": [ "# Comparing protein expression from different pipelines - CPTAC" ] }, { "cell_type": "markdown", "metadata": { "id": "dKN8ffL8MQXX" }, "source": [ "Check out more notebooks at our [Community Notebooks Repository](https://github.com/isb-cgc/Community-Notebooks)!\n", "\n", "```\n", "Title: Comparing protein expression from different pipelines \n", "Author: Boris Aguilar\n", "Created: 05-23-2021\n", "Purpose: Compare proteomic expression from PDC and other pipelines available in the cptac library (https://github.com/PayneLab/cptac)\n", "Notes: Runs in Google Colab \n", "```\n", "This notebook uses BigQuery to compare protein expression from the PDC and other pipelines. We used the [cptac library](https://github.com/PayneLab/cptac) to obtain protein expression derived from pipelines different than the one used by PDC.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Vlzjia2fc8k5" }, "source": [ "# Modules" ] }, { "cell_type": "code", "metadata": { "id": "i7INcldaoBgy" }, "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from google.cloud import bigquery\n", "from google.colab import auth\n", "import pandas_gbq" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8rXR-6LjdKOT" }, "source": [ "## Google Authentication\n", "The first step is to authorize access to BigQuery and the Google Cloud. For more information see ['Quick Start Guide to ISB-CGC'](https://isb-cancer-genomics-cloud.readthedocs.io/en/latest/sections/HowToGetStartedonISB-CGC.html) and alternative authentication methods can be found [here](https://googleapis.dev/python/google-api-core/latest/auth.html).\n", "\n", "Moreover you need to [create a google cloud](https://cloud.google.com/resource-manager/docs/creating-managing-projects#console) project to be able to run BigQuery queries." ] }, { "cell_type": "code", "metadata": { "id": "bo5UshXE47hv" }, "source": [ "auth.authenticate_user()\n", "my_project_id = \"\" # write your project id here\n", "bqclient = bigquery.Client( my_project_id )" ], "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8r7FzN1DgQp9" }, "source": [ "## Install cptac library" ] }, { "cell_type": "code", "metadata": { "id": "_DjhcMZwoMob", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7b6ec8e2-62e8-4ad3-efa3-7b018e88a22e" }, "source": [ "try:\n", " import cptac\n", "except ImportError:\n", " !pip install cptac --quiet\n", " import cptac\n", "import cptac.utils as ut" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", " import pandas.util.testing as tm\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "ZZDqAc3Cge4w" }, "source": [ "Use the cptac library to download proteomic data of Lung adenocarcinoma (LUAD) and save it into a pandas dataframe." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 501 }, "id": "FFLndGR_oNgh", "outputId": "d7b12610-53b9-4951-eed5-0ab5d281112b" }, "source": [ "cptac.download(dataset=\"Luad\", version=\"latest\")\n", "ov = cptac.Luad()\n", "df = ov.get_proteomics( )\n", "df" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "text": [ "" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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C3L-00080-1.9422-2.37820.19400.1920-2.2655NaN-1.66260.2149-0.75930.6113-0.0980-0.42970.47570.9284-0.10430.29841.1558-1.23500.9513-0.8448-1.7002-4.38920.7844-1.7607-1.7252NaN-2.7975-1.0764-0.7239NaN-1.42901.2142-0.3963-1.2350NaNNaN0.4590-0.9429-0.73430.0751...NaN0.19201.86500.1878NaNNaN-0.2586-2.4345-0.8177-0.4339-1.10150.7302-1.77941.0827NaN-0.30871.21420.40470.5570NaN1.02640.8658-0.2440-0.05420.0522NaN-0.5027-1.5020NaN2.2405NaNNaN-0.27950.6613NaN0.9659-0.3442-1.64801.2872-0.7301
C3L-000832.16363.1227-0.3044-1.7183-3.2851-1.82163.6147-0.4863-1.2387-0.4946-0.00680.3281-1.44130.37770.0594-1.6149-0.8873-2.6815-2.15650.7250-1.6397-0.19690.8986-0.8625NaN-0.7757-3.94650.5141-2.0862NaN-0.2837-2.5906-1.3421-0.5897-0.6186NaN-2.7270-1.7224-1.8092NaN...NaNNaN0.2124-1.7886NaN0.6671-1.86301.6055-0.0150-2.3425NaN-0.1142NaN-0.8625NaN-0.1514-0.0894-0.2755-0.68890.2620-0.46560.5886-0.11840.70020.1049-0.5111-1.09400.1379-1.4992-1.0072-3.0742-1.6769-0.5897-0.8129NaN0.9399-0.24650.31570.6547NaN
C3L-00093-1.0022-0.96320.81900.2556-11.1252NaN-0.16960.2911-0.4459-0.15180.36900.5533-0.59121.63400.1564-0.50970.7942NaN-0.9349-0.55580.5639-2.22111.1521-7.0045-2.75612.5835-3.5888-0.2900-1.3069-1.8668-0.4459-0.2865-0.5841-0.1199-3.02540.38321.2867-1.7888-2.9333-0.3609...NaN0.4434-0.9774-2.0085NaN-1.3176NaN-4.9494-2.74192.2363NaN0.5143NaNNaN4.8406-0.02430.2982-0.99160.0324NaN0.0076-0.09162.43830.40800.1139-1.31761.2655-1.4522NaNNaNNaNNaN0.6950-0.16251.8536-2.29900.4293-0.5876-0.4991-0.3077
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C3N-02582.N1.82773.62040.1783-1.68420.6852NaN1.5338-0.66662.3787-0.0458-0.2589-1.0964-0.6556-0.2442-0.1964-0.6336-1.0083NaN-0.66301.48973.52484.49830.06440.54203.1648NaN4.74441.5375-0.3581NaN1.4493-0.9054-0.5013-0.07150.2407NaN-3.5173NaN0.2407-1.1516...0.0203NaN-1.5005-0.7328NaNNaN0.5640NaN-1.44181.1554NaN0.21870.36202.4448NaN-0.33240.9313NaN0.5199-0.2074-1.10751.0048-0.8613-0.4572-0.5564-0.6924-3.32261.6293NaN0.9460-0.2001NaN-0.0826-1.6769-0.0017-0.12660.29952.39340.77700.9497
C3N-02586.N0.80351.64030.2300-1.88371.4085NaN1.3378-0.85440.1946-0.0726-0.3908-0.9801-0.3044-0.4615-0.40650.4460-1.2983NaN-1.22370.55211.00000.4421-0.96442.30042.72070.54821.61281.8368-0.21790.0924-0.5519-0.5951-0.8151-0.09220.1632-0.4144-2.2216-0.0726-0.0136-0.2651...1.7032-0.4458-1.4555-0.1079NaN-1.48690.4578-1.68720.79960.73280.47750.08072.38291.06283.3140-2.7834-0.9722NaN-0.17081.5578-0.8544-1.1372-0.5911-0.06080.0924-0.2336-1.19220.9332NaNNaN-0.82290.1750-0.0804-1.6401NaN2.40251.21611.64431.18861.1807
C3N-02587.N1.76372.2513-0.0532-1.41594.82640.81510.4511-0.81812.6187-0.33040.1037-1.1587-0.6043-0.1134-0.4707-0.7012-0.46402.0142-1.86680.42101.9540NaN0.21402.54521.15251.41633.19982.0743-0.3605NaN0.1605-0.9951-0.2369-0.64772.0509NaN-2.1507-1.59290.1605-0.2336...NaNNaN-0.1735-1.71990.1338-0.2703-1.7599NaNNaN1.42300.88860.27072.47512.3348-0.1668-0.96840.8252-0.7680-0.6143-0.6644-0.0866-2.6383-1.1988-0.29040.18061.2560NaN1.8572-13.41960.0670-0.1301NaN-0.0800-2.4146-2.8354NaN1.28612.12440.70831.1825
C3N-02588.N1.08751.7414-0.2270-1.70004.51530.4875NaN-0.21690.5044-0.3012-0.1225-1.08310.4707-0.7191-0.35850.1033-2.3201NaN-1.22810.30551.73130.1269-0.12932.74922.4492NaN4.02324.97030.1370-0.0686-0.5203-0.35850.2752-0.34500.4066NaN-1.7876NaN0.23470.1673...0.6763NaN-0.4731-0.5169NaNNaN0.08310.8718NaN0.41680.2448-0.28770.16060.2887-0.1832-0.6854-1.3730-0.7292-0.9854NaN-1.5955-1.4202-0.1192-0.81350.2179-1.0933NaN0.9628-7.3995-1.3157-0.9652-0.1293-0.4764-1.4775-2.29992.10540.49431.54590.63581.2729
C3N-02729.N2.60113.0462-0.2924-2.19534.14051.2990NaN-0.47412.0892-0.5594-0.5335-0.8970-0.5706-1.7502-0.40740.2900-2.30291.8295-1.03060.57573.43202.6715-1.27911.85172.15960.15280.95030.36050.2010NaN-0.4853-0.6077-0.7783-0.64111.9371NaN-4.89960.89470.5534-1.2309...NaNNaN-0.2108-1.1233NaN1.4511-0.69672.9423-0.47410.4978NaN0.3902NaN2.49722.3191NaNNaN-1.2197-0.5187-2.2547-0.7301-1.8318-1.0306-0.2330-0.5372-1.2606-0.01421.3064NaNNaNNaN3.2948-0.7338NaN-1.4238-2.07660.32341.65880.62020.8390
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211 rows × 10699 columns

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" ], "text/plain": [ "Name A1BG ... ZZZ3\n", "Database_ID NP_570602.2 ... NP_056349.1|NP_001295166.1\n", "Patient_ID ... \n", "C3L-00001 -2.5347 ... -2.5284\n", "C3L-00009 -0.5627 ... 0.4311\n", "C3L-00080 -1.9422 ... -0.7301\n", "C3L-00083 2.1636 ... NaN\n", "C3L-00093 -1.0022 ... -0.3077\n", "... ... ... ...\n", "C3N-02582.N 1.8277 ... 0.9497\n", "C3N-02586.N 0.8035 ... 1.1807\n", "C3N-02587.N 1.7637 ... 1.1825\n", "C3N-02588.N 1.0875 ... 1.2729\n", "C3N-02729.N 2.6011 ... 0.8390\n", "\n", "[211 rows x 10699 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 4 } ] }, { "cell_type": "markdown", "metadata": { "id": "1wQlAAsDR0TF" }, "source": [ "## From dataframe to BigQuery table\n", "The following commands transform the dataframe to a tidy format and save it into a BigQuery table in your project." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "hDNIJdt5yVg5", "outputId": "96733ae5-562d-4024-d632-50e05a3dba3b" }, "source": [ "tdf = pd.melt(df, var_name=\"gene_name\", value_name=\"protein_abundance\",ignore_index = False)\n", "tdf.reset_index(inplace=True)\n", "tdf[0:10]" ], "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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Patient_IDgene_nameprotein_abundance
0C3L-00001A1BG-2.5347
1C3L-00009A1BG-0.5627
2C3L-00080A1BG-1.9422
3C3L-00083A1BG2.1636
4C3L-00093A1BG-1.0022
5C3L-00094A1BG-1.5576
6C3L-00095A1BG-1.0718
7C3L-00140A1BG-1.0799
8C3L-00144A1BG-1.9159
9C3L-00263A1BG-1.1384
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" ], "text/plain": [ " Patient_ID gene_name protein_abundance\n", "0 C3L-00001 A1BG -2.5347\n", "1 C3L-00009 A1BG -0.5627\n", "2 C3L-00080 A1BG -1.9422\n", "3 C3L-00083 A1BG 2.1636\n", "4 C3L-00093 A1BG -1.0022\n", "5 C3L-00094 A1BG -1.5576\n", "6 C3L-00095 A1BG -1.0718\n", "7 C3L-00140 A1BG -1.0799\n", "8 C3L-00144 A1BG -1.9159\n", "9 C3L-00263 A1BG -1.1384" ] }, "metadata": { "tags": [] }, "execution_count": 5 } ] }, { "cell_type": "markdown", "metadata": { "id": "UMsjYZiC9478" }, "source": [ "The following commands send the protein expression data to a BigQuery table." ] }, { "cell_type": "code", "metadata": { "id": "YoB-E_lt5Y5t" }, "source": [ "table_id = 'test_dataset2.luad_cptac_paynelab' # test_dataset2 is dataset and luad_cptac_paynelab is the table name\n", "pandas_gbq.to_gbq(tdf, table_id, project_id=my_project_id)" ], "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "fY14AaAdH5iu" }, "source": [ "## Compute Pearson correlations in BigQuery\n", "Here we compare protein expressions from the cptac library with those generated from PDC proteomics data. The comparison is made by computing Pearson correlation. \n", "\n", "The first step is to build a query to retrieve PDC based protein expressions, which are available in BigQuery tables in the public project isb-cgc-bq." ] }, { "cell_type": "code", "metadata": { "id": "2RfZNG23HvrN" }, "source": [ "pdc = '''\n", "With pdc AS (\n", " SELECT meta.case_submitter_id, quant.gene_symbol, \n", " CAST(quant.protein_abundance_log2ratio AS FLOAT64) AS protein_abundance_log2ratio\n", " FROM `isb-cgc-bq.CPTAC.quant_proteome_CPTAC_LUAD_discovery_study_pdc_current` as quant\n", " JOIN `isb-cgc-bq.PDC_metadata.aliquot_to_case_mapping_current` as meta\n", " ON quant.case_id = meta.case_id\n", " AND quant.aliquot_id = meta.aliquot_id\n", " AND meta.sample_type = 'Primary Tumor'\n", ")\n", "'''" ], "execution_count": 7, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qeOtZ5_B3a_1" }, "source": [ "The following query combines the pdc and cptac data:" ] }, { "cell_type": "code", "metadata": { "id": "bKF3SMGS3iVC" }, "source": [ "cptac = '''\n", "qdata AS (\n", " SELECT pdc.case_submitter_id, pdc.gene_symbol, pdc.protein_abundance_log2ratio,\n", " cptac.protein_abundance\n", " FROM pdc \n", " JOIN `{0}.{1}` as cptac \n", " ON pdc.case_submitter_id = cptac.Patient_ID\n", " AND pdc.gene_symbol = cptac.gene_name\n", ")\n", "'''.format(my_project_id, table_id)" ], "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "RIDoFleQ4c9Y" }, "source": [ "Finally we compute Pearson correlations." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 436 }, "id": "2TOwffr24iXB", "outputId": "809d0ae8-0681-4bcc-a864-6ca643935a40" }, "source": [ "mysql = (pdc + ',' + cptac + '''\n", "SELECT gene_symbol, count(*) as N, corr(protein_abundance_log2ratio,protein_abundance) as Correlations\n", "FROM qdata \n", "WHERE NOT IS_NAN(protein_abundance_log2ratio)\n", " AND NOT IS_NAN(protein_abundance) \n", "GROUP BY gene_symbol\n", "HAVING N >= 20 \n", "ORDER BY Correlations DESC\n", "''' )\n", "\n", "df1 = pandas_gbq.read_gbq(mysql,project_id=my_project_id )\n", "df1" ], "execution_count": 9, "outputs": [ { "output_type": "stream", "text": [ "Downloading: 100%|██████████| 9650/9650 [00:00<00:00, 25317.80rows/s]\n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/html": [ "
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gene_symbolNCorrelations
0GLYATL2210.990671
1SLC2A10490.989670
2TNC1080.983017
3BCAS11040.980725
4SLC27A21080.980098
............
9645SACS108-0.166687
9646SLC38A930-0.303329
9647SASS627-0.329240
9648SPEF230-0.382587
9649NUFIP123-0.383124
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9650 rows × 3 columns

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" ], "text/plain": [ " gene_symbol N Correlations\n", "0 GLYATL2 21 0.990671\n", "1 SLC2A10 49 0.989670\n", "2 TNC 108 0.983017\n", "3 BCAS1 104 0.980725\n", "4 SLC27A2 108 0.980098\n", "... ... ... ...\n", "9645 SACS 108 -0.166687\n", "9646 SLC38A9 30 -0.303329\n", "9647 SASS6 27 -0.329240\n", "9648 SPEF2 30 -0.382587\n", "9649 NUFIP1 23 -0.383124\n", "\n", "[9650 rows x 3 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 9 } ] }, { "cell_type": "markdown", "metadata": { "id": "yFQWllYqtsD8" }, "source": [ "## Histogram of correlations\n", "The results above show the correlation between PDC and cptac protein expressions for 9650 proteins. Next we show a histogram of these correlations." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 386 }, "id": "N_MRrl3gIf3V", "outputId": "382a5214-ca23-489b-9561-4643e637320d" }, "source": [ "sns.displot(data=df1, x=\"Correlations\", binwidth=0.1)\n", "plt.xlim(-1.0, 1.1)" ], "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(-1.0, 1.1)" ] }, "metadata": { "tags": [] }, "execution_count": 10 }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "OqUF2eqGUZMz" }, "source": [ "The histogram shows that the two pipelines used in this analysis produced similar protein expression for most genes." ] } ] }