{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Use Case 6: Comparing Derived Molecular Data with Proteomics\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this use case, we will be looking at the derived molecular data contained in the Endometrial dataset, and comparing it with protein data. Derived molecular data means that we created new variables based on molecular data. One example of this is the activity of a pathway based on the abundance of phosphorylation sites. A second example is inferred cell type percentages from algorithms like CIBERSORT, which are based on comparing transcriptomics data to known profiles of pure cell types. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Importing packages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will start by importing the python packages we will need, including the cptac data package. We will then load the Endometrial dataset which includes the endometrial patient data as well as accessory functions that we will use to analyze the data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import cptac\n", "en = cptac.Ucec()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Getting data and selecting attributes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this use case, we will be using two dataframes contained in the Endometrial dataset: `derived_molecular` and `proteomics`. We will load the derived_molecular dataframe and examine the data contained within it." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \r" ] } ], "source": [ "der_molecular = en.get_derived_molecular('awg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The derived molecular dataframe contains many different attributes that we can choose from for analysis. To view a list of these attributes, we can print out the column names of the dataframe. Here we print only the first 10 column names. To view the full list of column names without truncation, omit the slice (`[:10]`) at the end of the call. If your terminal is still abbreviating the list, first use the command `pd.set_option('display.max_seq_items', None)`.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['Estrogen_Receptor',\n", " 'Estrogen_Receptor_%',\n", " 'Progesterone_Receptor',\n", " 'Progesterone_Receptor_%',\n", " 'MLH1',\n", " 'MLH2',\n", " 'MSH6',\n", " 'PMS2',\n", " 'p53',\n", " 'Other_IHC_specify']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "der_molecular.columns.tolist()[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this use case, we will compare MSI status with the JAK1 protein abundance. MSI stands for [Microsatellite instability](https://en.wikipedia.org/wiki/Microsatellite_instability). The possible values for MSI status are MSI-H (high microsatellite instability) or MSS (microsatellite stable). In this context, \"nan\" refers to non-tumor samples. To see all of the possible values in any column, you can use the pandas function `.unique()`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['MSI-H', 'MSS', nan], dtype=object)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "der_molecular['MSI_status'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Join dataframes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the `en.join_metadada_to_omics` function to join our desired molecular trait with the proteomics data. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \r" ] } ], "source": [ "joined_data = en.join_metadata_to_omics(metadata_name=\"derived_molecular\",\n", " metadata_source=\"awg\",\n", " metadata_cols='MSI_status',\n", " omics_name=\"proteomics\",\n", " omics_source=\"awg\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Plot data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will use the seaborn and matplotlib libraries to create a boxplot and histogram that will allow us to visualize this data. For more information on using seaborn, see this [Seaborn tutorial](https://seaborn.pydata.org/tutorial.html)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "msi_boxplot = sns.boxplot(x='MSI_status', y='JAK1_awg_proteomics', data=joined_data, showfliers=False, \n", " order=['MSS', 'MSI-H'])\n", "msi_boxplot = sns.stripplot(x='MSI_status', y='JAK1_awg_proteomics', data=joined_data, color = '.3', \n", " order=['MSS', 'MSI-H'])\n", "plt.show()\n", "\n", "msi_histogram = sns.FacetGrid(joined_data[['MSI_status', 'JAK1_awg_proteomics']], hue=\"MSI_status\", \n", " legend_out=False, aspect=3)\n", "msi_histogram = msi_histogram.map(sns.kdeplot, \"JAK1_awg_proteomics\").add_legend(title=\"MSI_status\")\n", "msi_histogram.set(ylabel='Proportion')\n", "plt.show()" ] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }