ABIDE_figs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating ABIDE Visualizations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Import\n",
    "Import the necessary libraries and the create a cleaned up version of the original ABIDE csv file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os.path as op\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "# location of partipcant csv files\n",
    "dir = \"/Users/chloehampson/Desktop/nbc/ABIDE/Hb_abide\"\n",
    "csv_dir = \"/Users/chloehampson/Desktop/nbc/ABIDE/Hb_abide/csv_files\"\n",
    "\n",
    "# create a path for the newly created csv files\n",
    "csv_file_path = op.join(csv_dir, \"pheno_data.csv\")\n",
    "\n",
    "# dataframe for combined reduced ABIDE csv\n",
    "abide_df = pd.read_csv(op.join(csv_dir, \"abide.csv\"))\n",
    "\n",
    "# replace the -9999.0, '#', -9999 with no value\n",
    "values_to_replace = [-9999.0, \"#\", \"-9999\"]\n",
    "for value in values_to_replace:\n",
    "    abide_df.replace(value, np.nan, inplace=True)\n",
    "\n",
    "\n",
    "# Save the melted DataFrame to the CSV file\n",
    "csv_file_path = op.join(csv_dir, \"abide_data.csv\")\n",
    "abide_df.to_csv(csv_file_path, index=False)\n",
    "print(abide_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Dataframes and Plots\n",
    "In this step, we modify the the ABIDE dataframe to have them in a format where we are able to create visualtions with the desired information.\n",
    "#### 1. ASD and HC\n",
    "We first must create a dataframe that will be used to compare our counts for ASD and HC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with relevant categories\n",
    "ppt_df = abide_df[[\"ABIDE\", \"alternative_id_1\", \"HANDEDNESS_CATEGORY\", \"CURRENT_MED_STATUS\", \"DX_GROUP\", \"DSM_IV_TR\", \"AGE_AT_SCAN\", \"SEX\", \"ADI_R_RSRCH_RELIABLE\", \"ADOS_RSRCH_RELIABLE\"]]\n",
    "\n",
    "ppt_df[\"DX_GROUP\"].replace({1: \"ASD\", 2: \"HC\"}, inplace=True)\n",
    "ppt_df[\"SEX\"].replace({1: \"Male\", 2: \"Female\"}, inplace=True)\n",
    "ppt_df[\"CURRENT_MED_STATUS\"].replace({0: \"No Meds\", 1: \"Meds\"}, inplace=True)\n",
    "ppt_df[\"DSM_IV_TR\"].replace(\n",
    "    {\n",
    "        0: \"Control\",\n",
    "        1: \"Autism\",\n",
    "        2: \"Asperbergers\",\n",
    "        3: \"PPD-NOS\",\n",
    "        4: \"Aspergers or PDD-NOS\",\n",
    "    },\n",
    "    inplace=True,\n",
    ")\n",
    "ppt_df[\"HANDEDNESS_CATEGORY\"].replace(\n",
    "    {\n",
    "        1: \"Right\",\n",
    "        2: \"Left\",\n",
    "        3: \"Mixed\",\n",
    "\n",
    "    },\n",
    "    inplace=True,\n",
    ")\n",
    "ppt_df[\"AGE_AT_SCAN\"] = pd.to_numeric(ppt_df[\"AGE_AT_SCAN\"], errors=\"coerce\")\n",
    "print(ppt_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code creates a countplot to see total counts of ASD and HC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, bx = plt.subplots(figsize=(10, 7))\n",
    "sns.countplot(\n",
    "    data=ppt_df, x=\"DX_GROUP\", stat=\"count\", width=0.8, alpha=0.6, ax=bx, zorder=2\n",
    ")\n",
    "bx.set_xlabel(\"DX GROUP\")\n",
    "bx.set_ylabel(\"Number of Participants\")\n",
    "bx.set_title(\"Participant DX Count Histogram\")\n",
    "bx.legend(title=\"DX_GROUP\", loc=\"upper right\")\n",
    "for line_count in range(0, 801, 50):\n",
    "    bx.axhline(line_count, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code creates a scatterplot to show the distribution of ASD to HC. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "\n",
    "# sns.stripplot(data=ppt_df, x=\"AGE_AT_SCAN\", hue=\"DX_GROUP\", palette=\"Set2\", orient='h', jitter = 0.1, dodge = 'True', size=4, edgecolor = \"black\",\n",
    "#  linewidth = 0.3, ax = ax, zorder=4)\n",
    "sns.swarmplot(\n",
    "    data=ppt_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    dodge=1.2,  # Adjust this value as needed\n",
    "    size=4,\n",
    "    edgecolor=\"black\",\n",
    "    linewidth=0.3,\n",
    "    ax=ax,\n",
    "    zorder=4\n",
    ")\n",
    "sns.boxplot(\n",
    "    data=ppt_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    dodge=True,\n",
    "    gap=.4,\n",
    "    whis=(0, 100),\n",
    "    orient=\"h\",\n",
    "    linecolor=\"black\",\n",
    "    linewidth=1.7,\n",
    "    width=.8,\n",
    "    ax=ax,\n",
    "    zorder=3,\n",
    ")\n",
    "sns.violinplot(\n",
    "    data=ppt_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    split=True,\n",
    "    inner=None,\n",
    "    alpha=1,\n",
    "    width=.99,\n",
    "    linecolor=\"black\",\n",
    "    ax=ax,\n",
    "    zorder=2,\n",
    ")\n",
    "\n",
    "# Create a custom legend for the diagnostic group\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "custom_legend = ax.legend(handles[0:2], labels[0:2], title=\"DX_GROUP\", loc=\"upper right\")\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Age at Scan\")\n",
    "ax.set_ylabel(\"Distribution of Diagnostic Group\")\n",
    "ax.set_title(\" Diagnostic Group Distribution (Combined ABIDE I/II)\")\n",
    "\n",
    "ax.set_xticks(range(0, int(ppt_df[\"AGE_AT_SCAN\"].max()) + 1, 5))\n",
    "for age in range(10, int(ppt_df[\"AGE_AT_SCAN\"].max()) + 1, 10):\n",
    "    ax.axvline(x=age, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 2))\n",
    "\n",
    "sns.stripplot(\n",
    "    data=ppt_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"DX_GROUP\",\n",
    "    hue=\"CURRENT_MED_STATUS\",\n",
    "    palette=\"Set2\",\n",
    "    edgecolor=\"black\",\n",
    "    jitter=0.4,  # Adjust the jitter value as needed\n",
    "    linewidth=0.3,\n",
    "    ax=ax,\n",
    "    zorder=4\n",
    ")\n",
    "\n",
    "# Create a custom legend for the diagnostic group\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "custom_legend = ax.legend(handles[0:2], labels[0:2], title=\"DX_GROUP\", loc=\"upper right\")\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_title(\"\")\n",
    "ax.legend(title=\"CURRENT_MED_STATUS\", loc=\"upper right\")\n",
    "\n",
    "ax.set_xticks(range(0, int(ppt_df[\"AGE_AT_SCAN\"].max()) + 1, 5))\n",
    "for age in range(10, int(ppt_df[\"AGE_AT_SCAN\"].max()) + 1, 10):\n",
    "    ax.axvline(x=age, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 2))\n",
    "\n",
    "sns.stripplot(\n",
    "    data=ppt_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"DX_GROUP\",\n",
    "    hue=\"ADOS_RSRCH_RELIABLE\",\n",
    "    palette=\"Set2\",\n",
    "    edgecolor=\"black\",\n",
    "    jitter=0.4,  # Adjust the jitter value as needed\n",
    "    linewidth=0.3,\n",
    "    ax=ax,\n",
    "    zorder=4\n",
    ")\n",
    "\n",
    "# Create a custom legend for the diagnostic group\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "custom_legend = ax.legend(handles[0:2], labels[0:2], title=\"DX_GROUP\", loc=\"upper right\")\n",
    "\n",
    "ax.set_xlabel(\"Age at Scan\")\n",
    "ax.set_ylabel(\"\")\n",
    "ax.set_title(\"\")\n",
    "ax.legend(title=\"ADOS_RSRCH_RELIABLE\", loc=\"upper right\")\n",
    "\n",
    "ax.set_xticks(range(0, int(ppt_df[\"AGE_AT_SCAN\"].max()) + 1, 5))\n",
    "for age in range(10, int(ppt_df[\"AGE_AT_SCAN\"].max()) + 1, 10):\n",
    "    ax.axvline(x=age, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "\n",
    "sns.swarmplot(\n",
    "    data=ppt_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"ABIDE\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    dodge=1.2,  # Adjust this value as needed\n",
    "    size=4,\n",
    "    edgecolor=\"black\",\n",
    "    linewidth=0.3,\n",
    "    ax=ax,\n",
    "    zorder=4\n",
    ")\n",
    "sns.violinplot(\n",
    "    data=ppt_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"ABIDE\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    split=True,\n",
    "    inner=None,\n",
    "    alpha=1,\n",
    "    width=.99,\n",
    "    linecolor=\"black\",\n",
    "    ax=ax,\n",
    "    zorder=2,\n",
    ")\n",
    "sns.boxplot(\n",
    "    data=ppt_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"ABIDE\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    dodge=True,\n",
    "    gap=.4,\n",
    "    whis=(0, 100),\n",
    "    orient=\"h\",\n",
    "    linecolor=\"black\",\n",
    "    linewidth=1.7,\n",
    "    width=.8,\n",
    "    ax=ax,\n",
    "    zorder=3,\n",
    ")\n",
    "\n",
    "# Create a custom legend for the diagnostic group\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "custom_legend = ax.legend(handles[0:2], labels[0:2], title=\"DX_GROUP\", loc=\"upper right\")\n",
    "\n",
    "ax.set_xlabel(\"Age at Scan\")\n",
    "ax.set_ylabel(\"ABIDE Dataset\")\n",
    "ax.set_title(\" Diagnostic Group Distribution (Seperate ABIDE I/II)\")\n",
    "\n",
    "ax.set_xticks(range(0, int(ppt_df[\"AGE_AT_SCAN\"].max()) + 1, 5))\n",
    "for age in range(10, int(ppt_df[\"AGE_AT_SCAN\"].max()) + 1, 10):\n",
    "    ax.axvline(x=age, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot factors in sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot is the same plot and factors in dsm-5 categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Phenotypic Dataframe\n",
    "We must first create a dataframe that will be used to analyze our phenotypic counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a temporary df of relevant information\n",
    "temp_pheno_df = abide_df[\n",
    "    [\n",
    "        \"alternative_id_1\",\n",
    "        \"DX_GROUP\",\n",
    "        \"DSM_IV_TR\",\n",
    "        \"AGE_AT_SCAN\",\n",
    "        \"SEX\",\n",
    "        \"ADI_RRB_TOTAL_C\",\n",
    "        \"ADI_R_SOCIAL_TOTAL_A\",\n",
    "        \"ADI_R_VERBAL_TOTAL_BV\",\n",
    "        \"ADOS_GOTHAM_SOCAFFECT\",\n",
    "        \"ADOS_GOTHAM_RRB\",\n",
    "        \"SRS_MOTIVATION\",\n",
    "        \"VINELAND_DAILYLVNG_STANDARD\",\n",
    "        \"VINELAND_COPING_V_SCALED\",\n",
    "    ]\n",
    "]\n",
    "print(temp_pheno_df)\n",
    "\n",
    "# Select the last 6 columns and melt them into a 'phenotype' column\n",
    "pheno_df = temp_pheno_df.melt(\n",
    "    id_vars=[\"alternative_id_1\", \"DX_GROUP\", \"DSM_IV_TR\", \"AGE_AT_SCAN\", \"SEX\"],\n",
    "    value_vars=[\n",
    "        \"ADI_RRB_TOTAL_C\",\n",
    "        \"ADI_R_SOCIAL_TOTAL_A\",\n",
    "        \"ADI_R_VERBAL_TOTAL_BV\",\n",
    "        \"ADOS_GOTHAM_SOCAFFECT\",\n",
    "        \"ADOS_GOTHAM_RRB\",\n",
    "        \"SRS_MOTIVATION\",\n",
    "        \"VINELAND_DAILYLVNG_STANDARD\",\n",
    "        \"VINELAND_COPING_V_SCALED\",\n",
    "    ],\n",
    "    var_name=\"Phenotype\",\n",
    "    value_name=\"value\",\n",
    ")\n",
    "\n",
    "# Remove NaN values from the 'value' column\n",
    "pheno_df = pheno_df.dropna(subset=[\"value\"])\n",
    "pheno_df = pheno_df[pheno_df[\"AGE_AT_SCAN\"] != \"#\"]\n",
    "pheno_df[\"AGE_AT_SCAN\"] = pd.to_numeric(pheno_df[\"AGE_AT_SCAN\"], errors=\"coerce\")\n",
    "\n",
    "pheno_df[\"DX_GROUP\"].replace({1: \"ASD\", 2: \"HC\"}, inplace=True)\n",
    "pheno_df[\"DSM_IV_TR\"].replace(\n",
    "    {\n",
    "        0: \"Control\",\n",
    "        1: \"Autism\",\n",
    "        2: \"Asperbergers\",\n",
    "        3: \"PPD-NOS\",\n",
    "        4: \"Aspergers or PDD-NOS\",\n",
    "    },\n",
    "    inplace=True,\n",
    ")\n",
    "print(pheno_df)\n",
    "\n",
    "# Save the melted DataFrame to the CSV file\n",
    "csv_file_path = op.join(csv_dir, \"pheno_data.csv\")\n",
    "pheno_df.to_csv(csv_file_path, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code creates a countplot for the phenotypes with dsm 5 totals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ex = plt.subplots(figsize=(10, 7))\n",
    "sns.countplot(\n",
    "    data=pheno_df,\n",
    "    x=\"Phenotype\",\n",
    "    hue=\"DSM_IV_TR\",\n",
    "    stat=\"count\",\n",
    "    palette=\"Set2\",\n",
    "    width=0.8,\n",
    "    ax=ex,\n",
    "    zorder=2,\n",
    ")\n",
    "ex.set_xlabel(\"Phenotype\")\n",
    "ex.set_ylabel(\"Number of Participants\")\n",
    "ex.set_title(\"Participant DSM5 Phenotypic Count Histogram\")\n",
    "ex.legend(title=\"DSM_IV_TR\", loc=\"upper right\")\n",
    "ex.set_xticklabels(ex.get_xticklabels(), rotation=45, horizontalalignment=\"right\")\n",
    "for line_count in range(0, 226, 25):\n",
    "    ex.axhline(line_count, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "    value_counts = pheno_df[\"Phenotype\"].value_counts()\n",
    "    value_counts = pheno_df.groupby(\"Phenotype\")[\"Phenotype\"].count()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ex = plt.subplots(figsize=(10, 7))\n",
    "sns.countplot(\n",
    "    data=pheno_df,\n",
    "    x=\"Phenotype\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    stat=\"count\",\n",
    "    palette=\"Set2\",\n",
    "    width=0.8,\n",
    "    ax=ex,\n",
    "    zorder=2,\n",
    ")\n",
    "ex.set_xlabel(\"Phenotype\")\n",
    "ex.set_ylabel(\"Number of Participants\")\n",
    "ex.set_title(\"Participant Diagnostic Group Phenotypic Count Histogram\")\n",
    "ex.legend(title=\"DX_GROUP\", loc=\"upper right\")\n",
    "ex.set_xticklabels(ex.get_xticklabels(), rotation=45, horizontalalignment=\"right\")\n",
    "for line_count in range(0, 401, 25):\n",
    "    ex.axhline(line_count, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "    value_counts = pheno_df[\"Phenotype\"].value_counts()\n",
    "    value_counts = pheno_df.groupby(\"Phenotype\")[\"Phenotype\"].count()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code creates a raincloudplot of the phenotypic age distribution for diagnostic category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, cx = plt.subplots(figsize=(10, 7))\n",
    "sns.stripplot(\n",
    "    data=pheno_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"Phenotype\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    jitter=0.05,\n",
    "    dodge=True,\n",
    "    size=3,\n",
    "    edgecolor=\"black\",\n",
    "    linewidth=0.5,\n",
    "    ax=cx,\n",
    "    zorder=3,\n",
    ")\n",
    "sns.boxplot(\n",
    "    data=pheno_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"Phenotype\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    whis=(0, 100),\n",
    "    width=0.83,\n",
    "    gap=0.75,\n",
    "    linecolor=\"black\",\n",
    "    linewidth=1.2,\n",
    "    ax=cx,\n",
    "    zorder=4,\n",
    ")\n",
    "sns.violinplot(\n",
    "    data=pheno_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"Phenotype\",\n",
    "    hue=\"DX_GROUP\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    split=True,\n",
    "    inner=None,\n",
    "    alpha=0.8,\n",
    "    width=1.2,\n",
    "    linecolor=\"black\",\n",
    "    ax=cx,\n",
    "    zorder=2,\n",
    ")\n",
    "\n",
    "cx.set_xlabel(\"Age at Scan\")\n",
    "cx.set_ylabel(\"Phenotype\")\n",
    "cx.set_title(\"Participant Diagnostic Group Phenotypic Age Distribution\")\n",
    "legend_labels = [\"ASD\", \"Control\"]\n",
    "legend_handles = [\n",
    "    plt.Line2D([0], [0], color=sns.color_palette(\"Set2\")[0], lw=4),\n",
    "    plt.Line2D([0], [0], color=sns.color_palette(\"Set2\")[1], lw=4),\n",
    "]\n",
    "cx.legend(legend_handles, legend_labels, title=\"DX_GROUP\", loc=\"lower right\")\n",
    "for age in range(10, int(pheno_df[\"AGE_AT_SCAN\"].max()) + 1, 10):\n",
    "    cx.axvline(x=age, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code creates a scatterplot with a boxplot for the phenotypic age with dsm5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, dx = plt.subplots(figsize=(10, 7))\n",
    "sns.boxplot(\n",
    "    data=pheno_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"Phenotype\",\n",
    "    hue=\"DSM_IV_TR\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    whis=(0, 100),\n",
    "    flierprops=dict(markersize=3),\n",
    "    linecolor=\"black\",\n",
    "    linewidth=1.2,\n",
    "    ax=dx,\n",
    "    zorder=2,\n",
    ")\n",
    "sns.stripplot(\n",
    "    data=pheno_df,\n",
    "    x=\"AGE_AT_SCAN\",\n",
    "    y=\"Phenotype\",\n",
    "    hue=\"DSM_IV_TR\",\n",
    "    palette=\"Set2\",\n",
    "    orient=\"h\",\n",
    "    jitter=0.23,\n",
    "    dodge=True,\n",
    "    size=3,\n",
    "    edgecolor=\"gray\",\n",
    "    linewidth=0.6,\n",
    "    ax=dx,\n",
    "    zorder=3,\n",
    ")\n",
    "dx.set_xlabel(\"Age at Scan\")\n",
    "dx.set_ylabel(\"Phenotype\")\n",
    "dx.set_title(\"Participant DSM5 Phenotypic Age Distribution\")\n",
    "dx.legend(title=\"DSM_IV_TR\", loc=\"lower right\")\n",
    "for age in range(10, int(pheno_df[\"AGE_AT_SCAN\"].max()) + 1, 10):\n",
    "    dx.axvline(x=age, color=\"gray\", linestyle=\"--\", linewidth=1.0, zorder=1)\n",
    "plt.tight_layout()"
   ]
  }
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