gr.275453.121/A4_Fig1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3468d800",
   "metadata": {},
   "source": [
    "# VIP This notebook use data from `Fig3c.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad1f0ee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys, warnings\n",
    "\n",
    "import numpy  as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_context(context='poster')\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['font.sans-serif'] = \"Arial\"\n",
    "mpl.rcParams['font.monospace'] = 'Andale Mono'\n",
    "# mpl.rcParams['mathtext.fontset'] = 'stix'\n",
    "mpl.rcParams['mathtext.rm'] = 'Arial'\n",
    "mpl.rcParams['mathtext.default'] = 'rm'\n",
    "\n",
    "mpl.rcParams['axes.linewidth'] = 1.5\n",
    "mpl.rcParams['xtick.major.width'] = 1.5\n",
    "mpl.rcParams['ytick.major.width'] = 1.5\n",
    "mpl.rcParams['text.usetex'] = False\n",
    "mpl.rcParams['text.latex.preamble'] = [\n",
    "    r'\\usepackage{wasysym}',\n",
    "    r'\\usepackage{amsmath}',\n",
    "    r'\\usepackage{amssymb}',\n",
    "]\n",
    "\n",
    "# mpl.rcParams['font.family'] = \"sans-serif\"\n",
    "# mpl.rcParams['font.serif'] = 'serif'\n",
    "# [f.name for f in mpl.font_manager.fontManager.ttflist if 'Mono' in f.name]\n",
    "\n",
    "bigsize   = 20\n",
    "midsize   = 20\n",
    "smallsize = 15\n",
    "hugesize  = 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa622068",
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append('.'); warnings.simplefilter(action='ignore', category=FutureWarning);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b86f6dd",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9360eef0",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "from figdata import xlsx_tab, mmqc\n",
    "from figdata import sample_colors, f1, f2, f3, f4\n",
    "\n",
    "from figdata import xlsx_tab, cells_tab\n",
    "from figdata import n_big_cnv, n_small_cnv\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "from matplotlib.colors import Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e9198c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fig1_barplot(axs, FSAMPLE=True):\n",
    "#     fig, axs = plt.subplots(ncols=1, nrows=2, figsize=(12,4), gridspec_kw={'height_ratios':[10,1],'hspace':0})\n",
    "\n",
    "    thresh = 2e5\n",
    "    three_colors = ['greyish', 'dusty purple', 'amber']\n",
    "    # three_colors = ['greyish', 'faded green', 'windows blue']\n",
    "    three_colors = sns.xkcd_palette(three_colors)\n",
    "    \n",
    "    folder = Path('/$MYPATH/scTn5/1k_SingleCell_Xten/hmmcopy/99.Save/01.bam_stat_list')\n",
    "\n",
    "    sum_n = []\n",
    "    idx = 0\n",
    "\n",
    "    if FSAMPLE:\n",
    "        fig_df = xlsx_tab.copy()\n",
    "        fig_df = fig_df.set_index( np.arange(fig_df.shape[0]) )\n",
    "        samples = xlsx_tab['SAMPLE']\n",
    "    else:\n",
    "        fig_df = xlsx_tab.copy().drop(11).replace('M11','M10')\n",
    "        fig_df = fig_df.set_index( np.arange(fig_df.shape[0]) )\n",
    "        samples = xlsx_tab['SAMPLE'].drop(11)\n",
    "\n",
    "    n_ticks = []\n",
    "    \n",
    "    for n, s in enumerate( samples ):\n",
    "        f = s+'.raw.list'\n",
    "        tmp_df = pd.read_csv(folder/f, sep='\\t', header=None ).rename(columns={0:'Name',1:'raw'})\n",
    "        tmp_df['Name'] = tmp_df['Name'].str.replace('.total_reads.list:', '_')\n",
    "        tmp_df['Lane'] = tmp_df['Name'].str.split('_',expand=True)[1]\n",
    "        n_lane  = tmp_df['Lane'].unique().shape[0]\n",
    "        n_get   = tmp_df.query('raw>@thresh').shape[0]\n",
    "        n_blank = (n_lane*5*96 - n_get)\n",
    "\n",
    "        x = idx+np.arange(0, n_lane*5*96)\n",
    "        y = np.log10( \n",
    "            np.array(\n",
    "                tmp_df.query('raw>@thresh').sort_values('raw',ascending=False)['raw'].tolist()\n",
    "                +\n",
    "                tmp_df.query('raw<@thresh').sort_values('raw',ascending=False)['raw'].tolist()[:n_blank]\n",
    "            )\n",
    "        )\n",
    "        mid_p = x.mean()\n",
    "        if FSAMPLE:\n",
    "            color = sample_colors[n]\n",
    "        elif n>9:\n",
    "            color = sample_colors[n+1]\n",
    "        else:\n",
    "            color = sample_colors[n]\n",
    "            \n",
    "        axs[0].fill_between(x, y, 0, color=color, zorder=0)\n",
    "#         axs[0].fill_between(x[y<np.log10(3e5)], np.log10(3e5), 0, facecolor='black', alpha=0.5, zorder=1, edgecolor=None)\n",
    "        axs[0].text(mid_p, 7, fig_df.loc[n, 'CODE'], fontsize=smallsize, ha='center')\n",
    "    \n",
    "        a = cells_tab.loc[s,'n_harvest']\n",
    "        b = cells_tab.loc[s,'n_reads']\n",
    "    #     b = tmp_df.query('raw>@thresh').shape[0]\n",
    "        c = cells_tab.loc[s,'n_pf']\n",
    "        \n",
    "        if len(sum_n) == 0:\n",
    "            axs[1].broken_barh([(idx+b, a-b)], [0.5,1], color='black', edgecolor='k', label='Undetected')\n",
    "            axs[1].broken_barh([(idx+c, b-c)], [0.5,1], color='gray',  edgecolor='k', label='Fail-QC')\n",
    "            axs[1].broken_barh([(idx+0, c-0)], [0.5,1], color='white', edgecolor='k', label='Pass-QC')\n",
    "        else:\n",
    "            axs[1].broken_barh([(idx+b, a-b)], [0.5,1], color='black', edgecolor='k')\n",
    "            axs[1].broken_barh([(idx+c, b-c)], [0.5,1], color='gray',  edgecolor='k')\n",
    "            axs[1].broken_barh([(idx+0, c-0)], [0.5,1], color='white', edgecolor='k')\n",
    "        \n",
    "        n_ticks.append( (mid_p, c ) )\n",
    "        \n",
    "        sum_n.append(n_lane*5*96)\n",
    "        idx += n_lane*5*96 + 10\n",
    "\n",
    "    axs[0].set_ylim(0,8)\n",
    "    axs[0].set_ylabel(r'Reads ($10^n$)', fontsize=bigsize)\n",
    "#     axs[0].hlines(np.log10(3e5), 0, idx, linestyles='dashed', color='k', lw=2)\n",
    "#     axs[0].text(idx, np.log10(3e5), ' 0.3M', ha='left', va='center', fontsize=midsize)\n",
    "\n",
    "    axs[0].set_yticks([0,2,4,6,8])\n",
    "    axs[0].set_yticklabels([0,2,4,6,8], fontsize=midsize)\n",
    "    axs[0].tick_params(axis='y', labelsize=midsize, length=5)\n",
    "\n",
    "    axs[0].set_title('Sequencing depth', fontsize=bigsize, pad=smallsize)\n",
    "\n",
    "    axs[0].set_xlim(0,idx)\n",
    "    axs[1].set_xlim(0,idx)\n",
    "    axs[0].set_xticks( [] )\n",
    "    \n",
    "    axs[1].xaxis.set_major_locator(mpl.ticker.FixedLocator( [0,idx] ))\n",
    "    axs[1].xaxis.set_minor_locator(mpl.ticker.FixedLocator( np.array(n_ticks)[:,0] ))\n",
    "    axs[1].xaxis.set_major_formatter(mpl.ticker.FixedFormatter( ['#1', '#' + str(np.array(sum_n).sum())] ))\n",
    "    axs[1].xaxis.set_minor_formatter(mpl.ticker.FixedFormatter( np.array(n_ticks)[:,1].astype(int) ))\n",
    "    axs[1].tick_params(\n",
    "        which='major', axis='x', labelsize=midsize, length=0\n",
    "    )\n",
    "    axs[1].tick_params(\n",
    "        which='minor', axis='x', labelsize=smallsize, length=0, \n",
    "        labeltop=True, labelbottom=False, \n",
    "        rotation=45,\n",
    "    )\n",
    "    \n",
    "    axs[1].set_ylim(0.2,1)\n",
    "    axs[1].set_yticks([])\n",
    "\n",
    "    axs[1].set_xlabel('Cell ID', labelpad=-1*smallsize, fontsize=bigsize)\n",
    "    axs[1].spines['bottom'].set_visible(False)\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b56c234b",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bfc54532",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "from figdata import ref, xlsx_tab, cells_tab, mmqc, good_df, sum_df\n",
    "from figdata import sample_colors, good_pos, r_acen, chrf, bigCNV, big_cnv_idx, small_cnv_idx\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "from figdata import xlsx_tab, mmqc, sum_df\n",
    "from figdata import sample_colors, good_pos, r_acen, chrf, bigCNV, f1, f2, f3, f4\n",
    "\n",
    "from scipy.ndimage.filters import gaussian_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35b90e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fig2_plot(ax, FSAMPLE=True):\n",
    "#     fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(4,6))\n",
    "\n",
    "    two_colors = sns.xkcd_palette(['bright blue','red'])\n",
    "\n",
    "    fig_df1 = pd.concat(\n",
    "        [\n",
    "            xlsx_tab.set_index('SAMPLE'),\n",
    "            (\n",
    "                sum_df.loc[big_cnv_idx]\n",
    "                    .query('Space!=\"chrY\" & Space!=\"chrX\"')\n",
    "                    .groupby(['Cell','Sample']).count().reset_index()\n",
    "                    .groupby('Sample').count()['Cell']\n",
    "            ),\n",
    "            cells_tab['n_pf'],\n",
    "        ], axis=1, sort=False\n",
    "    )\n",
    "\n",
    "    fig_df2 = pd.concat(\n",
    "        [\n",
    "            xlsx_tab.set_index('SAMPLE'),\n",
    "            (\n",
    "#                 sum_df.drop(big_cnv_idx)\n",
    "                sum_df.loc[small_cnv_idx]\n",
    "                    .query('r<@r_acen & Space!=\"chrY\" & Space!=\"chrX\"')\n",
    "                    .groupby(['Cell','Sample']).count().reset_index()\n",
    "                    .groupby('Sample').count()['Cell']\n",
    "            ),\n",
    "            cells_tab['n_pf'],\n",
    "        ], axis=1, sort=False\n",
    "    )\n",
    "\n",
    "    fig_df1['Ratio'] = 100*fig_df1['Cell']/fig_df1['n_pf']\n",
    "    fig_df2['Ratio'] = 100*fig_df2['Cell']/fig_df2['n_pf']\n",
    "\n",
    "    if not FSAMPLE:\n",
    "        fig_df1 = fig_df1.drop('$FSAMPLE').replace('M11','M10')\n",
    "        fig_df2 = fig_df2.drop('$FSAMPLE').replace('M11','M10')\n",
    "\n",
    "    ax.bar(\n",
    "        0, \n",
    "        fig_df1.groupby('GENDER').mean().loc['male',  'Ratio'], \n",
    "        yerr=fig_df1.groupby('GENDER').std().loc['male',  'Ratio'], \n",
    "        edgecolor='k', color=two_colors[0], alpha=0.8\n",
    "    )\n",
    "    ax.bar(\n",
    "        2, \n",
    "        fig_df1.groupby('GENDER').mean().loc['female','Ratio'], \n",
    "        yerr=fig_df1.groupby('GENDER').std().loc['female','Ratio'], \n",
    "        edgecolor='k', color=two_colors[1], alpha=0.8\n",
    "    )\n",
    "\n",
    "    ax.bar(\n",
    "        1, \n",
    "        fig_df2.groupby('GENDER').mean().loc['male',  'Ratio'], \n",
    "        yerr=fig_df2.groupby('GENDER').std().loc['male',  'Ratio'], \n",
    "        edgecolor='k', color=two_colors[0], alpha=0.8\n",
    "    )\n",
    "    ax.bar(\n",
    "        3, \n",
    "        fig_df2.groupby('GENDER').mean().loc['female','Ratio'], \n",
    "        yerr=fig_df2.groupby('GENDER').std().loc['female','Ratio'], \n",
    "        edgecolor='k', color=two_colors[1], alpha=0.8\n",
    "    )\n",
    "\n",
    "    ax.xaxis.set_minor_locator(mpl.ticker.FixedLocator([0.5,2.5]))\n",
    "    ax.xaxis.set_minor_formatter(mpl.ticker.FixedFormatter(['$\\u2642$','$\\u2640$']))\n",
    "#     ax.xaxis.set_minor_formatter(mpl.ticker.FixedFormatter([r'\\male',r'\\female']))\n",
    "\n",
    "    ax.xaxis.set_major_locator(mpl.ticker.FixedLocator([0,1,2,3]))\n",
    "    ax.set_xticklabels(('>10M', '$\\leqslant$10M', '>10M', '$\\leqslant$10M'), fontsize=midsize, rotation=45)\n",
    "\n",
    "    ax.tick_params(which='major', length=5)\n",
    "    ax.tick_params(which='minor', labelsize=hugesize, pad=3*hugesize, length=0)\n",
    "\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_ylabel('% of cell with CNV', fontsize=midsize)\n",
    "\n",
    "    ax.set_xlim(-0.5,3.5)\n",
    "    ax.set_ylim(0,60)\n",
    "\n",
    "    ax.tick_params(axis='y', labelsize=midsize, length=5)\n",
    "\n",
    "    n_male = fig_df1.groupby('GENDER')['CODE'].count()['male']\n",
    "    \n",
    "    xa1 = stats.uniform(-0.1, 0.1 * 2).rvs(n_male)+0\n",
    "    xa2 = stats.uniform(-0.1, 0.1 * 2).rvs(n_male)+1\n",
    "    ya1 = fig_df1.query('GENDER==\"male\"')['Ratio'].values\n",
    "    ya2 = fig_df2.query('GENDER==\"male\"')['Ratio'].values\n",
    "\n",
    "    ax.plot(xa1, ya1, 'v', color='white', ms=10, alpha=0.7, markeredgecolor='k', markeredgewidth=1)\n",
    "    ax.plot(xa2, ya2, 'v', color='white', ms=10, alpha=0.7, markeredgecolor='k', markeredgewidth=1)\n",
    "\n",
    "    for n in range(n_male):\n",
    "        ax.plot( [xa1[n],xa2[n]], [ya1[n],ya2[n]], '-', color='k', lw=1)\n",
    "\n",
    "    n_female = fig_df1.groupby('GENDER')['CODE'].count()['female']\n",
    "    \n",
    "    xa1 = stats.uniform(-0.1, 0.1 * 2).rvs(n_female)+2\n",
    "    xa2 = stats.uniform(-0.1, 0.1 * 2).rvs(n_female)+3\n",
    "    ya1 = fig_df1.query('GENDER==\"female\"')['Ratio'].values\n",
    "    ya2 = fig_df2.query('GENDER==\"female\"')['Ratio'].values\n",
    "\n",
    "    ax.plot(xa1, ya1, 'o', color='white', ms=7, alpha=0.9, markeredgecolor='k', markeredgewidth=1)\n",
    "    ax.plot(xa2, ya2, 'o', color='white', ms=7, alpha=0.9, markeredgecolor='k', markeredgewidth=1)\n",
    "\n",
    "    for n in range(n_female):\n",
    "        ax.plot( [xa1[n],xa2[n]], [ya1[n],ya2[n]], '-', color='k', lw=1)\n",
    "\n",
    "    ax.plot( [1,1,3,3], [47,48,48,47], '-', color='k', lw=2 )\n",
    "    ax.plot( [0,0,2,2], [54,55,55,54], '-', color='k', lw=2 )\n",
    "\n",
    "    p1 = np.round(\n",
    "        stats.ttest_ind(\n",
    "            fig_df1.query('GENDER==\"male\"'  )['Ratio'].values,\n",
    "            fig_df1.query('GENDER==\"female\"')['Ratio'].values,\n",
    "            equal_var = False,\n",
    "        ).pvalue,\n",
    "        4\n",
    "    )\n",
    "    p2 = np.round(\n",
    "        stats.ttest_ind(\n",
    "            fig_df2.query('GENDER==\"male\"'  )['Ratio'].values,\n",
    "            fig_df2.query('GENDER==\"female\"')['Ratio'].values,\n",
    "            equal_var = False,\n",
    "        ).pvalue,\n",
    "        4\n",
    "    )\n",
    "    ax.text(1, 57.5, p1, ha='center', va='center', fontsize=smallsize)\n",
    "    ax.text(2, 50.5, p2, ha='center', va='center', fontsize=smallsize)\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69fe85ef",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6aad7645",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def fig3_rainbow_plot(ax1, ax2, ax3, FSAMPLE=True, f=np.log10(4)):\n",
    "#     fig, (ax1, ax2, ax3) = plt.subplots(\n",
    "#         ncols=3, nrows=1, figsize=(11,4),\n",
    "#         gridspec_kw={\n",
    "#             'width_ratios': [20, 1, 1],\n",
    "#         },\n",
    "#     )\n",
    "    simu_dict = pd.read_csv('simu_dict.csv')\n",
    "    simu_dict['true_ratio'] = simu_dict['true_ratio'].apply(lambda x: round(x,3))\n",
    "\n",
    "#     ratios = np.arange(0,100.5,0.5)\n",
    "#     ratios = np.arange(0,20.5,0.1)\n",
    "#     ratios = np.arange(0,20.5,0.05)\n",
    "#     log_n_pick = np.linspace(0.5, 5, 100)\n",
    "\n",
    "    ratios = np.arange(0,20.5,0.01)\n",
    "    log_n_pick = np.linspace(0.5, 5, 200)\n",
    "\n",
    "    gb = simu_dict.groupby(['n_pick','true_ratio'], sort=False)\n",
    "\n",
    "    mean_df = gb.mean()\n",
    "    cv_df   = 100*(gb.std()/gb.mean()).fillna(0)\n",
    "\n",
    "    simu_df = pd.concat(\n",
    "        [\n",
    "            mean_df,\n",
    "            cv_df.rename(columns={'est_ratio':'CV'}),\n",
    "        ],axis=1\n",
    "    )\n",
    "\n",
    "    dif = simu_df.reset_index()\n",
    "    dif = (dif['est_ratio']-dif['true_ratio']).abs()\n",
    "\n",
    "    # simu_df.loc[\n",
    "    #     simu_df.reset_index().loc[\n",
    "    #         (dif/simu_df.reset_index()['true_ratio'])>0.95\n",
    "    #     ].set_index(['n_pick','true_ratio']).index, 'CV'\n",
    "    # ] = 200\n",
    "\n",
    "    simu_df['CV'] = pd.concat(\n",
    "        [\n",
    "            (dif/simu_df.reset_index()['true_ratio'])*100, \n",
    "            simu_df.reset_index()['CV']\n",
    "        ], axis=1\n",
    "    ).max(axis=1).values\n",
    "\n",
    "    s_simu_df = simu_df.copy()\n",
    "\n",
    "    for g,df in simu_df.reset_index().groupby('n_pick'):\n",
    "#         s_df = df.set_index(['n_pick','true_ratio'])\n",
    "#         s_df = df.set_index(['n_pick','true_ratio']).rolling(20, center=True, min_periods=1).mean()\n",
    "        s_df = df.set_index(['n_pick','true_ratio']).rolling(10, center=True, min_periods=1).median()\n",
    "        s_simu_df.loc[s_df.index,'CV'] = s_df['CV']\n",
    "\n",
    "    s_simu_df['CV'] = gaussian_filter(simu_df['CV'], sigma=50)\n",
    "\n",
    "    del (gb)\n",
    "\n",
    "    X, Y = np.meshgrid(log_n_pick, ratios)\n",
    "\n",
    "    z = (\n",
    "        s_simu_df\n",
    "        .loc[ \n",
    "            list( \n",
    "                zip(\n",
    "                    np.power(10, X.flatten()).astype(int),\n",
    "                    [round(i, 3) for i in Y.flatten()],\n",
    "                ) \n",
    "            ), 'CV'].values\n",
    "        .reshape(X.shape)\n",
    "    )\n",
    "\n",
    "    contour_levels = np.concatenate( [ np.arange(0,50,5), np.arange(50,100,10) ])\n",
    "    contour_lws = np.arange(0.5,1.8,0.08)[:contour_levels.shape[0]]\n",
    "\n",
    "    tmp_df1 = pd.concat(\n",
    "        [\n",
    "            (\n",
    "                sum_df.loc[big_cnv_idx]\n",
    "                    .query('Space!=\"chrY\" & Space!=\"chrX\"')\n",
    "                    .groupby(['Cell','Sample']).count()\n",
    "                    .reset_index()['Cell'].str.split('_N',expand=True).groupby(0).count()[1]\n",
    "            ).rename('n_cnv_cell'),\n",
    "            (\n",
    "                mmqc\n",
    "                    .query('meanBin_all<@f1')\n",
    "                    .query('(HMM_Seg_n_all<@f2 | CBS_Seg_n_all<@f2) & (HMM_Seg_n_chr<@f3 | CBS_Seg_n_chr<@f3)')\n",
    "                    .query('MAPD_chr<@f4')\n",
    "                    .index.str.split('_N',expand=True).to_frame().reset_index().groupby(0).count()[1]\n",
    "            ).rename('n_pf'),\n",
    "        ], axis=1\n",
    "    )\n",
    "\n",
    "    tmp_df1 = pd.concat(\n",
    "        [\n",
    "            tmp_df1.reset_index().rename(columns={0:'Lane'}),\n",
    "            pd.Series( tmp_df1.index.str.split('_',expand=True).to_frame()[0].values ).rename('Sample'),\n",
    "            pd.Series( 100*(tmp_df1['n_cnv_cell']/tmp_df1['n_pf']).values ).rename('ratio'),\n",
    "        ], axis=1\n",
    "    )\n",
    "\n",
    "    gb = tmp_df1.groupby('Sample')\n",
    "\n",
    "    tmp_df1 = pd.concat(\n",
    "        [\n",
    "            xlsx_tab.set_index('SAMPLE')['CODE'],\n",
    "            gb.sum()['n_pf'],\n",
    "            gb.mean()['ratio'],\n",
    "            100*(gb.std()['ratio']/gb.mean()['ratio']).rename('CV'),\n",
    "        ], axis=1, sort=False\n",
    "    )\n",
    "\n",
    "    if not FSAMPLE:\n",
    "        tmp_df1 = tmp_df1.drop('$FSAMPLE').replace('M11','M10')\n",
    "    \n",
    "    del (gb)\n",
    "\n",
    "#     tmp_df2 = pd.concat(\n",
    "#         [\n",
    "#             (\n",
    "#                 sum_df.drop(big_cnv_idx)\n",
    "#                     .query('r<@r_acen & (disBfS_M<=0.4 | disBfS_m<=0.4) & Space!=\"chrX\"')\n",
    "#                     .groupby(['Cell','Sample']).count()\n",
    "#                     .reset_index()['Cell'].str.split('_N',expand=True).groupby(0).count()[1]\n",
    "#             ).rename('n_cnv_cell'),\n",
    "#             (\n",
    "#                 mmqc\n",
    "#                     .query('meanBin_all<@f1')\n",
    "#                     .query('(HMM_Seg_n_all<@f2 | CBS_Seg_n_all<@f2) & (HMM_Seg_n_chr<@f3 | CBS_Seg_n_chr<@f3)')\n",
    "#                     .query('MAPD_chr<@f4')\n",
    "#                     .index.str.split('_N',expand=True).to_frame().reset_index().groupby(0).count()[1]\n",
    "#             ).rename('n_pf'),\n",
    "#         ], axis=1\n",
    "#     )\n",
    "\n",
    "#     tmp_df2 = pd.concat(\n",
    "#         [\n",
    "#             tmp_df2.reset_index().rename(columns={0:'Lane'}),\n",
    "#             pd.Series( tmp_df2.index.str.split('_',expand=True).to_frame()[0].values ).rename('Sample'),\n",
    "#             pd.Series( 100*(tmp_df2['n_cnv_cell']/tmp_df2['n_pf']).values ).rename('ratio'),\n",
    "#         ], axis=1\n",
    "#     )\n",
    "\n",
    "#     gb = tmp_df2.groupby('Sample')\n",
    "\n",
    "#     tmp_df2 = pd.concat(\n",
    "#         [\n",
    "#             xlsx_tab.set_index('SAMPLE')['CODE'],\n",
    "#             gb.sum()['n_pf'],\n",
    "#             gb.mean()['ratio'],\n",
    "#             100*(gb.std()['ratio']/gb.mean()['ratio']).rename('CV'),\n",
    "#         ], axis=1, sort=False\n",
    "#     )\n",
    "\n",
    "#     del (gb)\n",
    "\n",
    "#     cmap = plt.cm.OrRd\n",
    "#     cmap = plt.cm.magma_r\n",
    "#     cmap = plt.cm.inferno_r\n",
    "#     cmap = plt.cm.plasma_r\n",
    "    cmap = plt.cm.hot_r\n",
    "#     cmap = plt.cm.afmhot_r\n",
    "\n",
    "#     cs1 = ax1.contourf(Y, X, z, levels=contour_levels, cmap='rainbow', extend='max', zorder=0)\n",
    "    cs1 = ax1.contourf(Y, X+f, z, levels=contour_levels, cmap=cmap, extend='max', zorder=0)\n",
    "    cs2 = ax1.contour( Y, X+f, z, levels=contour_levels, colors='black', linewidths=contour_lws, zorder=0)\n",
    "\n",
    "#     ax1.set_xlim(0,50)\n",
    "#     ax1.set_xticks( np.arange(0,60,10) )\n",
    "\n",
    "    ax1.set_xlim(2.5,9.5)\n",
    "    ax1.set_xticks( [3,6,9] )\n",
    "\n",
    "#     ax1.set_ylim(0.5,4)\n",
    "#     ax1.set_yticks([1,2,3,4])\n",
    "\n",
    "    ax1.set_ylim(1.5,4.5)\n",
    "    ax1.set_yticks([2,3,4])\n",
    "    ax1.set_xlabel( 'CNV Ratio (%)', fontsize=midsize )\n",
    "    ax1.set_ylabel('Number of cells\\n($10^n$)', fontsize=midsize )\n",
    "    ax1.tick_params(axis='both', labelsize=midsize, length=5)\n",
    "\n",
    "    # Female\n",
    "    n_female = tmp_df1['CODE'].str.contains('F0').sum()\n",
    "    ax1.scatter( \n",
    "        x = tmp_df1['ratio'].tail(n_female), \n",
    "        y = np.log10( tmp_df1['n_pf'].tail(n_female) ),  \n",
    "        s = 150,\n",
    "        facecolors = 'None',\n",
    "#         c = cmap(Normalize(0, 90)(tmp_df1['CV'].tail(n_female).values)), \n",
    "        edgecolor = 'k',\n",
    "        lw = 2, \n",
    "        alpha = 0.8,\n",
    "        label = None,\n",
    "        zorder=1,\n",
    "    )\n",
    "    # Male\n",
    "    n_male = tmp_df1['CODE'].str.contains('M').sum()\n",
    "    ax1.scatter( \n",
    "        x = tmp_df1['ratio'].head(n_male), \n",
    "        y = np.log10( tmp_df1['n_pf'].head(n_male) ),  \n",
    "        s = 200,\n",
    "        facecolors = 'None',\n",
    "#         c = cmap(Normalize(0, 90)(tmp_df1['CV'].head(n_male).values)), \n",
    "        edgecolor = 'k',\n",
    "        lw = 2, \n",
    "        marker = 'v',\n",
    "        alpha = 0.8,\n",
    "        label = None,\n",
    "        zorder=1\n",
    "    )\n",
    "    \n",
    "#     ax1.scatter( \n",
    "#         x = tmp_df1['ratio'], \n",
    "#         y = np.log10( tmp_df1['n_pf'] ),  \n",
    "#         s = 150,\n",
    "#         c = cmap(Normalize(0, 90)(tmp_df1['CV'].values)), \n",
    "#         edgecolor = 'k',\n",
    "#         lw = 1, \n",
    "#         alpha = 0.8,\n",
    "#         label = None,\n",
    "#         zorder=1\n",
    "#     )\n",
    "    \n",
    "#     ax1.scatter( \n",
    "#         x = tmp_df2['ratio'], \n",
    "#         y = np.log10( tmp_df2['n_pf'] ),  \n",
    "#         s = 150,\n",
    "#         c = cmap(Normalize(0, 90)(tmp_df2['CV'].values)), \n",
    "#         edgecolor = 'w',\n",
    "#         lw = 1, \n",
    "#         alpha = 0.8,\n",
    "#         label = None,\n",
    "#         zorder=1\n",
    "#     )\n",
    "\n",
    "    cbar = plt.colorbar(cs1, ax2, shrink=0.5, orientation='horizontal')\n",
    "    cbar.ax.set_ylabel('CV (%)', ha='right', va='center', fontsize=bigsize, labelpad=smallsize, rotation=0)\n",
    "    cbar.ax.tick_params(axis='x', labelsize=midsize, length=5)\n",
    "    cbar.add_lines(cs2)\n",
    "    cbar.ax.set_xlim(0,1.2)\n",
    "    \n",
    "#     ax3.scatter(\n",
    "#         x = 5, y = 0, \n",
    "#         s = 150, c = '',\n",
    "#         edgecolor = 'k', lw = 2,\n",
    "#     )\n",
    "\n",
    "#     ax3.scatter(\n",
    "#         x = 5, y = 1, \n",
    "#         s = 150, c = '',\n",
    "#         edgecolor = 'k', lw = 2,\n",
    "#         marker='s',\n",
    "#     )\n",
    "\n",
    "#     ax3.text(\n",
    "#         0, 0, \n",
    "#         r'$\\leqslant$'+' 10M', \n",
    "#         fontsize=midsize, va='center', ha='center'\n",
    "#     )\n",
    "    \n",
    "#     ax3.text(\n",
    "#         0, 1, \n",
    "#         '> 10M',\n",
    "#         fontsize=midsize, va='center', ha='center'\n",
    "#     )\n",
    "\n",
    "#     ax3.set_xlim(-5,10)\n",
    "#     ax3.set_ylim(-1,3)\n",
    "\n",
    "    ax3.axis('off');\n",
    "    \n",
    "    \n",
    "    return s_simu_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1fe51b",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc448e63",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 744.3x631.26 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a4f = 2.5\n",
    "width_a4f = 0.5\n",
    "hight_a4f = 0.3\n",
    "\n",
    "fig = plt.figure( figsize=(a4f*width_a4f*8.27, a4f*hight_a4f*11.69) )\n",
    "gs = mpl.gridspec.GridSpec(nrows=25, ncols=20, figure=fig, hspace=0)\n",
    "\n",
    "ax1  = fig.add_subplot(gs[ 0: 6,   :  ])\n",
    "ax1b = fig.add_subplot(gs[ 6: 7,   :  ])\n",
    "\n",
    "ax2  = fig.add_subplot(gs[ 9:25,  0:7 ])\n",
    "\n",
    "axl  = fig.add_subplot(gs[  9:13, 10:  ])\n",
    "\n",
    "ax3  = fig.add_subplot(gs[ 13:25, 10:  ])\n",
    "ax3c = fig.add_subplot(gs[ 10:11, 14:  ])\n",
    "\n",
    "n_ticks = fig1_barplot([ax1, ax1b], FSAMPLE=False)\n",
    "fig2_plot(ax2, FSAMPLE=False)\n",
    "_ = fig3_rainbow_plot(ax3, ax3c, axl, f=0, FSAMPLE=False)\n",
    "\n",
    "handles, labels = ax1b.get_legend_handles_labels()\n",
    "axl.legend(\n",
    "    handles, labels, \n",
    "    loc='lower left', \n",
    "    bbox_to_anchor=(-0.3, 0),\n",
    "    prop={'size':smallsize}, \n",
    "    handletextpad=0.2, \n",
    "    frameon=False\n",
    ")\n",
    "\n",
    "ax3.scatter(x=0, y=0, s=200, c='white', edgecolor='k', lw=1, marker='v', label='Male')\n",
    "ax3.scatter(x=0, y=0, s=150, c='white', edgecolor='k', lw=1, marker='o', label='Female')\n",
    "\n",
    "ax3.legend(\n",
    "    loc='upper right',\n",
    "    prop={'size':smallsize}, \n",
    "    handletextpad=0.2, \n",
    "    frameon=False,\n",
    "    markerfirst=False\n",
    ")\n",
    "\n",
    "fig.savefig('A4_fig1.pdf', frameon=False, transparent=True, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2501b41",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61bde049",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbb9064d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3621e07b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdb7ff74",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}