2b_extra_bar_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/quanti/Desktop/HSC/Generational_multiplex_analysis_2_backup_copy_20200714/\n"
     ]
    }
   ],
   "source": [
    "import itertools as itt\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import scipy.optimize as spo\n",
    "import scipy.special as spsp\n",
    "import scipy.stats as sps\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "mpl.rcParams['axes.titlesize'] = 'xx-large'\n",
    "mpl.rcParams['axes.labelsize'] = 'xx-large'\n",
    "mpl.rcParams['xtick.labelsize'] = 'x-large'\n",
    "mpl.rcParams['ytick.labelsize'] = 'x-large'\n",
    "mpl.rcParams['xtick.direction'] = 'out'\n",
    "mpl.rcParams['ytick.direction'] = 'out'\n",
    "mpl.rcParams['legend.frameon'] = True\n",
    "mpl.rcParams['legend.framealpha'] = 0.5\n",
    "mpl.rcParams['legend.fontsize'] = 'large'\n",
    "\n",
    "path_proj = os.getcwd()+ '/'\n",
    "path_sc = path_proj + 'csv/Single_cell/'\n",
    "path_sort = path_proj + 'csv/Sort/'\n",
    "path_plot = path_proj + 'figures/'\n",
    "\n",
    "print(path_proj)\n",
    "\n",
    "#LOAD AND CLEAN THE DATA\n",
    "\"\"\"\n",
    "dfr = pd.read_excel(path_proj+'csv/Culture_results.xlsx', sheet_name='Sheet3')#, sep=';', decimal=',')\n",
    "for k in range(4,dfr.shape[1],3):\n",
    "    dfr.iloc[:,k:k+3] = dfr.iloc[:,k-3:k].values + dfr.iloc[:,k:k+3].values\n",
    "\"\"\"    \n",
    "dfpcp = pd.read_excel(path_proj+'csv/Culture_results_CEFFCFC.xlsx',\n",
    "                      sheet_name='Prop_colonies_CP')\n",
    "for k in range(4,dfpcp.shape[1],3):\n",
    "    dfpcp.iloc[:,k:k+3] = dfpcp.iloc[:,k-3:k].values + dfpcp.iloc[:,k:k+3].values\n",
    "dfpcp = dfpcp.drop(4,axis=0)\n",
    "\n",
    "dfpme = pd.read_excel(path_proj+'csv/Culture_results_CEFFCFC.xlsx',\n",
    "                      sheet_name='Prop_colonies_methyl_C')\n",
    "for k in range(4,dfpme.shape[1],3):\n",
    "    dfpme.iloc[:,k:k+3] = dfpme.iloc[:,k-3:k].values + dfpme.iloc[:,k:k+3].values\n",
    "\n",
    "dfecp = pd.read_excel(path_proj+'csv/Culture_results_CEFFCFC.xlsx',\n",
    "                      sheet_name='cloning efficiency_CP')\n",
    "dfecp = dfecp.drop(['GMP'], axis=1)\n",
    "\n",
    "dfeme = pd.read_excel(path_proj+'csv/Culture_results_CEFFCFC.xlsx',\n",
    "                      sheet_name='Cloning  efficiency_methyl_fina')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MK', 'CFU-MK', 'Mixed with MK'], dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PROCESSING\n",
    "lst_cols = {}\n",
    "lst_cols_std = {}\n",
    "\n",
    "for k in range(1,dfpcp.shape[1],3):\n",
    "    columns = dfpcp.columns[k:k+3]\n",
    "    label = dfpcp.columns[k][:-1]\n",
    "    means = dfpcp.loc[:,columns].mean(axis=1)\n",
    "    dct_col = {label:means.to_list()}\n",
    "    dct_col_std = {label:dfpcp.loc[:,columns].std(axis=1).to_list()}\n",
    "    lst_cols.update(dct_col)\n",
    "    lst_cols_std.update(dct_col_std)\n",
    "df = pd.DataFrame.from_dict(lst_cols)\n",
    "df.index = dfpcp.iloc[:,0].to_list()\n",
    "df_std = pd.DataFrame.from_dict(lst_cols_std)\n",
    "df_std.index = dfpcp.iloc[:,0].to_list()\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TZlYB6EnYJVpERHIRv0IuJtEr5ESSQX4Sl+HA22b2HPBEdOxwM6tGmP9SBziv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\n5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = sns.color_palette('GnBu', n_colors=5)\n",
    "colors = [colors[0], colors[1], colors[-1]]\n",
    "xlabel = 'Progenitor'\n",
    "ylabel = 'CFC type (%)'\n",
    "legend_title = 'Cell type'\n",
    "legend_classes = df.columns.to_list()\n",
    "legend_cols = colors\n",
    "cat_width = 0.7\n",
    "fig, ax = plt.subplots()\n",
    "n_cat = len(df)\n",
    "for nc,cat_k in enumerate(df.index):\n",
    "    x_cat = -cat_width/2 + nc\n",
    "    y = df.loc[cat_k].values\n",
    "    ci = df_std.loc[cat_k].values\n",
    "    bar_width = cat_width\n",
    "    bottoms = np.hstack([np.zeros(1),y[:-1]])\n",
    "    x_ci = [x_cat+k*bar_width/(len(y)) for k in np.arange(1,len(y))]\n",
    "    if len(ci) != 0:\n",
    "        prev_y = y[0]\n",
    "        for k in np.arange(len(y)-1):\n",
    "            if k == 0 or y[k] != prev_y:\n",
    "                ax.plot([x_ci[k]]*3, [max(y[k]-ci[k], 0), y[k], min(y[k]+ci[k], 100)],\n",
    "                        ls='-', marker='_', ms=5., lw=1., color='Black')#, color='Silver')\n",
    "            prev_y = y[k]\n",
    "    ax.bar(x=[x_cat]*len(y), height=y-bottoms,\n",
    "           width=[bar_width]*len(y), bottom=bottoms, align='edge',\n",
    "           color=colors)\n",
    "handles = [mpatches.Patch(color=co, label=cl) for cl,co in zip(legend_classes,legend_cols)]\n",
    "ax.legend(handles=handles, loc='upper left', ncol=1, bbox_to_anchor= (1.05, 1.), title=legend_title)\n",
    "\n",
    "ax.set_xticks(np.arange(n_cat, dtype=int))\n",
    "ax.set_xticklabels(df.index.to_list(), rotation=45)\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_xlabel(xlabel)\n",
    "ax.set_ylabel(ylabel)\n",
    "ax.set_yticks(np.linspace(0,100,5))\n",
    "ax.set_yticklabels(np.linspace(0,100,5, dtype=int))\n",
    "sns.despine(fig=fig)\n",
    "fig.savefig(path_plot+'FigPCP.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MK', 'CFU-MK', 'GM', 'BFU-E', 'Mixed'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PROCESSING\n",
    "lst_cols = {}\n",
    "lst_cols_std = {}\n",
    "\n",
    "for k in range(1,dfpme.shape[1],3):\n",
    "    columns = dfpme.columns[k:k+3]\n",
    "    label = dfpme.columns[k][:-1]\n",
    "    means = dfpme.loc[:,columns].mean(axis=1)\n",
    "    dct_col = {label:means.to_list()}\n",
    "    dct_col_std = {label:dfpme.loc[:,columns].std(axis=1).to_list()}\n",
    "    lst_cols.update(dct_col)\n",
    "    lst_cols_std.update(dct_col_std)\n",
    "df = pd.DataFrame.from_dict(lst_cols)\n",
    "df.index = dfpme.iloc[:,0].to_list()\n",
    "df_std = pd.DataFrame.from_dict(lst_cols_std)\n",
    "df_std.index = dfpme.iloc[:,0].to_list()\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jr2XLlu3WuXPnudtq26JFi5XNmjVbNG/evCbDhg37X+z4N998U2PWrFkHVKtW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\nljkoMDMzMwD+B0NZ6AcygAeeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = sns.color_palette('GnBu', n_colors=5)#sns.color_palette('Accent')[1:6]\n",
    "xlabel = 'Progenitor'\n",
    "ylabel = 'CFC type (%)'\n",
    "legend_title = 'Cell type'\n",
    "legend_classes = df.columns.to_list()\n",
    "legend_cols = colors\n",
    "cat_width = 0.7\n",
    "fig, ax = plt.subplots()\n",
    "n_cat = len(df)\n",
    "for nc,cat_k in enumerate(df.index):\n",
    "    x_cat = -cat_width/2 + nc\n",
    "    y = df.loc[cat_k].values\n",
    "    ci = df_std.loc[cat_k].values\n",
    "    bar_width = cat_width\n",
    "    bottoms = np.hstack([np.zeros(1),y[:-1]])\n",
    "    x_ci = [x_cat+k*bar_width/(len(y)) for k in np.arange(1,len(y))]\n",
    "    if len(ci) != 0:\n",
    "        prev_y = y[0]\n",
    "        for k in np.arange(len(y)-1):\n",
    "            if k == 0 or y[k] != prev_y:\n",
    "                ax.plot([x_ci[k]]*3, [max(y[k]-ci[k], 0), y[k], min(y[k]+ci[k], 100)],\n",
    "                        ls='-', marker='_', ms=5., lw=1., color='Black')#, color='Silver')\n",
    "            prev_y = y[k]\n",
    "    ax.bar(x=[x_cat]*len(y), height=y-bottoms,\n",
    "           width=[bar_width]*len(y), bottom=bottoms, align='edge',\n",
    "           color=colors)\n",
    "handles = [mpatches.Patch(color=co, label=cl) for cl,co in zip(legend_classes,legend_cols)]\n",
    "ax.legend(handles=handles, loc='upper left', ncol=1, bbox_to_anchor= (1.05, 1.), title=legend_title)\n",
    "\n",
    "ax.set_xticks(np.arange(n_cat, dtype=int))\n",
    "ax.set_xticklabels(df.index.to_list(), rotation=45)\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_xlabel(xlabel)\n",
    "ax.set_ylabel(ylabel)\n",
    "ax.set_yticks(np.linspace(0,100,5))\n",
    "ax.set_yticklabels(np.linspace(0,100,5, dtype=int))\n",
    "sns.despine(fig=fig)\n",
    "fig.savefig(path_plot+'FigPME.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cloning efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BgyPizQae18zMmqDbhvWPHSgJ+DWwPR/11Fo7Iq6SNCPwFDAmIn7RX8FOLjesWzvz58Da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\nFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#def plot_bars(dct_plot, cols_dct, xlabel, ylabel,\n",
    "#              legend_title, legend_classes, legend_cols,\n",
    "#              cat_width=0.7, str_sep='   '):\n",
    "#cols_dct\n",
    "colors = ['Red', 'Limegreen', 'seagreen', 'Deepskyblue', 'grey']\n",
    "#             'SLAM+ Flt3+':'seagreen', 'GMP':'aqua', 'MEP':'Deepskyblue', 'SLAM+ MEP':'Royalblue',\n",
    "#             'MP':'DarkViolet', 'CD16/32- ckit-':'Violet'}\n",
    "#['MK', 'CFU-MK', 'GM', 'BFU-E', 'Mixed']\n",
    "xlabel = 'Progenitor'\n",
    "ylabel = 'MK cloning efficiency (%)'\n",
    "legend_title = 'Cell type'\n",
    "legend_classes = df.columns.to_list()\n",
    "legend_cols = colors\n",
    "cat_width = 0.7\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for k,col in enumerate(dfecp.columns):\n",
    "    dfecp.loc[:,col]\n",
    "    x = k - cat_width/2\n",
    "    y = dfecp.loc[:,col].mean()\n",
    "    ci = dfecp.loc[:,col].std()\n",
    "    ax.plot([k]*2, [max(y-ci, 0), min(y+ci, 100)],\n",
    "            ls='-', marker='_', ms=5., lw=1., color='Black')#, color='Silver')\n",
    "    ax.bar(x=x, height=y,\n",
    "           width=cat_width, bottom=0, align='edge',\n",
    "           color=colors[k])#, label=col)\n",
    "#ax.legend(handles=handles, loc='upper left', ncol=1, bbox_to_anchor= (1.05, 1.), title=legend_title)\n",
    "\n",
    "ax.set_xticks(np.arange(len(dfecp.columns), dtype=int))\n",
    "#xtickslabels = [str_sep.join(dct_plot[cat_k].keys())+'\\n'+cat_k for cat_k in dct_plot.keys()]\n",
    "ax.set_xticklabels(dfecp.columns.to_list(), rotation=45)\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_xlabel(xlabel)\n",
    "ax.set_ylabel(ylabel)\n",
    "#ax.set_yticks(np.linspace(0,100,5))\n",
    "#ax.set_yticklabels(np.linspace(0,100,5, dtype=int))\n",
    "sns.despine(fig=fig)\n",
    "#return fig,ax\n",
    "fig.savefig(path_plot+'FigCEFF.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MOk4zCePjwMHAnMApwL1l//uBLwL/AY4ElgB2AX4vadOIuKh14ZqZWbs0kzBWA2YAlo+I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\nsEIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#def plot_bars(dct_plot, cols_dct, xlabel, ylabel,\n",
    "#              legend_title, legend_classes, legend_cols,\n",
    "#              cat_width=0.7, str_sep='   '):\n",
    "#cols_dct\n",
    "colors = ['Red', 'Limegreen', 'seagreen', 'Deepskyblue', 'aqua']\n",
    "#             'SLAM+ Flt3+':'seagreen', 'GMP':'aqua', 'MEP':'Deepskyblue', 'SLAM+ MEP':'Royalblue',\n",
    "#             'MP':'DarkViolet', 'CD16/32- ckit-':'Violet'}\n",
    "#['MK', 'CFU-MK', 'GM', 'BFU-E', 'Mixed']\n",
    "xlabel = 'Progenitor'\n",
    "ylabel = 'Total CFC cloning efficiency (%)'\n",
    "legend_title = 'Cell type'\n",
    "legend_classes = df.columns.to_list()\n",
    "legend_cols = colors\n",
    "cat_width = 0.7\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for k,col in enumerate(dfeme.columns):\n",
    "    dfeme.loc[:,col]\n",
    "    x = k - cat_width/2\n",
    "    y = dfeme.loc[:,col].mean()\n",
    "    ci = dfeme.loc[:,col].std()\n",
    "    ax.plot([k]*2, [max(y-ci, 0), min(y+ci, 100)],\n",
    "            ls='-', marker='_', ms=5., lw=1., color='Black')#, color='Silver')\n",
    "    ax.bar(x=x, height=y,\n",
    "           width=cat_width, bottom=0, align='edge',\n",
    "           color=colors[k])#, label=col)\n",
    "#ax.legend(handles=handles, loc='upper left', ncol=1, bbox_to_anchor= (1.05, 1.), title=legend_title)\n",
    "\n",
    "ax.set_xticks(np.arange(len(dfeme.columns), dtype=int))\n",
    "#xtickslabels = [str_sep.join(dct_plot[cat_k].keys())+'\\n'+cat_k for cat_k in dct_plot.keys()]\n",
    "ax.set_xticklabels(dfeme.columns.to_list(), rotation=45)\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_xlabel(xlabel)\n",
    "ax.set_ylabel(ylabel)\n",
    "#ax.set_yticks(np.linspace(0,100,5))\n",
    "#ax.set_yticklabels(np.linspace(0,100,5, dtype=int))\n",
    "sns.despine(fig=fig)\n",
    "#return fig,ax\n",
    "fig.savefig(path_plot+'FigMEEFF.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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