From 5ad15c950846161c4ef1f1d9fc52979c60b0e2c6 Mon Sep 17 00:00:00 2001 From: Shantanu Singh Date: Fri, 19 Apr 2024 10:41:53 -0400 Subject: [PATCH] Update analysis --- 5.reanalysis/7.replicability_map.ipynb | 194 ++++++++++++++++++ .../metrics/map_nonrep.parquet | 4 +- 2 files changed, 196 insertions(+), 2 deletions(-) create mode 100644 5.reanalysis/7.replicability_map.ipynb diff --git a/5.reanalysis/7.replicability_map.ipynb b/5.reanalysis/7.replicability_map.ipynb new file mode 100644 index 0000000..e62741e --- /dev/null +++ b/5.reanalysis/7.replicability_map.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Replicability" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from statsmodels.stats.multitest import multipletests" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "ap_path = \"output/analysis_results/var_mad_int_featselect/metrics/ap_nonrep.parquet\"\n", + "map_path = \"output/analysis_results/var_mad_int_featselect/metrics/map_nonrep.parquet\"\n", + "\n", + "map_scores = pd.read_parquet(map_path)\n", + "\n", + "map_p_value_threshold = 0.05\n", + "\n", + "\n", + "reject, pvals_corrected, alphacSidak, alphacBonf = multipletests(\n", + " map_scores[\"p_value\"], method=\"fdr_bh\"\n", + ")\n", + "\n", + "\n", + "def correct_pvalues(group):\n", + " reject, pvals_corrected, alphacSidak, alphacBonf = multipletests(\n", + " group[\"p_value\"], method=\"fdr_bh\"\n", + " )\n", + " group[\"p_value_corrected\"] = pvals_corrected\n", + " return group\n", + "\n", + "\n", + "map_scores[\"Metadata_Plate_dummy\"] = map_scores[\"Metadata_Plate\"]\n", + "\n", + "map_scores = map_scores.groupby(\"Metadata_Plate_dummy\").apply(\n", + " correct_pvalues, include_groups=False\n", + ")\n", + "\n", + "map_scores[\"below_p\"] = map_scores[\"p_value\"] < map_p_value_threshold\n", + "map_scores[\"below_corrected_p\"] = (\n", + " map_scores[\"corrected_p_value\"] < map_p_value_threshold\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "map_scores[\"neglog10_p_value_corrected\"] = -np.log10(map_scores[\"p_value_corrected\"])\n", + "map_scores[\"neglog10_p_value\"] = -np.log10(map_scores[\"p_value\"])\n", + "map_scores[\"cell_plate\"] = (\n", + " map_scores[\"Metadata_cell_type\"] + \":\" + map_scores[\"Metadata_Plate\"].astype(str)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7774r0dHR6uPmnJvM+zuY9RrmvYZ5r2HeN6yl5D2zXsO81zDvNcx7DfO+vpaS9dR6WIiIPO4JTyIiIiIiIiIiImr5WuU9G4mIiIiIiIiIiKjpcbKRiIiIiIiIiIiIGgUnG4mIiIiIiIiIiKhRcLKRiIiIiIiIiIiIGgUnG4mIiIiIiIiIiKhRcLKRiIiIiIiIiIiIGgUnG4mIiIiIiIiIiKhRcLKRiJqNcePGYdy4cWYbT0REzQPznojoycC8J3oyWYiIPO4iiIgA4OzZswAAd3d3s4wnIqLmgXlPRPRkYN4TPZk42UhERERERERERESNgpdRk0nCwsKwZs0aJCYmIjg4GH5+fpgzZw5+/fVX6PV6DBkyBP369cPMmTNRWlqqrvfxxx/jhRdeQN++fRESEoLVq1ejpqbGaNsff/wxIiMj4evrC51Oh4iICHz22Wfq89u3b4eXlxdOnDiBV155Bd7e3ggJCUFaWprJx1FbWwu9Xo/Ro0dDp9PB19cXY8eORUFBAQDgm2++gYeHB7788kuj9f73v//Bw8MDn3/+OQCgqqoKS5cuxdChQ9G3b1/86U9/Mqq5rmfvv/8+3njjDfj7+yM+Ph4A8MMPP2DGjBno378/+vTpg+eeew5LlixBZWWlum55eTni4+MxYMAA+Pn5ISYmBunp6fDw8DDax5dffonIyEh4e3tj0KBBWLJkCSoqKkzqSVFRETw8PLBnzx68/fbb8PHxwdChQ7F69WoYDIbfPJ7r168jPj4eAwcOhLe3N15++WW1n3Vu376NlJQUDBs2DDqdDi+88AKys7PV5++9bCI/Px+vvPIK/Pz8EBgYiGnTpuHcuXP3HV9VVYWUlBSMHDkS3t7eeP7556HX643qHzduHObPnw+9Xo+QkBB4e3tj7NixOHHihEn9ImrtmPfMe+Y90ZOBec+8Z94TUaMTIhOEhoaKv7+/zJgxQw4ePCipqamiKIqMGDFCxo8fL/v375eNGzdK7969ZeHChSIikpqaKh4eHvLee+/J119/LXq9Xry9veVvf/ubut3MzEzx9PSUNWvWyKFDh2Tv3r3y0ksviZeXl1y8eFFERLKzs8XDw0NCQkIkPT1d8vPzJTY2VhRFkQMHDph0HB988IHodDrJyMiQw4cPyyeffCLPP/+8BAYGyq+//ioiIsOHD5eYmBij9VasWCH9+vWTyspKMRgMMnnyZPHz85ONGzfKgQMH5B//+IcoiiI7duww6pmXl5ckJCRIXl6eHD16VIqLi8Xf318mTZokubm5cvDgQUlISBBFUWTdunXquuPHj5eAgADJysqS3NxcmTJlivTt21cURVHHfPrpp6IoisyZM0e++uor2bx5swQGBsobb7whBoPhoXtSWFgoiqJIQECAuq3ly5eLp6enJCYmPvB4KisrZcyYMTJw4EDZunWr7N+/X2bOnCleXl6Sn5+vrhsTEyM6nU7WrVsn+fn58s9//tOoX9HR0RIdHS0iIhcuXBCdTieLFi2SgoIC2bt3r4wYMULCw8Oltra23niDwSATJkwQX19fSUtLk7y8PElOTpbevXvL3//+d7WG6Oho6devn7z88svyxRdfyL59+yQ8PFyGDBkiNTU1D90votaOec+8Z94TPRmY98x75j0RNTZONpJJQkND5bnnnpPbt2+ry0aMGCF+fn5y8+ZNddnUqVNlzJgxcvPmTfHx8ZH4+Hij7WzdulUURZHTp0+LiEhiYqIsXbrUaMx3330niqLIrl27ROTOyYiiKLJ161Z1TFVVlXh7e8vixYtNOo7Y2FjZuHGj0bKcnBxRFEW++eYbERFZvXq1+Pr6SkVFhTpm2LBhMn/+fBERycvLE0VRZM+ePUbbmTt3rgwaNEjtUWhoqISEhKgvoCIiX3/9tbz++utSVlZmtO7o0aNl0qRJIiKSn58viqJITk6O+nxtba2MGjVKPRkxGAwyZMgQmTx5stF26tbNzc196J7UnYyMHz/eaPmSJUukT58+cuPGjfsez5YtW0RRFDl+/Li6zGAwyOuvvy6RkZEiInL69GlRFEU2bdpktP1Zs2ZJXFyciBifXOzevVsURZHLly+rY0+cOCHLly9X+3b3+P3794uiKPLJJ58YbT8lJUUURZEzZ86o6/j4+Bj1fseOHaIoipw6deqh+0XU2jHvmffMe6InA/Oeec+8J6LGxsuoyWQ6nQ7W1tbq4y5duqBnz55wdHRUl7Vv3x5lZWX49ttvcevWLYSFhaGmpkb9CQsLAwAcPHgQABAXF4d33nkHZWVlOHXqFHbt2oWsrCwAdz6afzc/Pz/1d1tbW3Ts2NHkSwqSk5MxYcIElJSU4Ntvv8X27dvx6aefGu0vIiICFRUVyM3NBQCcPHkSFy5cQEREBACgoKAAFhYWGDp0aL1ju3r1Ks6cOaPur1evXrC01P7dBg8ejMzMTNjZ2eGnn35Cbm4uUlNTUVJSgurqagDAoUOHYGNjg2HDhqnrWVpa4o9//KP6+Ny5c7h8+XK9/gYGBqJt27Zqf00xZswYo8cjRozA7du3cfz48fseT0FBAbp06YI+ffqoNdTW1iI0NBTfffcdbty4gaNHjwIAhg8fbrT9lStXIjExsV4dPj4+sLOzQ1RUFBITE5Gfnw9PT0/ExMSgbdu29cYfOXIEVlZWGDVqVIPHc/jwYXWZu7u70Ta6du0KALh169YDe0P0pGHeM++Z90RPBuY98555T0SNyfq3hxAZa+iFoE2bNg2OvX79OgDgrbfeavD5K1euAAAuXLiA+Ph4HDp0CNbW1ujZs6d63xK55zuM7O3tjR5bWlrWG/NbTp06hUWLFuHUqVOwt7eHu7s7XF1djfbXrVs3+Pv7Y8+ePRg1ahR27doFV1dXBAQEqMcmIvD397/vsfXu3RsA0LlzZ6PnDAYDli9fjqysLFRUVMDFxQU6nQ52dnbqmNLSUrRv397oRf/ebdX1d9GiRVi0aFGDNZjq6aefNnrcsWNHAMDNmzcbrKGujqtXr6JPnz4NbvPq1atqrZ06dXqoOtzc3JCZmQm9Xo+tW7ciPT0d7dq1w2uvvYZZs2bV68uNGzfQoUMHoxNl4M7JMgCUlZWpy+79e63b1t33fiEi5j3znnlP9KRg3jPvmfdE1Jg42Uhm1a5dOwBAUlISunfvXu/5zp07w2Aw4K233oKNjQ22bt0KLy8vWFtb4+zZs+q7kY2pvLwcb775Jjw8PLB79271XbyvvvoKOTk5RmMjIiKQkJCAsrIyfP7553jppZdgYWEBAHB0dISDgwMyMjIa3M+zzz573xr0ej3S09OxcOFCjBgxQn3XOCoqSh3TtWtXlJaWwmAwGL3wXrt2Tf29rr/z5s1DUFBQvf04OTn9VjvqqTtpuHd/DzqJcHR0RPfu3ZGUlNTg825ubmqtJSUlcHZ2Vp87d+4cSkpK1JO8u+l0OqxZswbV1dU4duwYtmzZgtTUVHh4eNR7h9PJyQmlpaWoqakxOiGpOyHr0KHDA46aiH4v5n3DmPfMe6LWhnnfMOY9856INLyMmszKx8cHNjY2KC4uhre3t/pjY2OD5ORkFBUVobS0FD/99BOioqKMLuE4cOAAgMZ/N+rcuXO4fv06xo8fjz/84Q/qC31D+6u7pGHVqlW4evWq0SUIQUFBqKiogIgYHduZM2eQkpJS79v47nbs2DG4u7sjKipKPREpLi7G6dOn1f0HBQWhpqYG//3vf43Wvfsb9Hr27IlOnTqhqKjIqAZnZ2ckJyfj+++/N7k/9+4vJycHbdq0gY+Pz33XCQoKwqVLl9CpUyejOgoKCrBhwwZYWVmhX79+9eoHgBUrVuC9996rt8309HSEhYWhuroatra2GDBggDru0qVLDdZQW1tb79sC605o6/ZPRObBvG8Y817DvCdqHZj3DWPea5j3RMRPNpJZdejQAW+++SZWrVqF8vJyBAcHo7i4GKtWrYKFhQU8PT3h6OgIV1dXZGVlwdnZGe3atUNeXh42bdoEoPHvs9GjRw+0bdsWqampsLa2hrW1NXJycrBt27Z6+3NyckJoaCg2b94Mb29v9OrVS31u6NChCAwMxLRp0zBt2jT06tULJ0+exOrVqzF48GD18oSG6HQ6rF27Fnq9Hr6+vjh//jzWr1+P6upqdf+BgYEYNGgQ5s+fj19++QXPPPMMtm3bhh9++EF999XKygoxMTGIj4+HlZUVQkNDcfPmTaxduxbFxcX3vezhQfbu3YvOnTtj6NChOHLkCLKyshATEwMHB4f7rhMZGYnMzExMnDgRb7/9NlxcXJCfn4+0tDRER0fDxsYGnp6eGDlyJJKSklBZWYk+ffogLy8PX3zxBVauXFlvm/3790dSUhKmT5+O6OhoWFlZ4T//+Q9sbW0RGhpab/yQIUMQHByMBQsW4MqVK/Dy8sKRI0eQlpaGP//5z3B3dze5F0T08Jj3DWPeM++JWhvmfcOY98x7ItJwspHMbvbs2ejSpQs2b96MDRs2wMnJCQMGDEBsbKz6rt/atWuRkJCAuLg42Nrawt3dHevWrcP777+Po0ePYty4cY1Wj6OjI9auXYulS5di1qxZeOqpp9C7d29kZmZiypQpOHr0qHqDa+DODYhzcnLq3VjZ0tISer0eq1atwvr163Ht2jV07doVEyZMwPTp0x9Yw9SpU1FaWoqMjAykpKTAxcUFERERsLCwwPr163Hjxg04OTlhxYoV+OCDD5CcnIyamhqEh4fj1Vdfxc6dO9Vt/eUvf8FTTz2FDRs2YMuWLXBwcIC/vz+SkpLQrVs3k/sza9YsHDlyBFu2bIGLiwvi4+Px6quvPnAdBwcHZGVlITk5GcuWLUNZWRlcXV0xZ84cTJo0SR23bNkyrFmzBv/+979RWlqKHj16YOXKlRg5cmS9bXp6eiI1NRUpKSmIjY1FbW0t+vbti48++gg9e/asN76udx9++CEyMjJQUlICNzc3xMTEYOLEiSb3gYhMx7yvj3nPvCdqjZj39THvmfdEpLEQU++8S0RN4uLFizh+/DjCw8ONbpr917/+FYWFhdixY0ej7q+oqAjh4eFITExEZGRko26biIjuj3lPRPRkYN4T0ZOCn2ykVsNgMDzU/V+srKzUyxSaM0tLS8TFxSE8PBxRUVGwsrLCgQMHsG/fPiQmJj70dmpra3/z2/xaQj+IiOow7xvGvCei1oZ53zDmPRE1d5xspFYjJSUFa9as+c1xGRkZCA4OboKKfh8XFxekpaUhJSUFs2fPRk1NDXr16oWkpCSMHj36obczfPhwXLx48YFjXF1d7/ute0REzQ3zvmHMeyJqbZj3DWPeE1Fzx8uoqdUoLi7GlStXfnNc3Q2knxQ//vgjqqurHzjG1tYWHh4eTVQREdHvw7xvGPOeiFob5n3DmPdE1NxxspGIiIiIiIiIiIgaheXjLoCIiIiIiIiIiIhaB042EhERERERERERUaPgZCMRERERERERERE1Ck42EhERERERERERUaPgZCMRERERERERERE1Ck42EhERERERERERUaPgZCMRERERERERERE1Ck42EhERERERERERUaP4P8OIIeiGOYA8AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set up the plotting environment\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "\n", + "# Create a custom marker function to handle different markers for Metadata_line_source\n", + "def scatter_custom(x, y, **kwargs):\n", + " markers = {\"human\": \"o\", \"animal\": \"^\"}\n", + " marker = markers.get(kwargs[\"label\"], \"o\") # Default to \"o\" if not found\n", + " sns.scatterplot(x=x, y=y, marker=marker, **kwargs)\n", + "\n", + "\n", + "# Create the scatter plot using custom function\n", + "g = sns.FacetGrid(\n", + " map_scores,\n", + " col=\"cell_plate\",\n", + " hue=\"Metadata_line_condition\",\n", + " palette=\"muted\",\n", + " col_wrap=3,\n", + " height=4,\n", + ")\n", + "\n", + "g.map(scatter_custom, \"mean_average_precision\", \"neglog10_p_value_corrected\", alpha=0.7)\n", + "\n", + "# Add a legend and adjust the plot\n", + "g.add_legend(title=\"Line Condition\")\n", + "g.figure.subplots_adjust(top=0.9)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set up the plotting environment\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "\n", + "# Create a custom marker function to handle different markers for Metadata_line_source\n", + "def scatter_custom(x, y, **kwargs):\n", + " markers = {\"human\": \"o\", \"animal\": \"^\"}\n", + " marker = markers.get(kwargs[\"label\"], \"o\") # Default to \"o\" if not found\n", + " sns.scatterplot(x=x, y=y, marker=marker, **kwargs)\n", + "\n", + "\n", + "# Create the scatter plot using custom function\n", + "g = sns.FacetGrid(\n", + " map_scores,\n", + " col=\"cell_plate\",\n", + " hue=\"Metadata_line_condition\",\n", + " palette=\"muted\",\n", + " col_wrap=3,\n", + " height=4,\n", + ")\n", + "\n", + "g.map(scatter_custom, \"neglog10_p_value\", \"neglog10_p_value_corrected\", alpha=0.7)\n", + "\n", + "# Add a legend and adjust the plot\n", + "g.add_legend(title=\"Line Condition\")\n", + "g.figure.subplots_adjust(top=0.9)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ncp", + "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": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/5.reanalysis/output/analysis_results/var_mad_int_featselect/metrics/map_nonrep.parquet b/5.reanalysis/output/analysis_results/var_mad_int_featselect/metrics/map_nonrep.parquet index a388a67..10309b0 100644 --- a/5.reanalysis/output/analysis_results/var_mad_int_featselect/metrics/map_nonrep.parquet +++ b/5.reanalysis/output/analysis_results/var_mad_int_featselect/metrics/map_nonrep.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5eb53cbf6aa742d18fe23aa3d3ad3e9c26de78b63bd851bbfc1df6b5a52d3bb0 -size 10960 +oid sha256:3af8f4c1fbb6fcea2d57d19ba1fafc5c73d36ecfd2d5af74fcabb6cbe64eaca9 +size 14070