{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Target gene and neighbouring gene expression\n",
    "\n",
    "1. Run through the 10x Cellranger pipeline and velocyto for single cell RNAseq quatification and using (2) guides quantifiction. all found in the cellranger files folder **bash**\n",
    "2.  Guide Calling for dual guide. Use repogle method to take molecule.h5 generated by cellranger and py to run through repogle version of guide calling or use cellranger_guidecalling.ipynb for Direct Capture Perturb-Seq dual guide. Formed guide-specific lists of cells.\n",
    "3. Pseudobulk analysis.\n",
    "    A. Seperation of guide-specific fastq files. **bash**\n",
    "    B. Whippet pseudobulk for transcript specific analysis, post UMI deduplication.  **bash**\n",
    "    C. Transcript quality control. **R**\n",
    "    D. Whippet result visualisation.\n",
    "4. Normalisation of adata object and E-distance of KD\n",
    "5. **Check gene and neighboring gene expression**\n",
    "6. Create individual umaps per gene of interest \n",
    "    A. UMAPs \n",
    "    B. Rand Index score\n",
    "7. Cell phase assignment model from FUCCI-matched single cell paper (GSE146773_)\n",
    "8. Differential Expression analysis.\n",
    "    A. Find the shared P1 and P2 genes. \n",
    "    B. Check the shared P1 and P2 across different protospacers with the same A/B and C/D.\n",
    "9.  CNV Score & Numbat to quantify and Velocity quantification with loom file\n",
    "10. ESR1-specific analysis from proliferation analysis to rt-qpcr\n",
    "11.  Spectra analysis and visualisation for pathway enrichment\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook, 5_genekd_neighbouring_gene_expression.ipynb, focuses on the specificity and quality control of the CRISPR interference (CRISPRi) perturbations. Specifically, it ensures that the observed transcriptomic changes are caused by the targeted gene knockdown and not by accidental silencing of nearby \"neighbouring\" genes.\n",
    "\n",
    "\n",
    "\n",
    "Here is a breakdown of the computational steps and biological significance of this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helenking/anaconda3/envs/apu/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scanpy 1.10.3\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%matplotlib inline\n",
    "%autoreload 2\n",
    "#general\n",
    "import scanpy as sc\n",
    "import matplotlib.pyplot as pl\n",
    "import anndata as ad\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import hdf5plugin\n",
    "import warnings\n",
    "#form a location\n",
    "loc=\"alt-prom-crispr-fiveprime/\"\n",
    "\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm\n",
    "import scperturb\n",
    "import sys\n",
    "sys.path.append(loc+'scripts/')\n",
    "from apu_analysis import *\n",
    "import scperturb\n",
    "import infercnvpy as cnv\n",
    "from apu_analysis.cell_import import CellPopulation\n",
    "from IPython.display import clear_output\n",
    "pd.options.display.float_format = '{:.4f}'.format\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#for this python\n",
    "from scipy.special import rel_entr\n",
    "import sklearn.cluster as cluster\n",
    "import umap\n",
    "from scipy import stats\n",
    "from scipy.stats import bootstrap\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from numpy import reshape\n",
    "from numpy import array\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.patches as mpatches\n",
    "# from statannotations.Annotator import Annotator\n",
    "\n",
    "\n",
    "# Taken from: \n",
    "# Adamson, B.A., Norman, T.M., *et al.* \"A multiplexed CRISPR screening platform enables systematic dissection of the unfolded protein response\", *Cell*, 2016.\n",
    "# My experiment deals with two KDs- one of the MP, one of the AP using two guides. Positve controls include GINS1 ect. This is a combnatorial KD double for the same gene. No treatments were used\n",
    "\n",
    "# colours using garvan \n",
    "color1 ='#4d00c7'\n",
    "palecolor1=\"#b366ff\"\n",
    "color2= '#da3c07'\n",
    "palecolor2=\"#ff8954\"\n",
    "color3='#05d3d3'\n",
    "color4='#c6c7c5'\n",
    "color4=\"#434541\"\n",
    "color5=\"#eb31e1\"\n",
    "color6=\"#3175eb\"\n",
    "color7=\"#a7eb31\"\n",
    "color8=\"#b366ff\"\n",
    "color9=\"#ff8954\"\n",
    "color10=\"#35c9d4\"\n",
    "\n",
    "#use viridis\n",
    "# color1=\"#fde725\"\n",
    "# color2=\"#7ad151\"\n",
    "# color3=\"#22a884\"\n",
    "# color4=\"#2a788e\"\n",
    "# color5=\"#2a788e\"\n",
    "# color6='#440154'\n",
    "\n",
    "# # Set the color for the histogram\n",
    "color1 ='#4d00c7'\n",
    "palecolor1=\"#b366ff\"\n",
    "color2= '#da3c07'\n",
    "palecolor2=\"#ff8954\"\n",
    "color3='#05d3d3'\n",
    "color4='#c6c7c5'\n",
    "color5='black'\n",
    "\n",
    "# Set the color for the histogram\n",
    "color1 ='#4d00c7'\n",
    "palecolor1=\"#b366ff\"\n",
    "color2= '#da3c07'\n",
    "palecolor2=\"#ff8954\"\n",
    "color3='#05d3d3'\n",
    "color4='#c6c7c5'\n",
    "color5='black'\n",
    "\n",
    "# # Create the color palette\n",
    "# Create the color palette\n",
    "palette = sns.color_palette([palecolor1,palecolor2])\n",
    "palette2 = sns.color_palette([color1, color2, color3, color4,color5,color6 ,color7])\n",
    "new_palette = sns.color_palette([color1, color2,color3, color2,color1, color2,color1, color2,color1, color2,color1, color2, color3, color4])\n",
    "\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print(\"Scanpy\", sc.__version__)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "##load the adata frame\n",
    "adata = ad.read_h5ad(loc+\"files/adata_normalised.h5ad\")\n",
    "#set adata.X as log1p\n",
    "adata.X = adata.layers[\"log1p\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gene list import\n",
    "gene_list = pd.read_csv('\n",
    "            sep='\\t',\n",
    "            header=None,\n",
    "            names=['chr', 'source', 'type', 'start', 'end', 'null1', 'orientation', 'null2', 'notes'])\n",
    "\n",
    "gene_list = gene_list[['chr', 'source', 'type', 'start', 'end', 'orientation', 'notes']]\n",
    "gene_list['gene_id'] = gene_list['notes'].map(lambda x: x.split('\"')[1])\n",
    "\n",
    "gene_list[\"notes\"][0]\n",
    "\n",
    "gene_list['gene_name'] = gene_list['notes'].map(lambda x: x.split('\"')[7])\n",
    "gene_list['gene_type'] = gene_list['notes'].map(lambda x: x.split('\"')[5])\n",
    "\n",
    "protein_gene_list = gene_list[gene_list['gene_type'] == \"protein_coding\"]\n",
    "protein_gene_list = protein_gene_list[protein_gene_list[\"gene_name\"].isin(list(adata.var_names))] ##subseet for the genes found in the signle cell matrix \n",
    "protein_gene_list.reset_index(inplace=True,drop=True)\n",
    "genes_interest=adata.obs[\"perturbation\"][~adata.obs[\"perturbation\"].isin([\"non-targeting\"])].drop_duplicates() #create a list of genes to subset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "n =10 \n",
    "output=list()\n",
    "for goi in genes_interest:\n",
    "    print(goi)\n",
    "    subset = get_subset(gene_list, goi)\n",
    "    if ((subset.empty) | (goi==\"UPF3B\") | (goi==\"STAG2\")):\n",
    "        print(goi,\"not found\")\n",
    "        continue\n",
    "    else:\n",
    "        if check_promoters(adata, goi):\n",
    "            for promoter in [\"MP\",\"AP\"]:\n",
    "                print(goi,\"found\")\n",
    "                cell_barcodes = get_cell_barcodes_goi(adata, goi, promoter)\n",
    "                neighboring_genes = get_neighboring_genes(protein_gene_list, goi, n, subset)\n",
    "                neighborhood = get_neighborhood(adata, cell_barcodes, neighboring_genes)\n",
    "                control_neighborhood = get_control_neighborhood(adata, neighboring_genes)\n",
    "                output += perform_ztest(neighborhood, control_neighborhood, neighboring_genes, goi, promoter, n)\n",
    "output_df = pd.DataFrame(output, columns=['gene', 'promoter','neighbouring_gene', 'position','distance',\"samechr\" ,'z_test', 'z_test_pvalue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neighbouring Gene Identification\n",
    "CRISPRi uses a KRAB domain to silence promoters, but this silencing can sometimes spread to adjacent genomic regions.\n",
    "\n",
    "\n",
    "What is happening: The code uses genomic coordinates to identify all genes located within a specific window (e.g., the \"next-door\" genes) of the 42 targeted candidates.\n",
    "\n",
    "\n",
    "\n",
    "Purpose: To create a reference set of genes that should not be affected if the CRISPRi targeting is truly promoter-specific."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
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",
      "text/plain": [
       "<Figure size 6000x3000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with two subplots\n",
    "#set style\n",
    "sns.set(style=\"whitegrid\")\n",
    "fig, ax = plt.subplots(1, 1,figsize=(10, 5), dpi= 600, facecolor='none')\n",
    "# order=[\"-5\",\"-4\",\"-3\",\"-2\",\"-1\",\"0\",\"1\",\"2\",\"3\",\"4\",\"5\"]\n",
    "sns.boxplot(data=output_df[output_df[\"z_test_pvalue\"] < 0.05], y=\"z_test\", x=\"position\", ax=ax, hue=\"position\",  palette=palette)\n",
    "# , order=order)\n",
    "plt.ylabel(\"Z-Test (Relative to Negative Control)\")\n",
    "plt.xlabel(\"Location Relative to Knockdown of Interest\")\n",
    "#tremove the legend\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.savefig(loc+\"figures/5_gene_kd_neighboring/distance_overall.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "sns.set(font_scale=0.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IffbZJ3bfffeIiDjqqKNi7ty5sWrVqthnn336fLxKpRpdXZt2eC6aV7FYiPb2QdHV9UqUy5WGmKVcrkR3dznXWRpNuVLpvW26tclej67ucjmi+s4f3hdNvS511PNaboTXdQqN9HmMt9fePmibzkr2ObyuuuqqN913zz33xO23397XQ73JH/zBH8TixYvj+eefjzFjxsTDDz8c3d3dMW7cuO0+Zne3TUr8JnYaYy9Uq9Wtfsk88dv1qEbTrU3v5cU6zN7M61JPPWvRSK/rFPrb821VNfkXUA899NA4++yzd/g4EydOjBkzZsS0adNiwIAB0dbWFldeeWUMGTKkBlMCAOSrz+G1YcOGrd7esmVL3HHHHTFy5MiaDPTJT34yPvnJT9bkWAAAjaTP4TVx4sSt/nZNtVqN9vb2uPDCC2s5FwBAy+lzeN19991bvV0sFmPEiBF+hRAAwDvoc3jtuuuusXHjxrjvvvvi2WefjVGjRsXBBx8cw4YNq8N4AACto8/h9dRTT8W0adOira0tdt1113jmmWdi7ty58a//+q/xwQ9+sB4zAgC0hD6H19y5c2Pq1Knxmc98pve+f/mXf4lLLrlkh/+VeQCAVtbn3z/w6KOPxsknn7zVfSeffHL85Cc/qdlQAACtqM/hNXjw4Fi9evVW93V2dsbQoUNrNhQAQCvq86XGv/zLv4xTTjklZsyYEWPHjo3Ozs648sor47jjjqvHfAAALaPP4XXKKafEli1b4h//8R/jhRdeiLFjx8axxx4bn/70p+sxHwBAy+jTpcZly5bFokWL4nOf+1wsX748HnrooRg4cGDsscceUSwW6zUjAEBL2Obwuvfee+Oss86KQYMG9d5XrVbj0EMPjTPPPDMeeOCBugwIANAqtvlS48KFC+PLX/5yHHnkkb33tbW1xRlnnBG77bZbXHXVVfHRj360LkMCALSCbT7jtWrVqvj4xz/+lu87+uijY+XKlTUbCgCgFW1zeGVZFtVq9S3fVywWt/rF2QAAvNk2h9c+++wT991331u+b/ny5bH77rvXbCgAgFa0zeE1ffr0uOCCC+Luu++OcrkcERHlcjn+7d/+LWbPnh0nnXRS3YYEAGgF2/zD9RMnToyzzjorvvCFL0R3d3cMHTo0NmzYEDvvvHPMmjUrPvaxj9VzTgCAptenf0D1mGOOicmTJ8ePfvSjWL9+fYwaNSr+8A//MAYOHFiv+QAAWkaf/+X6nXfeOfbff/96zAIA0NL6/EuyAQDYPsILACAR4QUAkIjwAgBIRHgBACQivAAAEhFeAACJCC8AgESEFwBAIn3+l+thWxQKWRQKWd5jRLFY2Oo2T40wAwD5El7UXKGQxbCOtigWGic02tsH5T0CAAgvaq9QyKJYKMSNSx+Ptes35TpLlmVRLBaiXK5EtVrNdZa93jc8Pj5pXGRZ/mcCAciH8KJu1q7fFKvXbcx1hizLolQqRnd3OffwGtXhrBtAf9c414IAAFqc8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABJpuPB66qmn4oQTTohjjjkmjj322Pjxj3+c90gAADXRUOH16quvxkknnRQnnHBC3HbbbfHZz342zjjjjKhWq3mPBgCww0p5D/C7HnjggRg1alQcfvjhERFx0EEHxZVXXhnVajWyLNuuY5ZKDdWW/UKx+PqaZ1m23f/daib77W0W+c7SuxZZ5L8uDaap16aOe6yp16WOetai53NNq+t5nv3l+ba6hgqvp59+Ot797nfH+eefH4899lgMGTIkZs2aFYXC9m22QiGLjo7BNZ6SbVUsFqJUKuY9RkRElIr5z1H8zT4uFhpnXRpFK6xNPfZYK6xLPfQESHv7oJwnSau/Pd9W1VDh1d3dHf/xH/8Rixcvjn/4h3+I++67L/7u7/4uli1bFkOGDOnz8SqVanR1barDpPx/isVCtLcPinK5Et3d5XyHyV7/gthdLkfkfMW6XKn03ua+Lg2mqdemjnusqdeljsrl19elq+uV3j+3sp7Pqf3l+Tar9vZB23RWsqHCa/To0fG+970v9ttvv4h4/VJjqVSKn/3sZzF+/PjtOmZ3t02al2q1mvvP5/Ve+qlG7rP0Pn4DzNJomnlt6rnHmnld6qlnLV7/5q7/fI7vb8+3VTXUBeMDDzwwnnvuud6/yfjDH/4wXnvttdh9993zHQwAoAYa6ozXyJEj4+qrr45LLrkkNm3aFMViMRYsWLBdlxkBABpNQ4VXRMQf//Efx80335z3GAAANddQlxoBAFqZ8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAk0rDh9cgjj8Tee+8dzz//fN6jAADUREOG169+9auYPXt2bNmyJe9RAABqppT3AG/U3d0dM2fOjLPOOitOOumkHT5eqdSQbdnSisXX1zzLssiyLN9hst/eZpHvLL1rkUX+69Jgmnpt6rjHmnpd6qhnLQYMKPZ+vmllhULfnm+lUo1qtVrvsdhODRdel112WXzkIx+JAw44YIePVShk0dExuAZTsT2KxUKUSsW8x4iIiFIx/zmKhULvbaOsS6NohbWpxx5rhXWph6Hv2ikqlWoMGbJz3qMkta3Pt1Kp9sYajaehwuu73/1u/PKXv4xzzz23JserVKrR1bWpJsdi2xWLhWhvHxTlciW6u8v5DpO9/gWxu1yOyPkbwHKl0nub+7o0mKZemzrusaZelzoaWCpEoZDFN7/3RKxd3w8+x2evx3e5UnnHPfbu4W3xySM+GF1dr0S5XEkzHxER0d4+aJvOSDZUeH3729+ONWvWxDHHHNN73/Tp02POnDmx3377bdcxu7ttvLxUq/mf7u699FON3GfpffwGmKXRNPPa1HOPNfO61FPPWqxdvyk6176c8zT1l2VZlErF6O4uv+M+6Hn/69/4+vrXiBoqvBYvXrzV23vttVdce+21MWbMmJwmAgCondb/qUQAgAbRUGe83ujJJ5/MewQAgJpxxgsAIBHhBQCQiPACAEhEeAEAJCK8AAASEV4AAIkILwCARIQXAEAiwgsAIBHhBQCQiPACAEhEeAEAJCK8AAASEV4AAIkILwCARIQXAEAiwgsAIBHhBQCQiPACAEhEeAEAJCK8AAASEV4AAIkILwCARIQXAEAiwgsAIBHhBQCQiPACAEhEeAEAJCK8AAASEV4AAIkILwCARIQXAEAiwgsAIBHhBQCQiPACAEhEeAEAJCK8AAASEV4AAIkILwCARIQXAEAiwgsAIBHhBQCQiPACAEhEeAEAJCK8AAASEV4AAIkILwCARIQXAEAiwgsAIBHhBQCQiPACAEhEeAEAJCK8AAASEV4AAIkILwCARIQXAEAiwgsAIBHhBQCQiPACAEhEeAEAJFLKe4A3+uY3vxk33nhjZFkWgwYNivPOOy/Gjx+f91gAADusocLrv//7v2PhwoXx7W9/O4YPHx7Lly+PU089Ne6///7Isizv8QAAdkhDhdfQoUPjy1/+cgwfPjwiIsaPHx+/+tWv4pVXXom2trbtOmapVL+rqVmWRaEgCN+oZ02yLMs/mLPf3maR7yy9a5FF/uvSYJp6beq4x5p6Xeqo361LH/ZYz3oMGFCMYtFPE/2uSqUa1Wo17zEaK7z22GOP2GOPPSIiolKpxCWXXBIHH3zwdkdXoZBFR8fgWo64lUqlKrz+H8ViIUqlYt5jREREqZj/HMVCofe2UdalUbTC2tRjj7XCutRDf12XbdljQ9+1U1Qq1RgyZOcEEzWXRvma3VDh1WPjxo1x9tlnx/r162PhwoXbfZxKpRpdXZtqONlvFYuFaG8fFN/83hOxdn19HqNZ7fXejpg8aVyUK5Xo7i7nO0z2+ier7nI5IudvdMqVSu9t7uvSYJp6beq4x5p6Xeqo361LH/bYwFIhCoXM16Y3ePfwtvjkER+Mrq5Xolyu1OUx2tsHbdNZxoYLr6effjo+85nPxPjx42PevHmx00477dDxurvrs8A91vzq17F63ca6PkazGTnsN99pVSP307q9p+UbYJbex2+AWRpNM69NPfdYM69LPfW3denLHut5/9r1m6Jz7cv1Hq1p9KxLuVypexe8k4YKr2effTamTp0a06dPj+nTp+c9DgBATTVUeF177bXR1dUVt99+e9x+++299y9cuDBGjx6d42QAADuuocLrS1/6UnzpS1/KewwAgLrwd00BABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEGi687r///pgyZUpMnjw5pk2bFmvXrs17JACAmmio8Fq/fn3MmjUrLrvssli6dGkccsghce655+Y9FgBATWTVarWa9xA9br/99vjWt74V1113XUREvPbaa7HvvvvG8uXLY9SoUX0+XrVajUqlPk8vyyIKhUJs3PRalOv0GM1qQKkQbTsPsDZvYF3enrV5a9blrVmXt2dt3lqxkMWQtoFRqVSiXtVTKGSRZdk7flypPg+/fZ5//vnYZZddet8eOHBgdHR0xHPPPbdd4ZVlWRSL77wIO2JI28C6Hr+ZWZu3Zl3enrV5a9blrVmXt2dt3lqhkP+Fvvwn+B3VavUta7ERFgoAYEc1VNGMHTs21qxZ0/v2a6+9Fi+++GKMHTs2x6kAAGqjocLrgAMOiMceeyxWrlwZERG33HJLTJgwIYYPH57zZAAAO66hfrg+IuI///M/46tf/Wps3rw5RowYEXPnzo1dd90177EAAHZYw4UXAECraqhLjQAArUx4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCCwAgEeEFAJCI8AIASKSU9wCQl8WLF8eGDRvijDPOyHsUWkylUolDDz003vOe90RExD/90z/5nbPUVLVajQsvvDB++tOfRkdHR8yfPz9KJV/Sm4EzXvRLTz/9dCxdujTvMWhRv/jFL+Kwww6L66+/Pq6//nrRRc3dc889MXLkyLjhhhti8uTJ8cILL+Q9EttIeNHvVCqVWLBgQZxyyil5j0KLeuqpp+LHP/5xTJ06NRYuXJj3OLSgH/7wh1Eul+PEE0+MX/7ylzFmzJi8R2IbCS/6ncWLF8dxxx0XbW1teY9Cixo1alTMnDkzvvGNb8Sjjz4ajz32WN4j0WJeeuml2Lx5c3z961+PJ598Mh5//PG8R2IbuSBMv3PvvffGvffeG11dXdHV1RUTJ06M/fffP++xaCEf+tCHolQqRZZlsf/++8eqVavi93//9/Meixbyrne9KyZOnBgRERMnToyVK1fGhz70oZynYls440W/0/NzN1/84hfj6KOPFl3U3KJFi+LOO++MiIgf/ehH8f73vz/niWg1e++9d6xYsSIiIh599NF43/vel+9AbDPhBVBjf/M3fxNLliyJv/3bv41dd93VmQhq7vDDD49nn302jj/++CiVSjFhwoS8R2IbZdVqtZr3EAAA/YEzXrSM+fPnx3nnndf79v333x9TpkyJyZMnx7Rp02Lt2rU5TkcrsMeoN3us9Qkvml5nZ2fMmDEjFi9e3Hvf+vXrY9asWXHZZZfF0qVL45BDDolzzz03xylpZvYY9WaP9R/Ci6Z30003xaRJk2LatGm99z3wwAOx1157xV577RUREX/9138dDz74YKxbty6vMWli9hj1Zo/1H8KLpnfmmWfG1KlTo1gs9t73/PPPxy677NL79sCBA6OjoyOee+65PEakydlj1Js91n8IL1pStVqNLMvedH+hYMtTG/YY9WaPtSb/9WhJY8eOjTVr1vS+/dprr8WLL74YY8eOzXEqWok9Rr3ZY61JeNGSDjjggHjsscdi5cqVERFxyy23xIQJE/yyYmrGHqPe7LHW5FcG0ZKGDx8e8+bNiy984QuxefPmGDFiRFx22WV5j0ULsceoN3usNfkHVAEAEnGpEQAgEeEFAJCI8AIASER4AQAkIrwAABIRXgAAiQgvAIBEhBcAQCLCC+iXnnnmmbxHAPohvzIIaHp///d/H3vvvXesW7cu2tra4pxzzvl/P/4rX/lKbN68OS644IJEEwK8TngBLeOiiy7apo978cUXo62trc7TALyZ8AKazn/913/FJZdcEs8880z86Z/+aWzcuDEiIs4555xoa2uLCy64IJ588smYPXt2/PSnP41hw4bFxz72sZg1a1YsWrQo7rjjjsiyLH7+85/HokWLYunSpXHNNddEZ2dndHd3x4EHHhiXXHJJDBo0KM4555wYNGhQ/OxnP4v/+Z//id122y2++MUvxv777x8REffcc0/Mnz8/nnnmmRg7dmycddZZcfDBB0e5XI5rr702br755nj55Zdj3333jdmzZ8eYMWPyXDogZ37GC2gq69evjxkzZsSnPvWpWLFiRRxxxBGxYsWKN33cnDlz4sADD4yHH344rrvuurjzzjvj+9//fpx88slx1FFHxfHHHx+LFi2K5557Ls4+++w455xz4sEHH4wlS5bEihUr4rvf/W7vsW677baYOXNmPPjgg/HhD3845syZExERq1atis997nNx6qmnxooVK+KMM86I008/PdatWxfXXXddfOc734lrr702vv/978e4cePi1FNPjWq1mmytgMYjvICmsnz58hg9enT81V/9VZRKpTjyyCNj3333fdPH7bTTTnH//ffHsmXLor29Pe6999445JBD3vRxI0aMiDvvvDP+5E/+JF566aV44YUXoqOjI9asWdP7MQceeGBMmDAhBg4cGEceeWT8/Oc/j4iIu+66Kz784Q/H5MmTo1gsxmGHHRY33HBDDB48OG6++eY45ZRT4r3vfW/stNNOMXPmzHj66afjf//3f+u2NkDjE15AU1m3bt2bLtfttttub/q4efPmxQc/+MG49NJL4yMf+UiceuqpW8VUjwEDBsStt94aH/3oR+PYY4+Na665Jl599dWtzkyNGDGi98+lUqn3fWvXro1ddtllq+ONHz8+2tra4tlnn40LL7ww9ttvv9hvv/1i4sSJUalUYvXq1Tv0/IHmJryApjJ69Oh49tlnt7rvjUFVrVbjiSeeiFmzZsU999wTd955Z2zcuDHmzZv3puPdddddcdttt8W3vvWtuPvuu+NrX/taDB8+fJtmGTNmTDz//PNb3XfVVVfFypUrY/To0TFv3rxYsWJF7/9uvfXWOPTQQ/v4jIFWIryApnLIIYfEhg0b4utf/3p0d3fHsmXL4qGHHtrqY7Isi4svvji+9rWvxWuvvRYjR46MUqkUw4YNi4iIgQMHxssvvxwRES+99FIUCoUYOHBgdHd3xy233BKPPPJIbNmy5R1n+fjHPx4PP/xw3HPPPVGpVOLuu++Oa665JoYOHRrHHXdcXHHFFbF69eqoVCpxww03xF/8xV/Ehg0bar0kQBPxtxqBpjJs2LBYuHBhzJkzJy6//PL4oz/6ozjooIPe9HGXX355XHTRRTFp0qQoFApx8MEHx2mnnRYRrwfT5z//+TjuuOPixhtvjIceeigOO+yw2GmnnWLChAkxZcqUWLly5TvOMm7cuFiwYEHMmzcvZs2aFe95z3viiiuuiNGjR8f06dOju7s7PvWpT8WLL74Y48aNi6uvvjpGjx5d8zUBmkdW9VdsAACScKkRACAR4QUAkIjwAgBIRHgBACQivAAAEhFeAACJCC8AgESEFwBAIsILACAR4QUAkIjwAgBI5P8AwkP3mnxY5SkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.set(xscale=\"log\")\n",
    "\n",
    "sns.histplot(output_df[\"distance\"][(output_df[\"position\"]==1) &(output_df[\"promoter\"]==\"MP\") ].abs(),ax=ax )\n",
    "# save figujre\n",
    "plt.savefig(loc+\"figures/5_gene_kd_neighboring/distance_hist.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook performs a rigorous statistical comparison between \"on-target\" effects and \"off-target\" effects on genomic neighbours.\n",
    "\n",
    "\n",
    "Differential Expression: It compares the expression of the targeted gene and its neighbours in perturbed cells versus non-targeting control (NTC) cells using z-tests.\n",
    "\n",
    "\n",
    "\n",
    "Result Found: The analysis shows that while the targeted genes (e.g., ESR1) show significant knockdown, their neighbouring genes remain stably expressed. This confirms that the Isoform-Specific Perturb-Seq approach has high spatial resolution and does not cause widespread collateral silencing in the genome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 6000x3000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#get corrrelation between\n",
    "output_df_zero = output_df[output_df[\"position\"]==0]\n",
    "output_df_zero=pd.pivot_table(output_df, values=['z_test'], index=['gene','position'], columns=['promoter'])\n",
    "output_df_zero.reset_index(inplace=True)\n",
    "output_df_zero['notzero']=np.where(output_df_zero[\"position\"]==0,True,False)\n",
    "\n",
    "#correlation between MP and AP KD\n",
    "statistics_ttest_1=stats.ttest_ind(output_df_zero.loc[output_df_zero['notzero']==True,('z_test', 'MP')].values,output_df_zero.loc[output_df_zero['notzero']==False,('z_test', 'MP')].values , equal_var=False)\n",
    "statistics_ttest_2=stats.ttest_ind(output_df_zero.loc[output_df_zero['notzero']==True,('z_test', 'AP')].values,output_df_zero.loc[output_df_zero['notzero']==False,('z_test', 'AP')].values , equal_var=False)\n",
    "\n",
    "\n",
    "\n",
    "# Create a figure with two subplots\n",
    "sns.set(style=\"white\")\n",
    "fig, ax = plt.subplots(2, figsize=(10, 5), dpi= 600, facecolor='none')\n",
    "sns.set(font_scale=0.8)\n",
    "order=[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\"]\n",
    "output_df_sig=output_df[output_df[\"z_test_pvalue\"]<0.05]\n",
    "output_df_sig[\"pos_position\"]=np.abs(output_df_sig[\"position\"])\n",
    "g1=sns.boxplot(data=output_df_sig[(output_df_sig[\"promoter\"]==\"MP\")], y=\"z_test\", x=\"pos_position\", hue=\"pos_position\", ax=ax[0], palette=palette, order=order)\n",
    "g2=sns.boxplot(data=output_df_sig[(output_df_sig[\"promoter\"]==\"AP\")], y=\"z_test\", x=\"pos_position\", hue=\"pos_position\",ax=ax[1], palette=palette, order=order)\n",
    "#remove the legend from each plot\n",
    "ax[0].legend([],[], frameon=False)\n",
    "ax[1].legend([],[], frameon=False)\n",
    "plt.text(2.5,+18.5, \"sgP1 Target v OffTarget: \"+str( statistics_ttest_1.pvalue.round(12)), fontsize=12, ha='center')\n",
    "plt.text(2.5,-8.5, \"sgP2 Target v OffTarget: \"+str( statistics_ttest_2.pvalue.round(12)), fontsize=12, ha='center')\n",
    "\n",
    "#remove the xlabel from \n",
    "ax[0].set_xlabel(\"\")\n",
    "ax[1].set_xlabel(\"\")\n",
    "#same for ylabel\n",
    "ax[0].set_ylabel(\"\")\n",
    "ax[1].set_ylabel(\"\")\n",
    "#put the text \n",
    "#add the labels\n",
    "plt.ylabel(\"Knockdown (z-test)\")\n",
    "plt.xlabel(\"Knockdown Relative to the gene of interest\")\n",
    "plt.savefig(loc+\"figures/5_gene_kd_neighboring/distance_promoter_spec.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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HZPR5E8DEKQsEAFgFqmrHJNdfZuQz3X3pFl67W0ZPhjwkyR4ZNdlfMaMvfZ2X0VMhO8mJ3X3uJHMDsHZV1bWTvGiZkTd291tmlQcAJqWqDk7ytIGxk7r7v2eRBwAuw70Hzr/T3afMJAkAK8GFSV6d5Hnd/e15hwFgxbjOwPlQkd8knDZwfuAMMgCwcnwmybO7++3zDgIAM3JUkisNzJwwiyAArFnHJdl7YOY1swgCwIr2gmz5IVY/SfKA7t44wzwArA2HDZx/8rL+xapal6SS3CLJ1fN/vQ5JckGSb2d0P8Vnk/x3d2+eRFhg9VAWCACwOlwvyU7LnP9cA31V7ZfkwUnumVFJ4PoxdlxaVZ9O8uYk/9jdZ21jVgDWuKraIaMnd11hCyNnJHnM7BIBwGRU1f5J3p1kt4HRp84gDgD8kqq6XZKbDoz9xyyyALDwvplRSeDfdPf35h0GgBXnGgPnZ8wgw1DJ7VWrarfuPn8GWQBYTJckeW+Sv+pufxMDYK150hgzfj8CMBVV9ZQkDx8Y+3h3v3cWeQBYmarqTkkeuczI07r7i7PKA8CacqNlzjYn+fRP/6Gq1ie5Y5IHJblt/q8ccMj3q+qdSf4xyb8rDgQSZYEAAKvFcm8qk6UG+qq6ZZInJ/nNJDts5Y71SW629POcqjo+o6cpz+ImfgBWlydn9PSTLXlwd/9gVmEAYBKq6q5J/jbJXgOj/9rd/z6DSABwWZ48xsybpp4CgEX3yow+A7pk3kEAWLGuPnA+i4cTDpUFJsn+Sb487SAALKxrdve35h0CAGatqm6V5NcGxj7d3afMIg8Aa0dV7Z7kxUnuPzB6cZLHTTsPACtXVe2R5O+WGTkhyXEzigPA2rNcr8PJ3X1eVV0+o/c1j8jo3oStddUkxyz9/HdVPbu737wN1wFWEWWBAACrw2ED59+tqjcl+Z0J7ds5ozen96uqpyd5kUZ6AMZRVTdJ8rRlRl7e3e+fVR4A2F5VddMkz0ty+zHGv5HkwdNNBACXrarumeQOA2NnJXnfDOIAsMC6exYFTgCsbkMP1BinyG97fTfJ5iTrlpm56gxyALCgFAUCsBZV1Q5J/nqM0ddOOwsAa8dSScajkzwlyZXHeMmTuvuTUw0FwEr3yiR7b+HsR0mO6e5LZ5gHgDWiqq6S5IBlRj5dVccmeU6SfSe09vpJ3lRVj0ry8O4+eULXBVYYZYEAAKvDcg30SfLujAr+Ju0KSV6Q5Deq6v7dfc4UdgCwSlTVLknekGTHLYycmuSPZ5cIALbe0o3z109yuyQPzPD7sZ/6TpK7dPf3p5UNALakqq6U5KVjjL6wuy+edh4AAGD1qqqdMrqXYDk/mHaO7r6kqi4YyHKVaecAAABYMI/P8H0O342yQAC2U1XtnuRXkxyd5F4Z/pvhT720u184tWAArHhLBUz3XGbk8d391RnFAWDtOWzg/LeT/O6Udh+RURnhw7v7jVPaASwwZYEAACtcVa1LcujA2DSKAn/WUUk+XlVHdvc3prwLgJXrL5NcdwtnlyR5YHdfMMM8ADCoqt6R0e+pKyW5apLrJLn8Vl7ma0l+s7t7wvEAYFx/lWSfgZnvJHn5DLIAAACr2zgFfOdPPcX/7VEWCAAAkKSqrpfkGWOM/mV3XzjlOACsIlV13yS/k2SnjP7mtneSa27lZTYneV53P2Wy6QBYTarqmklesszIe7r7VTOKA8DaNPQgjml3OlwhyT9W1YHd/RdT3gUsGGWBAAAr34EZlVbM20FJPlZVt1IYCMAvqqrfSPKYZUb+srtPnFUeABhHVV01yV238zL/kOTR3X3eBCIBwFarqoclOWaM0ScocAcAACZgtzFmZvW3svOzfHH6lWeUAwAAYK6qarck/5xkl4HRr2T0ECoA2Bq3SHL37Xj92UmO7e73TigPAKtQVa1P8vps+bOo7yV56OwSAbBGHTbvAEueU1W7dvdT5x0EmJ318w4AAMB2G2qg35JPJ3lqkqOSXCOjP5LunGT/JDdOcu8kr0lyxlZcc78k/7ZUqAEASf63aOm1SdZtYeTzGe+JxQAwa9fYjtd+Ksntu/v+igIBmJequm2Sl40x+sHu/vtp5wEAANaEXceY+dHUU4xcOHC+00xSAAAAzFFV7ZDkLUl+ZWB0c0YPQ9w0/VQArDLbep/dj5M8L8nBigIBGMMTk/zaMueP7u6zZhUGgDVrW3odfphR4e0DkxyaZI+M7le4akZ/szsiyVOSfCTJxVtx3T+tqsdsQx5ghdow7wAAAGy3rX1T+dYkf97dX9jC+beWfj67NJuq+u0kf57hm0SSpJK8LsldtzIXAKvXy5Psu4WzjUke0N0bZ5gHAMa1PWWBFyU5rKq+1t1fm1QgABhXVR2S5F+S7Dgw+v0kx0w/EQAAsEbsMsbM1tzcvj2G9gy9XwIAAFgNXpnkjmPMPb+7PzTlLACsTtt6n92FSXZPcsuq+oD7yQHYkqo6LMkzlxl5U3e/ZUZxAFijqupyGa9r4afOSPLcJH/X3RddxvkPln46o6LA51bVPkmeluShGa8X7MVV9enu/sRW5AJWqPXzDgAAwHY7bMy505Mc2d33XqYo8DJ19z8nuUGSJye5dIyX3KWqHrU1OwBYnarq/knuvczI07r7i7PKAwBbaf/teO2tk7wgyWlV9daqusGEMgHAoKq6TpJ/S3LlgdHNSR7S3d+aeigAAGCtGOdmdWWBAAAAM1BVxyV5yBij/5XkqVOOA8Dqta332e2e5GFJ3pPkq1X1mKraeXKxAFgNloqZ/j7JTlsYOTPJo2eXCIA17AYZ756IZPQAj+t298u2UBR4mbr7rO5+VJLrJ/nSGC/ZkOSNVbXruDuAlUtZIADAynejMWZOSHLT7v6PbV3S3Zd09/OS/HZGT+8a8pyq2n1b9wGw8lXVNZK8dJmRjyV5/oziAMC22NYnHv+sdUmOTvKFqnqpmxkBmLaqunaS/0yy9xjjz+jut083EQAAsMbsMMbMopQFbulLZQAAACteVT03yePHGD0jyT26e9OUIwGwClXVLkn2mMCl9svovvOuqltM4HoArB7PzagwaUse0t0/mFUYANa0cTodNiY5trsf2d3nb+ui7j45ya2SvG+M8Wsm+ZNt3QWsHMoCAQBWsKraK8m+A2MfT3JUd393EjuXvjx8dJLNA6NXSfKUSewEYOWpqvVJXpfkSlsYuSDJMd196exSAcBWm0RZ4E+tS/KYJB9bKtQFgImrqoOSfCjJ1ccYf3N3P2u6iQAAAC7T0P0GkzL0OZR7aAEAgFVpqShwnC8IX5jkt7r7rClHAmD1mvS9cAck+XBVPW7C1wVgBaqq2yV57DIjr+jucUqUAGAShsoCL0nyu939ukks6+7zktw1yUfHGP/jpd4JYBVzoxMAwMq2Y5IXJXl7ks8nOe8Xzs9McrfuvnCSS7v7vUn+fIzRh1bVFSe5G4AV44+S3GaZ88d392kzygIA2+q8JC9L8uAkN0uyV5KdMirDvU6SX0/ytCQnZPiLxz91kyQf8CEcAJNWVTdI8pGMVxT4wSTHTDcRAACwRm0aY2bD1FOM7DhwfslMUgAAAMxIVa2rqpdkvKLATUmO7u5PTTkWAKvbVZK8K8lTkvxmkoOSXCGj++z2TXJokt/N6CH03xnzmjsmeVFV/eHE0wKwYlTVlTP6/bFuCyOnJnnCzAIBQHJiktcm+XCS0/PL9xw8tbv/eZILu/viJPfJ8PupyyV59CR3A4tnVjdcAQAwBd19RkZlTP+rqnZPcmCSayU5pbu/N6X1z8iojf6wZWaumOS+SV45pQwALKCqumGWL5V9b3f73QDAwuvuR2zhaFNGRYKnZfSErmcvFTQ9K8k9xrj0dZK8t6p+bdLl7gCsTVX1q0nenWT3McY/ntEDRn4y3VQAAMAatXGMmR2mnmJk6B7Zi2eSAgAAYAaqakOSv814D4y6JMnvLj1AHgC2WXd/IqPvFl2Ws5Z+vpDkjVW1U5KHZ1QsuM8Yl39BVX27u984kbAArDR/ky0/OPeSJMd09wUzzAPAGtfdb0jyhp/+c1XtmOSAjHod9kny91Pae2ZVPTrJUBHhw6rqmd29eRo5gPlTFggAsMoslQN+L8lUn/LY3ZdW1fOSvGlg9OgoCwRYM5Zu4vj7JDtvYeT7SR4yu0QAMBvd/aUk96yquyT5h4zK05dz4yRPS/In084GwOpWVXdM8k9JLj/G+IlJftNNkgAAwBRtGmNmVveuDu0Zp9gQAABg4VXVLknekuQuY4xfkuT+3f1P000FAD+vuzcmeWlVvS6jgo27D7xkXZKXV9V/dve3px4QgIVRVfdNct9lRp7f3R+fVR4AuCzdvSnJqUs/0/a2JF9Ocr1lZvZNcsskH5tBHmAO1s87AAAAK9o/JTltYOaIqrrcLMIAsBCek+SQZc4f3d1nzSoMAMxad78rya8mOX2M8cdX1Q2mHAmAVayqjknyjoxXFPiRJEd193nTTQUAAKxxPxljZlb3EGzp4VY/pUgdAABY8apq9yT/kfGKAjcluU93Dz0sHgCmprvPT3KPJP9vjPErJXnRdBMBsEiqar8kL1tm5PNJnj6jOACwELp7c5K/HGP0qGlnAeZnVk9nBQBYlarqmkm+NuU1r+vuY6e8Y5t09yVV9a9J/miZsR2T3CRa6AFmYp6/m6rqiCz/O+FN3f3mqaUCYCGtxfdN3f0/VXW3jN4HLVfetCHJU5PcZybBAEiyen43VdXTkjxzzPH3J7lHd184xUgAbIPV8nsJAH7GuWPMXGHqKUZ2GzhXFggAAKxoVXXtJO9NctAY4xclObq73z3dVAAwbKno4k+WCqHuPzB+n6p6Znf/zwyiATBHVbUuyfFJrrKFkY1JHtDdG2cWCgAWx78muTTJ+mVmbjGjLMAcLPf//AAAMI4PjzFz2LRDADBfVXXFJK/Llv/WcGaSR88uEQDMV3d/PslDxhi9R1XtMe08AKweVbVTVb024xcFviXJXRUFAgAAM/K9MWaGSvwmZWjPD2cRAgAAYBqq6pZJTsx4RYHnJTlKUSAAC+ihST4/xtzDpx0EgIXw2CR3WOb8ad39xVmFAYBF0t0/zPD7p8OmnwSYF2WBAABsr4+OMXPg1FMAMG8vTXLAMucP6e4fzCoMACyC7n5zRjfmL2enJPedQRwAVoGq2j3Jvyc5dsyXvDzJfT1JGQAAmJXu3pTkRwNjsyoLvMLA+TjFhgAAAAunqu6f5INJ9hxj/DtJbtPdH5luKgDYet39kyRPHGP0AVW1btp5AJifqrpekucuM/KxJM+fURwAWFRDf+Pbo6qG7pUAVihlgQAAbJel4qdzB8auPossAMxHVR2d5AHLjLyiu983qzwAsGCeOsbMraeeAoAVr6oOTvJfSX59zJc8rbsf3d2XTjEWAADAZRkq4RunzGK7VNVVk2wYGPvutHMAAABMUlWtq6pnJXlDkp3HeMkpSW7Z3Z+dbjIA2Hbd/f4kHx0Y2yPJr8wgDgBzUFU7JvmHJJfbwsgFSY5xLxwA5OtjzOh1gFVKWSAAAJPw/YFzDfQAq1RV7ZPklcuMnJbkCTOKAwCL6D8z/KXjX51FEABWrqo6Isknklx7jPGLkzyou5893VQAAABb9K2B86vNIMM4O86YegoAAIAJqaqdMyrP+LMxX/KJjIoCvzq9VAAwMW8ZY8Z9dgCr17OSHLbM+eO7+7QZZQGARTbU6ZDodYBVa+ipqQAAMI7vJTlwmfNdZhUEgJk7KslVlzm/dpIfVdWM4iRJvja0r7vXzSgLAGtcd2+uqn9Lcv9lxg6oqit293mzygXAylFVD0zy6iQ7jTF+XpJ7dff7p5sKAABgWV9LcqtlzhehLPDC7v7hDHIAAABst6raI8nbs/x7rZ/1tiT36+4fTy0UAEzWe8eYOWTqKQCYl/sOnL+iql4xkyQjx1TVMQMzz+zuZ8wiDAD8jO+NMaPXAVap9fMOAADAqnDJwPnGmaQAAABYTJ8ZY2a58l0A1qiqemqS12W8osDTk9xaUSAAALAAvjpwfs0ZZBjacdoMMgAAAGy3qjowyYkZvyjwhUmOVhQIwErS3adl9IDE5bjHDgAAWOuGOh0SvQ6wam2YdwAAAFaF3QfOL5xJCgAAgMV09hgzV0ny9SnnAGCFqKp1SV6c5A/GfMmnk9y1u8+aWigAAIDxDRXxHTSDDEM7TplBBgAAgO1SVTdM8r4k+44xfkmS3+/ul083FQBMzdlJrrjM+VVmFQQAAGBBDXU6JHodYNVSFggAsB26++tJ1s07xwIYejrXd2aSAgC/mwBYOH43JUm+O8bM5aeeAoAki/+7qao2JDk+yf3GfMnbk9yvu93YAbACLfrvJQDYRl8YOJ9FWeB1Bs6/NIMMAAAA26yqbpnkXRmvGOm8JPfu7n+bbioAmKrvZvm/67nHDgAAWOuGOh0SvQ6waikLBABYZarqikn2SnK57p76ze1VtWOSKw+MnT7tHAAAAEOqaockV09yrSQHJvl8d396Bqt3G2PmgqmnAGDhLRUFvjHJ0WO+5EVJntDdl04vFQAAwFb7UpKLklxuC+dXrqoDuvsbU8xw44Hzz01xNwAAwHapqlsneW+SK4wx/s0kd+7uL043FQBrxdL3kq619HO17n75jFYP3WfnHjsAAGBhLN33vWdGvQ6nd/cPZrB2r4Hzi7r77BnkAOZAWSAAwApVVQ9IcoeM3tT99GfPJDsvjZyz9M/TdrMk6wdmvjyDHAAAAD+nqn47yZH5vxsXr5Fkx58Z+bskD51BlKEP45Lkh9MOAcBi28qiwEuTPLa7XzrdVAAAAFuvuy+uqs8n+dVlxm6WZCplgVV1lSTXGRj75DR2AwAAbK+qulXGLwr8bJK7dPeZ000FwGpVVXskeXz+7x67A5Ps/gsz7+zub80gztB9dj+cQQYAAICfU1V7JnlmfrnT4SpJ1i2N/X6SWdzXvdx9GEnylRlkAOZEWSAAwMp1WJIHLnO+R1Vdq7u/OuUcvz7GzKennAEAAOCy3CbJI5Y5v92Mcuw/cH5xkm/OIggAi6mq1iU5PuMVBf44yX26+x1TDQUAALB9TszyN6nfMsk/TWn3LQfOvzajLzcDAABslaq6ScYvCnxfknt194+mmwqAVW5zkj8ZmLldktdPM0RV7ZJkj4GxaX8/CgAA4LJclORRAzOHTzvE0oPpbzEwptMBVjFlgQAAK9cpY8zcPNP/MOyIgfPTuvuMKWcAYE66+/iMCi1mpqq+nuSAZUYO7O6vzyYNAAvu5IHzA6vqwO7+2pRzHDlwfmp3XzzlDAAstucnud8Yc99Lcpfu/sSU8wAAAGyv9yV53DLnd07yR1PafaeB8w9MaS8AAMA2q6prJXlPkt3GGD8+ycPcawDA9uru71XV95NcdZmx22fKZYEZFRKuH5j5nylnAGBOuvuas9xXVccmee0yI6/r7mNnkwaARdfd51fVd5JcbZmxm88gyk0y/JCRD88gBzAnQ384AQBgcX1yjJl7TTNAVR2Y4dKL90wzAwAAwDL+a4yZB0wzQFXtldEHcss5YZoZAFhsVfV7SR4/xug3k9xaUSAAALBCfDjJj5c5P7iqfmXSS6tqfZK7Doy9a9J7AQAAtkdVXSWj0vW9xhh/fnc/SFEgABM0dJ/dParq8lPO8JtjzLjPDgAAmJehXoeDqurQKWd4xMD5pRn9jRFYpZQFAgCsXJ9Pcv7AzJ2ravcpZvijJDsMzLxpivsBAACW89kkPxqYefDSF4in5WFJ1g3M/PsU9wOwwKrqVkleNMboqRkVBf7PlCMBAABMRHdflOQ/BsYeNoXVd0yy/zLnP0jyb1PYCwAAsE2qal2Sv09y0Bjjf9rdT5xyJADWno8MnO+W5HemtbyqrpjkPgNjX+nuM6aVAQAAYMBHx5g5ZlrLq2qfJPcbGPtAd58zrQzA/CkLBABYoZaeBvnegbEdkzxyGvurar8kDxoY+0p3f3wa+wEAAIZ096Yk7x4YOyDJ3aexv6qumuSPB8bOT/LOaewHYLFV1Z5J3pJkw8Do15LcprtPn34qAACAiTp+4PxBVXXlCe/8w4HzNywVGQIAACyKpyS50zhz3f0X0w4DwJr09jFmfn+KD+X94yS7D8z845R2AwAAjGOc7/08sKquNKX9T0qy08DM301pN7AglAUCAKxsbxpj5k+W2uInpqp2SPIPSS4/MHrcJPcCAABsg3FuEnx+Ve08hd3PSzL0Qd8/dPePp7AbgMX3N0n2HZj5YZI7d/cZ048DAAAwce9M8t1lzq+S5BmTWlZVd0lyh4GxV09qHwAAwPaqqkOTPH2M0Vd193OnnQeAtam7/yfJZwfGDkvy4EnvrqqDM/wAkE0ZfjAJAADA1HT3V5J8YWBs9yRPm/TuqjoyyR8MjH01yT9PejewWJQFAgCsbO9I8s2BmSskeWlVrZvg3qcmOWJg5qtJXj/BnQAAANviXRm9P1nOtZM8eZJLq+pRSR42MLYpo0JBANaYqrpnkqMHxi5OcvTSzSUAAAArTndvSvKqgbHfr6rbb++uqrpaklcOjH24u7+0vbsAAAAmoao2JHltkh0HRv8jyWOmnwiANe4lY8z8RVXtNamFVXXljB44cvmB0eO7+1uT2gsAALCNXjbGzO9X1U0ntXDpXog3JBnqiXhOd188qb3AYlIWCACwgnX3JRmvWOKeSSbyNMmqemjGe4LlY72pBAAA5q27L03ynDFGn1ZV95nEzqUCqHFunnxpd39jEjsBWDmqauckLxxj9Dnd/YFp5wEAAJiyFyY5d5nz9UneVFW1rQuqatck/5xk34HRJ23rDgAAgCl4aJIbDcx8N8n93JMNwAz8Q5LTBmb2TPLuqhoq9xtUVVdM8i9JDh4YPT/Js7Z3HwAAwAS8Psk3B2Z2TPLOqrrm9i6rqqsmeV+Sqw2MfjLJ8du7D1h8ygIBAFa+Vyc5eYy5J1XVX1bVDtuypKrWV9Uzkrwqw+3z/9Td79qWPQAAAFNwfJLPDcysS/K6qrrf9iyqqj9N8k9JNgyMnp7kz7ZnFwAr1u8lOWBg5lNJ/nwGWQAAAKaqu7+f4cL0PZJ8oKpusLXXr6orJXlnklsNjP5Td//X1l4fAABgGpZKlsZ5ePvDu/vsaecBgO7elOSJY4zeNMm7qmrPbd1VVddJ8okktx1j/E+7+1vbugsAAGBSuvuiJE8dY3TvJB+sqkO3ddfSAxdPTHLYwOjGJI/o7ku3dRewcgx9WREAgAXX3Zuq6iFJPpzhMug/TvKrVfWo7v7yuDuq6oZJXpbk18YYPz3Jw8e9NgAAwLR196VVdWySk5LstMzoTkn+vqpul+RJ3X3OuDuq6qZJnpfk9mOMb0zyO919wbjXB2B1qKqdM/7N9ZtG93nMxeu6+9h5LQcAAFad5yf53STLvcnZL8nHquqx3X38OBetqpsleX2SXxkYPT/Jk8a5JgAAwIw8PKMvDQ952xw/L0qSA7v76/MMAMDsdPe/VNVbktx7YPQ2ST5XVQ/v7nePe/2q2iXJHyR5cpIrjfGSdyR56bjXBwAAmLbufn1V/W6SowZGD0xyYlU9Kckru3vjONdfutf895I8K8muY7zkyd392XGuDax8ygIBAFaB7j6hqp6Z5JljjP96ki9W1Vszumn+Q9194S8OVdVuSW6X5EFJ7pZk3RjX/lGSe3T3D8YODwAAMAPd/fmq+sOMitCHPDjJfarq1UnemuS/uvviXxxaejryHZMcneSuGe99U5I8prs/MeYsAKvL7yTZa94hAAAAZqm7f1xVD0zysSx/3+oVk7y2qh6V5IVJ3vmL9zNU1fokt07y6CT3yvBDFZPkEd391W0KDwAAMGFL72seM+8cALAFj0hyWJKDB+b2TfKuqvp0kr9O8v7uPusXh5Z+7x2e5E5JHrL0unH8d5IHdvfmMecBAABm5UFJPpXh9ze7JHlJkidU1UuSvKu7+7IGq+q6GX036eFJrj5mjn9M8qIxZ4FVQFkgAMDq8ew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",
      "text/plain": [
       "<Figure size 6000x3000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get corrrelation between\n",
    "output_df_zero = output_df[output_df[\"position\"]==0]\n",
    "output_df_zero=pd.pivot_table(output_df[output_df[\"z_test_pvalue\"]<0.05], values=['z_test'], index=['gene','position'], columns=['promoter'])\n",
    "#correlation between MP and AP KD\n",
    "# statistics_ttest=stats.ttest_ind(, , equal_var=False)\n",
    "# put the statistics between\n",
    "output_df_zero[('z_test', 'MP')].corr(output_df_zero[('z_test', 'AP')])\n",
    "#remove warnings output\n",
    "# Create a figure with two subplots\n",
    "sns.set(style=\"white\")\n",
    "fig, ax = plt.subplots(2, figsize=(10, 5), dpi= 600, facecolor='none')\n",
    "sns.set(font_scale=0.8)\n",
    "order=[\"-5\",\"-4\",\"-3\",\"-2\",\"-1\",\"0\",\"1\",\"2\",\"3\",\"4\",\"5\"]\n",
    "#split for AP and MP into two plots\n",
    "plt.text(2.5,-8.5, \"R2: \"+str(output_df_zero[('z_test', 'MP')].corr(output_df_zero[('z_test', 'AP')]).round(2) ), fontsize=12, ha='center')\n",
    "#melt the dataframe output_df_zero so z_test for MP and AP\n",
    "#put texct on the plot\n",
    "output_df_sig=output_df[output_df[\"z_test_pvalue\"]<0.05]\n",
    "g1=sns.boxplot(data=output_df_sig[(output_df_sig[\"promoter\"]==\"MP\")], y=\"z_test\", x=\"position\", hue=\"position\", ax=ax[0], palette=palette, order=order)\n",
    "g2=sns.boxplot(data=output_df_sig[(output_df_sig[\"promoter\"]==\"AP\")], y=\"z_test\", x=\"position\", hue=\"position\",ax=ax[1], palette=palette, order=order)\n",
    "#remove the legend from each plot\n",
    "ax[0].legend([],[], frameon=False)\n",
    "ax[1].legend([],[], frameon=False)\n",
    "#remove the xlabel from \n",
    "ax[0].set_xlabel(\"\")\n",
    "ax[1].set_xlabel(\"\")\n",
    "#same for ylabel\n",
    "ax[0].set_ylabel(\"\")\n",
    "ax[1].set_ylabel(\"\")\n",
    "#put the text \n",
    "#add the labels\n",
    "plt.ylabel(\"Knockdown (z-test)\")\n",
    "plt.xlabel(\"Knockdown Relative to the gene of interest\")\n",
    "plt.savefig(loc+\"figures/5_gene_kd_neighboring/distance_promoter_spec.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "#check the number A, B, C and D for each of the gene_targets\n",
    "adata.obs[\"protospacer_number\"]=adata.obs[\"guide_assignment\"].str.split(\"_\").str.get(-1)\n",
    "# adata.obs[\"protospacer_number\"].value_counts()\n",
    "%%capture\n",
    "output= [process_gene(adata, goi, geneset=\"transcriptome\") for goi in genes_interest if process_gene(adata, goi,geneset=\"transcriptome\") is not None]\n",
    "\n",
    "output_df = pd.DataFrame(output, columns=['gene', 'Protospacer_P1_1', 'pvalue_P1_1','Random_Guide_P1_1', 'pvalue_other_P1_1','Protospacer_P1_2', 'pvalue_P1_2','Random_Guide_P1_2', 'pvalue_other_P1_2','Protospacer_P2_1', 'pvalue_P2_1','Random_Guide_P2_1', 'pvalue_other_P2_1','Protospacer_P2_2', 'pvalue_P2_2', 'Random_Guide_P2_2', 'pvalue_other_P2_2'])\n",
    "correlation=output_df[[\"Protospacer_P1_1\",\"Protospacer_P1_2\",\"Random_Guide_P1_1\",\"Random_Guide_P1_2\",\"Protospacer_P2_1\",\"Protospacer_P2_2\",\"Random_Guide_P2_1\",\"Random_Guide_P2_2\"]].corr()\n",
    "# correlation=output_df[[\"Protospacer_P1_1\",\"Protospacer_P1_2\",\"Protospacer_P2_1\",\"Protospacer_P2_2\"]].corr()\n",
    "sns.heatmap(correlation,annot=True, cmap=\"OrRd\")\n",
    "plt.savefig(loc+\"figures/5_gene_kd_neighboring/correlation_allgene.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "apu",
   "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": "3.12.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}