{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantitative  individual UMAPs per gene of interest\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script focuses on using unsupervised learning and information theory to prove that alternative promoters drive distinct transcriptional states.\n",
    "\n",
    "Visualization of Transcriptional Landscapes (UMAP & t-SNE)\n",
    "The primary goal of this notebook is to visualize how cells cluster based on which promoter (P1 or P2) was targeted.\n",
    "\n",
    "\n",
    "What is happening: The code performs high-dimensional reduction using UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-distributed Stochastic Neighbor Embedding).\n",
    "\n",
    "\n",
    "The Logic: If the two promoters were redundant, their respective cell populations would overlap completely. Instead, for genes like ESR1, BRIP1, and MYBB1A, the notebook generates plots showing cells segregating into distinct \"clouds\" or gradients based on the promoter knockdown"
   ]
  },
  {
   "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",
    "\n",
    "#form a location\n",
    "loc=\"alt-prom-crispr-fiveprime/\"\n",
    "\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm\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",
    "import statsmodels.api as sm\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 hdbscan\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",
    "# %%capture\n",
    "\n",
    "\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",
    "umap_palette= sns.color_palette([\"#F05325\",\"#04AFBB\"])\n",
    "# Create the color palette\n",
    "palette = sns.color_palette([color1, color2,color3])\n",
    "new_palette = sns.color_palette([color1, color2,color1, color2,color1, color2,color1, color2,color1, color2,color1, color2, color3, color4])\n",
    "\n",
    "#change palette to 3d82c4 36a047\n",
    "palette=sns.color_palette([\"#f3766e\", \"#7094cd\"])\n",
    "#change #36a047 to G1 #7671b3 toG2M and #d76127 to S \n",
    "cellcycle_palette=sns.color_palette([\"#36a047\", \"#7671b3\",\"#d76127\"])\n",
    "\n",
    "print(\"Scanpy\", sc.__version__)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "adata = ad.read_h5ad(loc+\"files/adata_normalised.h5ad\")\n",
    "adata.X=adata.layers[\"log1p\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1280x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.rc_context():  # Use this to set figure params like size and dpi\n",
    "    sc.pl.highly_variable_genes(adata,show=False)\n",
    "    plt.savefig(loc+\"figures/hvg.pdf\", bbox_inches=\"tight\")\n",
    "with plt.rc_context():  # Use this to set figure params like size and dpi\n",
    "    sc.pl.highest_expr_genes(adata, show=False,n_top=15)\n",
    "    plt.savefig(loc+\"figures/heg.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n",
      "WARNING:matplotlib.text:posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##figure2\n",
    "#change plot figure\n",
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "#make prettier\n",
    "sns.set(style=\"white\")\n",
    "#check if AP and MP present for each gene\n",
    "value_counts_p1p2=adata.obs[[\"perturbation\",\"promoter_type\"]].value_counts().reset_index()\n",
    "value_counts_p1p2=pd.DataFrame(pd.pivot(value_counts_p1p2,index='perturbation', columns=\"promoter_type\",values=\"count\")).reset_index()\n",
    "sns_plot=sns.jointplot(data=value_counts_p1p2, x=\"AP\",y=\"MP\", color=color5,\n",
    "    marker=\"*\", s=100, marginal_kws=dict(bins=25, fill=False),\n",
    ")\n",
    "for i in range(value_counts_p1p2.shape[0]):\n",
    "    #randomn number between 0 and 1\n",
    "    random_1=np.random.uniform(-2,2)\n",
    "    x_coord=value_counts_p1p2.AP[i]+random_1\n",
    "    y_coord=value_counts_p1p2.MP[i]+random_1\n",
    "    name=value_counts_p1p2.perturbation[i]\n",
    "    plt.text(x=x_coord,y=y_coord,s=name, fontsize=7)\n",
    "    if value_counts_p1p2.perturbation[i]==\"ESR1\":\n",
    "        #style bold\n",
    "        plt.text(x=value_counts_p1p2.AP[i]+0.3,y=value_counts_p1p2.MP[i]+0.3,s=value_counts_p1p2.perturbation[i], fontsize=8,color=color3)\n",
    " \n",
    "plt.xlabel(\"Number of Cells with \\n the P2 Guide For a Gene Present\")\n",
    "plt.ylabel(\"Number of Cells with  \\n the P1 Guide For a Gene Present\")\n",
    "#save plot in pdf form\n",
    "output_fig_loc=loc+\"figures/4_5_quality_control/\"\n",
    "sns_plot.figure.savefig(output_fig_loc+\"cellcount_perprom.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc=\"alt-prom-crispr-fiveprime/\"\n",
    "# specify parameters and distributions to sample from\n",
    "# genelist=list(pop.cells[\"perturbation\"][(pop.cells[\"perturbation\"]!=\"control\") & (pop.cells[\"perturbation\"]!=\"NegCtrl3b\")].drop_duplicates())\n",
    "genelist=adata.obs[\"perturbation\"].drop_duplicates().values\n",
    "#remove non-targeting\n",
    "genelist=[x for x in genelist if x != \"non-targeting\"]\n",
    "hyperparam_list=[]\n",
    "best_list_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helenking/anaconda3/envs/apu/lib/python3.12/site-packages/scanpy/preprocessing/_highly_variable_genes.py:75: UserWarning: `flavor='seurat_v3'` expects raw count data, but non-integers were found.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#calculate the highly variable genes \n",
    "sc.pp.highly_variable_genes(adata, n_top_genes=1500, flavor=\"seurat_v3\", n_bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "highlyvariable_genes=adata.var.index[(adata.var[\"highly_variable\"]==True) | (adata.var.index.isin(genelist))]\n",
    "#add highlyvariable_genes to the targetggene list\n",
    "adata_sub=adata\n",
    "adata_sub.obs.index=adata_sub.obs[\"cell_barcode\"]\n",
    "#subset the highly variable genes\n",
    "adata_sub=adata[:,highlyvariable_genes]\n",
    "new_popmatrix_no_duplicates=pd.DataFrame(adata_sub.layers[\"log1p\"])\n",
    "# new_popmatrix_no_duplicates=pd.DataFrame(adata_sub.layers[\"sctransform\"].todense())\n",
    "#columns are genes, rows are cells\n",
    "new_popmatrix_no_duplicates.columns=adata_sub.var.index\n",
    "new_popmatrix_no_duplicates.index=adata_sub.obs.index\n",
    "new_popmatrix_no_duplicates_read_count=new_popmatrix_no_duplicates.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "#map a umap, calculate kl diveergence, and rand score\n",
    "# genelist=[\"MYBBP1A\",\"ESR1\"]\n",
    "# genelist=[\"MYBBP1A\",\"ESR1\", \"PSMC5\",\"BRIP1\",\"STAT3\",\"CHD4\"]\n",
    "#change the backgorun dto white\n",
    "for gene in genelist:\n",
    "    print(gene)\n",
    "    ##list_guides from adata\n",
    "    list_guides_df=adata_sub.obs[adata_sub.obs[\"perturbation\"]==gene]\n",
    "    list_guides=list(list_guides_df[\"guide_identity\"].drop_duplicates())\n",
    "    #find the gene name of interest \n",
    "    if not list_guides:\n",
    "        print(\"skip\")\n",
    "    if gene not in new_popmatrix_no_duplicates.columns:\n",
    "        print(\"skip\")\n",
    "    else:\n",
    "        #subset cells+in\n",
    "        # cells_interest=new_popmatrix_no_duplicates[new_popmatrix_no_duplicates.index.isin(list_guides_df[\"cell_barcode\"].drop_duplicates())]\n",
    "        cells_interest=adata_sub.obs[adata_sub.obs.index.isin(list_guides_df[\"cell_barcode\"].drop_duplicates())]\n",
    "        # cells_interest[\"cell_barcode\"]=cells_interest.index\n",
    "        cells_interest=cells_interest.drop_duplicates(subset=\"cell_barcode\")\n",
    "        column_nopromotergene = [col for col in new_popmatrix_no_duplicates.columns if (('MT' not in col)&('Non' not in col)&('sg' not in col)&('_' not in col))]\n",
    "        all=new_popmatrix_no_duplicates.loc[cells_interest[\"cell_barcode\"],column_nopromotergene]\n",
    "        #this creates one list that contains repeating elements one for     \n",
    "        list_rep=cells_interest[\"promoter_type\"].values\n",
    "        ##maybe make an if loop that if one of the types are not present then skip....\n",
    "        list_rep=np.squeeze(pd.DataFrame(list_rep))\n",
    "        # list_rep_cellcycle=cells_interest[\"phase\"].values\n",
    "        # list_rep_cellcycle=np.squeeze(pd.DataFrame(list_rep_cellcycle))\n",
    "        standard_embedding = umap.UMAP(random_state=22).fit_transform(all)   \n",
    "        ##hyperparam\n",
    "        hyper_paramlist=hyperparam(df = standard_embedding, list_rep=list_rep)\n",
    "        best = best_validity(hyper_paramlist)\n",
    "        clust_alg_eom = hdbscan.HDBSCAN(algorithm='best', alpha=1.0,\n",
    "                                    approx_min_span_tree=True,\n",
    "                                    gen_min_span_tree=True, \n",
    "                                    cluster_selection_method=best['method'],\n",
    "                                    metric=best['metric'], \n",
    "                                    min_cluster_size=int(best['min_cluster_size']), \n",
    "                                    min_samples=int(best['min_samples']), \n",
    "                                    allow_single_cluster=False).fit(standard_embedding)\n",
    "        best_list=pd.DataFrame(best).T\n",
    "        best_list[\"gene\"]=gene\n",
    "        best_list_list.append(best_list)\n",
    "        ##all \n",
    "        df_list=pd.DataFrame(hyper_paramlist)\n",
    "        df_list[\"gene\"]=gene\n",
    "        hyperparam_list.append(df_list)\n",
    "        \n",
    "        ###!!!plotting section !!!!####\n",
    "        ##individual plotting \n",
    "        fig, ax = plt.subplots(1, 1,  dpi= 600, facecolor='none')\n",
    "        sns.set(style=\"white\",font_scale=0.5,rc={'figure.figsize':(4,3)})\n",
    "        sns_plot=sns.scatterplot(x=standard_embedding[:,1],y=standard_embedding[:,0],  hue=np.where(list_rep==\"MP\", \"P1\", \"P2\"),ax=ax,palette=palette)\n",
    "\n",
    "        ###with cell cycle assignment\n",
    "        # sns_plot=sns.scatterplot(x=standard_embedding[:,1],y=standard_embedding[:,0],  hue=np.where(list_rep==\"MP\", \"P1\", \"P2\"),ax=ax[0],palette=palette)\n",
    "        # sns_plot=sns.scatterplot(x=standard_embedding[:,1],y=standard_embedding[:,0],  hue=list_rep_cellcycle,ax=ax[1],palette=cellcycle_palette)\n",
    "        #place the legend outside\n",
    "        # ax[0].get_legend().remove()\n",
    "        # ax[0].legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "        \n",
    "        #tsne4.kl_divergence_\n",
    "        ###!!!cluster!!!####\n",
    "        print(best)\n",
    "        fig, ax =plt.subplots(1,2)\n",
    "        sns_plot=sns.scatterplot(x=standard_embedding[:,1],y=standard_embedding[:,0],  hue=np.where(list_rep==\"MP\", \"P1\", \"P2\"),ax=ax[0],size=8, palette=umap_palette)\n",
    "        sns_plot=sns.scatterplot(x=standard_embedding[:,1],y=standard_embedding[:,0],  hue=clust_alg_eom.labels_,ax=ax[1], size=8, palette=umap_palette)\n",
    "        #\n",
    "        sns_plot.figure.savefig(\"alt-prom-crispr-fiveprime/figures/umap/\"+gene+\".pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hyperparam\n",
    "dataframe=pd.concat(hyperparam_list)\n",
    "cols = ['metric','min_cluster_size', 'method','min_samples', 'validity_score', 'n_clusters','randscore','mutualscore' ,'gene']\n",
    "dataframe.columns=cols\n",
    "dataframe.to_csv(\"alt-prom-crispr-fiveprime/files/singlecell_shortread_analysis/hyperparam_cluster.csv\")\n",
    "\n",
    "#metric min_cluster_size method min_samples validity_score n_clusters randscore mutualscore   gene\n",
    "dataframe=pd.concat(best_list_list)\n",
    "cols = ['metric','min_cluster_size', 'method','min_samples', 'validity_score', 'n_clusters','randscore','mutualscore','gene']\n",
    "dataframe.columns=cols\n",
    "dataframe.to_csv(\"alt-prom-crispr-fiveprime/files/singlecell_shortread_analysis/best_cluster.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparam_df=pd.read_csv(\"alt-prom-crispr-fiveprime/files/singlecell_shortread_analysis/hyperparam_cluster.csv\",index_col=0)\n",
    "best_df=pd.read_csv(\"alt-prom-crispr-fiveprime/files/singlecell_shortread_analysis/best_cluster.csv\",index_col=0)\n",
    "best_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantifying Separation with Rand Index and Mutual Information\n",
    "To move beyond visual interpretation, the notebook uses rigorous statistical metrics to quantify how \"different\" the P1 and P2 populations really are.\n",
    "\n",
    "\n",
    "Rand Index (Adjusted Rand Score): This measures the similarity between the \"ground truth\" (which promoter was targeted) and unsupervised clusters. A higher score confirms that the transcriptomic differences are strong enough for an algorithm to \"guess\" the targeted promoter.\n",
    "\n",
    "\n",
    "\n",
    "Mutual Information (MI): This information-theory metric quantifies the amount of information obtained about the promoter perturbation by observing the cell's transcriptome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the validity_score randscore and mutualscore\n",
    "sns.set(style=\"white\")\n",
    "sns.set_style(\"ticks\")\n",
    "sns.set_context(\"paper\", font_scale=2)\n",
    "sns.scatterplot(data=best_df, x=\"mutualscore\", y=\"randscore\", palette=palette)\n",
    "#label the poiints abopve 0.04 mutual score\n",
    "for i in range(best_df.shape[0]):\n",
    "    if best_df.mutualscore[i]>0.04:\n",
    "        plt.text(best_df.mutualscore[i],best_df.randscore[i],best_df.gene[i], fontsize=12)\n",
    "#label the two \n",
    "\n",
    "plt.xlabel(\"Mutual Information Score\")\n",
    "plt.ylabel(\"Rand Score\")\n",
    "plt.savefig(\"alt-prom-crispr-fiveprime/figures/umap/mutual_rand.pdf\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_barcode\n",
       "GTGTGCGGTTAGTGGG-1       NBN\n",
       "TCAGATGAGCATCATC-1     PA2G4\n",
       "GCAATCACACGACGAA-1       PKM\n",
       "CACACAAAGCTGCAAG-1     SIN3A\n",
       "GTCACGGGTTAAGTAG-1       SP1\n",
       "                       ...  \n",
       "TAGCCGGAGCGTAATA-1     NCOR2\n",
       "CTTGGCTTCAGCTTAG-1      CHD4\n",
       "CATATGGAGGTGTTAA-1     GPBP1\n",
       "TCTCTAAAGGAGCGTT-1     KDM2A\n",
       "GGGAATGTCTTGACGA-1    CTNNB1\n",
       "Name: gene, Length: 21657, dtype: category\n",
       "Categories (50, object): ['ADAR', 'AHCYL1', 'APP', 'ARNT', ..., 'XRCC5', 'YWHAZ', 'ZNF3', 'ZNF217']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs[\"gene\"][adata.obs[\"successfulKD\"]==\"True\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CHD4', 'ESR1', 'MYBBP1A', 'STAT3', 'BRIP1', 'GTF2F1']\n",
       "Categories (50, object): ['ADAR', 'AHCYL1', 'APP', 'ARNT', ..., 'XRCC5', 'YWHAZ', 'ZNF3', 'ZNF217']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_gene=[\"MYBBP1A\",\"ESR1\", \"GTF2F1\",\"BRIP1\",\"STAT3\",\"CHD4\"]\n",
    "subset_suc=adata.obs[\"gene\"][adata.obs[\"successfulKD\"]==\"True\"]\n",
    "\n",
    "subset_suc[subset_suc.isin(vis_gene)].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in edistance and check the correlation per gene \n",
    "edistance=pd.read_csv(\"alt-prom-crispr-fiveprime/files/singlecell_shortread_analysis/edistance.csv\",index_col=0)\n",
    "edistance[\"gene\"]=edistance.index.str.split(\"_\").str[0]\n",
    "edistance[\"promoter\"]=edistance.index.str.split(\"_\").str[1]\n",
    "#merge edistance and best_df\n",
    "edistance=edistance.merge(best_df, left_on=\"gene\", right_on=\"gene\")\n",
    "#check correlation between edistance and randscore\n",
    "#perform spearmans correlation between the two\n",
    "edistance[\"edist\"]=edistance[\"edist\"].astype(float)\n",
    "edistance[\"randscore\"]=edistance[\"randscore\"].astype(float)\n",
    "sns.set(font_scale=1)  # crazy big\n",
    "\n",
    "sns.heatmap(edistance[['edist','randscore']].corr(method=\"pearson\"), annot=True, cmap=\"crest\", linewidths=.5, vmin=0, vmax=1, cbar_kws={\"shrink\": .5})\n",
    "#change the size of text\n",
    "plt.xticks(fontsize=8)\n",
    "plt.yticks(fontsize=8)\n",
    "plt.savefig(\"alt-prom-crispr-fiveprime/figures/edistance_randscore.pdf\")"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}