GTEX_manual_match.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a2d50e2-ab6c-4349-b4dd-a8349101ff2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc69b307-9269-4f63-84d3-545c99a5e963",
   "metadata": {
    "id": "NRI9KisU11bM"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import functools\n",
    "import collections\n",
    "import random\n",
    "import re\n",
    "import glob\n",
    "import math\n",
    "import os\n",
    "import json\n",
    "import pickle\n",
    "\n",
    "import pyranges as pr\n",
    "\n",
    "#os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\"\n",
    "\n",
    "#import joblib\n",
    "import gzip\n",
    "import kipoiseq\n",
    "from kipoiseq import Interval\n",
    "import pyfaidx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "from scipy.special import loggamma\n",
    "from scipy.special import expit, logit\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "\n",
    "import seaborn as sns\n",
    "import statannot\n",
    "import plotnine as p9\n",
    "import sklearn\n",
    "from sklearn import ensemble\n",
    "from sklearn import pipeline\n",
    "import statsmodels\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import patsy\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f988c243-4537-4ae6-aa93-af72680c2a86",
   "metadata": {
    "id": "hTGOLrbZxNHK"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-05 14:52:04.656554: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-02-05 14:52:04.883586: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-02-05 14:52:04.884369: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_hub as hub\n",
    "import tensorflow as tf\n",
    "# Make sure the GPU is enabled \n",
    "assert tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0005cec-5aec-4b4e-ab71-d737c6db95d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Code.Utilities.seq_utils import one_hot_encode, rev_comp_sequence, rev_comp_one_hot, compute_offset_to_center_landmark\n",
    "from Code.Utilities.enformer_utils import *\n",
    "\n",
    "import Code.Utilities.basenji2_utils as basenji2_utils\n",
    "import Code.Utilities.basenji1_utils as basenji1_utils\n",
    "import kipoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2242f94c-476a-40b5-aef4-2d74b8d8f217",
   "metadata": {
    "id": "g0F1A9AaCrkQ"
   },
   "outputs": [],
   "source": [
    "transform_path = 'gs://dm-enformer/models/enformer.finetuned.SAD.robustscaler-PCA500-robustscaler.transform.pkl'\n",
    "model_path = 'https://tfhub.dev/deepmind/enformer/1'\n",
    "fasta_file = 'Data/Genome/genome.fa'\n",
    "fasta_file_hg19 = 'Data/Genome/genome_hg19.fa'\n",
    "gtf_file = 'Data/Genome/genes.gtf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "efc018bf-5719-40bd-a94d-4effc515627a",
   "metadata": {
    "id": "5m1IEvOxux_t"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "# import target df\n",
    "target_df = pd.read_csv(\"Data/Targets/targets.tsv\",sep=\"\\t\")\n",
    "target_df_basenji1 = pd.read_csv(\"Models/Basenji/basenji1_targets.txt\",sep=\"\\t\", names=[\"identifier\",\"file\",\"description\"])\n",
    "print(\"done\")\n",
    "\n",
    "# import gtf\n",
    "gtf_df = pr.read_gtf(gtf_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f42c9a59-001e-465c-9a66-0031b4a894e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path_data = \"Data/GTEX/\"\n",
    "base_path_data_kaessmann = \"Data/Kaessmann/\"\n",
    "base_path_data_gtex_fc = \"Data/gtex_aFC/\"\n",
    "base_path_data_tss_sim = \"Data/TSS_sim/\"\n",
    "base_path_data_fulco = \"Data/Fulco_CRISPRi/\"\n",
    "base_path_results = \"Results/TSS_sim/\"\n",
    "base_path_results_xpresso = \"Results/TSS_Xpresso/\"\n",
    "base_path_results_gtex_fc = \"Results/gtex_aFC/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b7a128-f7e7-4d15-9b53-31593f542b62",
   "metadata": {},
   "source": [
    "# Load GTEX data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "70c49850-e2dd-4944-b4ba-25d7f9ee0261",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load gtex data\n",
    "gtex_df = (pd.read_csv(base_path_data + \"GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct\", sep=\"\\t\")\n",
    "           .rename(columns={'Name':'gene_id','Description':'gene_name'})\n",
    "          )\n",
    "tissues = gtex_df.keys()[2:]\n",
    "gtex_df = gtex_df.loc[~gtex_df.gene_id.str.endswith(\"PAR_Y\")]\n",
    "gtex_molten = gtex_df.melt(id_vars=['gene_id','gene_name'], var_name=\"tissue\", value_name='observed_tpm')\n",
    "gtex_molten['gene_id'] = gtex_molten['gene_id'].apply(lambda x: x.split('.')[0])\n",
    "\n",
    "# remove genes which are never expressed (otherwise removing colMean will create phantom deviation for these)\n",
    "nonexpressed_genes = set(gtex_molten.groupby('gene_id')[\"observed_tpm\"].max().reset_index().query('observed_tpm == 0')[\"gene_id\"])\n",
    "gtex_molten = gtex_molten.query('gene_id not in @nonexpressed_genes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1572ddb8-3079-4c7d-8923-013b9899388c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load kaessmann data\n",
    "kaessmann_df = (pd.read_csv(base_path_data_kaessmann + \"Human.RPKM.txt\", sep=\" \")\n",
    "                .reset_index()\n",
    "           .rename(columns={'index':'gene_id'})\n",
    "          )\n",
    "dev_tissues = kaessmann_df.keys()[1:]\n",
    "kaessmann_molten = kaessmann_df.melt(id_vars=['gene_id'], var_name=\"sample\", value_name='observed_tpm')\n",
    "kaessmann_molten['gene_id'] = kaessmann_molten['gene_id'].apply(lambda x: x.split('.')[0])\n",
    "\n",
    "# remove genes which are never expressed (otherwise removing colMean will create phantom deviation for these)\n",
    "nonexpressed_genes = (kaessmann_molten.groupby('gene_id')[\"observed_tpm\"].max().reset_index().query('observed_tpm == 0')[\"gene_id\"])\n",
    "kaessmann_molten = kaessmann_molten.query('gene_id not in @nonexpressed_genes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9dcfffc1-0026-4ef6-902a-31166307c167",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = pd.read_csv(base_path_data + \"human_regions.bed\", names=[\"Chromosome\",\"Start\",\"End\",\"set\"], sep=\"\\t\").query('set == \"test\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0f795e9-385f-4889-a4f3-1d2d28199fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "length_df = gtf_df.df.query('Feature == \"exon\"')\n",
    "length_df[\"len\"] = length_df[\"End\"] - length_df[\"Start\"]\n",
    "length_df = length_df.groupby(['gene_id','transcript_id'])['len'].sum().reset_index()\n",
    "length_df = length_df.groupby(['gene_id'])['len'].median().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1680f3b-dc8f-4486-831c-69cdcbe7894f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute length for each gene\n",
    "gene_locs = gtf_df.df.query('Feature == \"gene\"')[[\"Chromosome\",\"Start\",\"End\",\"Score\",\"Strand\",\"Frame\",\"gene_id\"]]\n",
    "gene_locs = gene_locs.merge(length_df, on=\"gene_id\")\n",
    "gene_locs = gene_locs.loc[~gene_locs.gene_id.str.endswith(\"PAR_Y\")]\n",
    "gene_locs[\"gene_id\"] = gene_locs[\"gene_id\"].apply(lambda x: x.split('.')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "645bcc77-45e1-4d03-81d7-ec59359455c1",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# remove y and MT chromosome from gtex and kaessmann\n",
    "y_mt_chrom_genes = set(gene_locs.query('Chromosome == \"chrY\" or Chromosome == \"chrMT\"')[\"gene_id\"])\n",
    "gtex_molten = gtex_molten.query('gene_id not in @y_mt_chrom_genes')\n",
    "kaessmann_molten = kaessmann_molten.query('gene_id not in @y_mt_chrom_genes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c631f36b-2a3b-417a-bd6c-ae095276256f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test genes are those which are fully contained in a test region\n",
    "test_genes = set(test_set.merge(gene_locs, on=\"Chromosome\",suffixes=(\"\",\"_gene\"))\n",
    "                 .query('Start < Start_gene & End > End_gene')[\"gene_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "30fc43e3-cc6c-4b94-a51d-21749c6c4bb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train genes are all those which do not at all intersect a test region\n",
    "intersect_genes = set(test_set.merge(gene_locs, on=\"Chromosome\",suffixes=(\"\",\"_gene\"))\n",
    "                      .query('(Start < Start_gene & End > Start_gene) or (Start < End_gene & End > End_gene)')[\"gene_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8770f68a-b27e-4059-b07e-d1014035a891",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_pred(lm, X_train, X_test, y_train, y_test):\n",
    "    lm.fit(X_train, y_train)\n",
    "    y_pred_train = lm.predict(X_train).reshape(-1)\n",
    "    y_pred_test = lm.predict(X_test).reshape(-1)\n",
    "    return lm, y_pred_train, y_pred_test\n",
    "\n",
    "def manual_match_windowed(gene_df, obs_df, samples, sample_col, locs_df, windows, \n",
    "                          model_name, dataset_name,\n",
    "                          matching, print_alpha=False,\n",
    "                          remove_mean=True, covariates=[]):\n",
    "    import warnings\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "    windows = [str(x) if x != -1 else \"ingenome\" for x in windows]\n",
    "    cage_tracks = [k for k in gene_df.keys() if \"CAGE\" in k]\n",
    "    \n",
    "    \n",
    "    rows = []\n",
    "    predictions = []\n",
    "    model_dict = collections.defaultdict(dict)\n",
    "    for sample in set(samples):\n",
    "        print(sample)\n",
    "        # find matching tracks\n",
    "        match_tracks = matching[sample]\n",
    "        print(match_tracks)\n",
    "        # prepare data\n",
    "        subset = (obs_df.query('{} == @sample'.format(sample_col))\n",
    "                  .merge(locs_df[['gene_id','len']],on=\"gene_id\")\n",
    "                  .merge(gene_df.drop(columns=\"gene_name\"), on=[\"gene_id\"]))\n",
    "        subset = subset.query('(gene_id in @ test_genes) or (gene_id not in @intersect_genes)')\n",
    "        subset['log_obs'] = np.log10(subset['observed_tpm']+1)\n",
    "        subset[\"expressed\"] = subset[\"log_obs\"] > 0\n",
    "        subset['log_len'] = np.log10(subset['len'])\n",
    "        subset[[k for k in subset.keys() if \"CAGE\" in k]] = np.log10(subset[[k for k in subset.keys() if \"CAGE\" in k]]+1)\n",
    "        # train test split\n",
    "        subset_train = subset.query('gene_id not in @intersect_genes')\n",
    "        subset_test = subset.query('gene_id in @test_genes')\n",
    "        # remove mean\n",
    "        mean = subset_train['log_obs'].mean()\n",
    "        subset_train['log_obs'] = subset_train['log_obs'] - mean\n",
    "        subset_test['log_obs'] = subset_test['log_obs'] - mean\n",
    "        subset_pred = pd.concat([subset_train[[\"gene_id\",sample_col,\"expressed\",\"log_obs\"]],subset_test[[\"gene_id\",sample_col,\"expressed\",\"log_obs\"]]])\n",
    "        y_train = np.array(subset_train['log_obs'])\n",
    "        y_test = np.array(subset_test['log_obs'])\n",
    "        for wdw in windows:\n",
    "            wdw_cage_tracks = [x for x in cage_tracks if wdw in x and any(y in x for y in match_tracks)] + covariates\n",
    "            assert len(match_tracks) == len(wdw_cage_tracks)\n",
    "            X_train = np.array(subset_train[wdw_cage_tracks]).mean(axis=1)\n",
    "            X_test = np.array(subset_test[wdw_cage_tracks]).mean(axis=1)\n",
    "            mean = X_train.mean()\n",
    "            X_train = X_train - mean\n",
    "            X_test = X_test - mean\n",
    "            tpl = (y_test, X_test)\n",
    "            r = scipy.stats.pearsonr(*tpl)[0]\n",
    "            print(r)\n",
    "            r2 = r**2\n",
    "            print(r2)\n",
    "            rho = scipy.stats.spearmanr(*tpl)[0]\n",
    "            rows.append({sample_col:sample,\n",
    "                         \"window_size\":wdw,\n",
    "                         \"r\":r,\n",
    "                         \"R2\":r2,\n",
    "                         \"RMSE\":sklearn.metrics.mean_squared_error(*tpl,squared=False),\n",
    "                         \"rho\":rho})\n",
    "            subset_pred['log_pred_'+wdw] = np.concatenate([X_train,X_test])\n",
    "        predictions.append(subset_pred)\n",
    "            \n",
    "\n",
    "    corrs_lm = pd.DataFrame(rows)\n",
    "    corrs_lm[\"window_size\"] = corrs_lm[\"window_size\"].apply(lambda x: x if x != \"ingenome\" else \"Full\")\n",
    "    #corrs_lm[\"window_size\"] = pd.Categorical(corrs_lm[\"window_size\"], [x for x in windows if x != \"ingenome\"] + [\"Full\"])\n",
    "    corrs_lm.to_csv(base_path_results + dataset_name + \"_corrs_manualmatched_{}.tsv\".format(model_name), index=None, sep=\"\\t\")\n",
    "\n",
    "    #with open(base_path_results + dataset_name + \"_{}_manual_lm_pseudocount1.pkl\".format(model_name), 'wb') as handle:\n",
    "    #    pickle.dump(model_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "    pred_df = pd.concat(predictions)\n",
    "    pred_df.to_csv(base_path_results + dataset_name + \"_pred_manualmatched_{}.tsv\".format(model_name), index=None, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f57e5eed-8f45-4b45-b582-2174716a0f99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def agnostic_match_windowed(gene_df, obs_df, samples, sample_col, locs_df, windows, \n",
    "                          model_name, dataset_name):\n",
    "    import warnings\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "    windows = [str(x) if x != -1 else \"ingenome\" for x in windows]\n",
    "    cage_tracks = [k for k in gene_df.keys() if \"CAGE\" in k]\n",
    "    \n",
    "    \n",
    "    rows = []\n",
    "    predictions = []\n",
    "    model_dict = collections.defaultdict(dict)\n",
    "    for sample in set(samples):\n",
    "        print(sample)\n",
    "        # prepare data\n",
    "        subset = (obs_df.query('{} == @sample'.format(sample_col))\n",
    "                  .merge(locs_df[['gene_id','len']],on=\"gene_id\")\n",
    "                  .merge(gene_df.drop(columns=\"gene_name\"), on=[\"gene_id\"]))\n",
    "        subset = subset.query('(gene_id in @ test_genes) or (gene_id not in @intersect_genes)')\n",
    "        subset['log_obs'] = np.log10(subset['observed_tpm']+1)\n",
    "        subset[\"expressed\"] = subset[\"log_obs\"] > 0\n",
    "        subset['log_len'] = np.log10(subset['len'])\n",
    "        subset[[k for k in subset.keys() if \"CAGE\" in k]] = np.log10(subset[[k for k in subset.keys() if \"CAGE\" in k]]+1)\n",
    "        # train test split\n",
    "        subset_train = subset.query('gene_id not in @intersect_genes')\n",
    "        subset_test = subset.query('gene_id in @test_genes')\n",
    "        # remove mean\n",
    "        mean = subset_train['log_obs'].mean()\n",
    "        subset_train['log_obs'] = subset_train['log_obs'] - mean\n",
    "        subset_test['log_obs'] = subset_test['log_obs'] - mean\n",
    "        subset_pred = pd.concat([subset_train[[\"gene_id\",sample_col,\"expressed\",\"log_obs\"]],subset_test[[\"gene_id\",sample_col,\"expressed\",\"log_obs\"]]])\n",
    "        y_train = np.array(subset_train['log_obs'])\n",
    "        y_test = np.array(subset_test['log_obs'])\n",
    "        for wdw in windows:\n",
    "            wdw_cage_tracks = [x for x in cage_tracks if wdw in x] + covariates\n",
    "            X_train = np.array(subset_train[wdw_cage_tracks]).median(axis=1)\n",
    "            X_test = np.array(subset_test[wdw_cage_tracks]).median(axis=1)\n",
    "            mean = X_train.mean()\n",
    "            X_train = X_train - mean\n",
    "            X_test = X_test - mean\n",
    "            tpl = (y_test, X_test)\n",
    "            r = scipy.stats.pearsonr(*tpl)[0]\n",
    "            print(r)\n",
    "            r2 = r**2\n",
    "            print(r2)\n",
    "            rho = scipy.stats.spearmanr(*tpl)[0]\n",
    "            rows.append({sample_col:sample,\n",
    "                         \"window_size\":wdw,\n",
    "                         \"r\":r,\n",
    "                         \"R2\":r2,\n",
    "                         \"RMSE\":sklearn.metrics.mean_squared_error(*tpl,squared=False),\n",
    "                         \"rho\":rho})\n",
    "            subset_pred['log_pred_'+wdw] = np.concatenate([X_train,X_test])\n",
    "        predictions.append(subset_pred)\n",
    "            \n",
    "\n",
    "    corrs_lm = pd.DataFrame(rows)\n",
    "    corrs_lm[\"window_size\"] = corrs_lm[\"window_size\"].apply(lambda x: x if x != \"ingenome\" else \"Full\")\n",
    "    #corrs_lm[\"window_size\"] = pd.Categorical(corrs_lm[\"window_size\"], [x for x in windows if x != \"ingenome\"] + [\"Full\"])\n",
    "    corrs_lm.to_csv(base_path_results + dataset_name + \"_corrs_agnosticmatched_{}.tsv\".format(model_name), index=None, sep=\"\\t\")\n",
    "\n",
    "    #with open(base_path_results + dataset_name + \"_{}_manual_lm_pseudocount1.pkl\".format(model_name), 'wb') as handle:\n",
    "    #    pickle.dump(model_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "    pred_df = pd.concat(predictions)\n",
    "    pred_df.to_csv(base_path_results + dataset_name + \"_pred_agnosticmatched_{}.tsv\".format(model_name), index=None, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6706b9e6-a656-41b2-abc3-7ab3e8ce05b9",
   "metadata": {},
   "source": [
    "# Load Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f92383-6327-424f-8ce2-6a12f35a2b59",
   "metadata": {},
   "source": [
    "## GTEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d9e8cad1-27ef-453a-977b-70e68505b917",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(base_path_results  + \"tss_sim-enformer-latest_results.tsv\", 'r') as tsv:\n",
    "    header = tsv.readline()\n",
    "    cols = header.split(\"\\t\")\n",
    "cols = [k for k in cols if \"CAGE\" in k and k.endswith(\"landmark_sum\")]\n",
    "\n",
    "results = pd.read_csv(base_path_results + \"tss_sim-enformer-latest_results.tsv\",sep=\"\\t\",\n",
    "                     usecols = ['gene_id', 'gene_name', \"transcript_id\",\"window_type\",\"window_size\"] + cols)\n",
    "\n",
    "results = (results.groupby([\"gene_id\",\"gene_name\",\"window_type\",\"window_size\"])[[x for x in results.keys() if \"CAGE\" in x]].mean().reset_index())\n",
    "\n",
    "universal_samples = ['Clontech Human Universal Reference Total RNA', 'SABiosciences XpressRef Human Universal Total RNA', 'CAGE:Universal RNA - Human Normal Tissues Biochain']\n",
    "track_cols = [k for k in results.keys() if \"CAGE\" in k and k.endswith(\"landmark_sum\") and not any(x in k for x in universal_samples)]\n",
    "\n",
    "gene_df = results[[\"gene_id\",\"gene_name\",\"window_type\",\"window_size\"] + track_cols]\n",
    "gene_df = gene_df[[\"gene_id\",\"gene_name\",\"window_size\"]+track_cols].pivot([\"gene_id\",\"gene_name\"],[\"window_size\"],track_cols).reset_index()\n",
    "gene_df.columns = ([x for x,y in zip(gene_df.columns.get_level_values(0),gene_df.columns.get_level_values(1)) if y == \"\"]\n",
    "                   + [x + \"_\" + str(y) for x,y in zip(gene_df.columns.get_level_values(0),gene_df.columns.get_level_values(1)) if y != \"\"])\n",
    "gene_df = gene_df.rename(columns={k+\"_-1\":k+\"_ingenome\" for k in track_cols})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "629de9b0-1eb4-4cf6-b2cd-7cc08126bd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "windows = sorted(list(set(results[\"window_size\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "392f7158-8f63-4489-bde5-55b2bad5f487",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_df.to_csv(base_path_results + \"tss_sim-enformer-gene_df.tsv\", sep=\"\\t\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904ee65a-1232-4f59-8e87-666d97e10165",
   "metadata": {},
   "source": [
    "## Basenji2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7cbf71ad-2eee-47e7-a42c-3c1266c89dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(base_path_results + \"tss_sim-basenji2-latest_results.tsv\", 'r') as tsv:\n",
    "    header = tsv.readline()\n",
    "    cols = header.split(\"\\t\")\n",
    "cols = [k for k in cols if \"CAGE\" in k and k.endswith(\"landmark_sum\")]\n",
    "\n",
    "basenji2_results = pd.read_csv(base_path_results + \"tss_sim-basenji2-latest_results.tsv\",sep=\"\\t\",\n",
    "                              usecols = ['gene_id', 'gene_name', 'orient', 'offset', 'window_size'] + cols)\n",
    "basenji2_results = (basenji2_results.groupby([\"gene_id\",\"gene_name\"])[[x for x in basenji2_results.keys() if \"CAGE\" in x]].mean().reset_index())\n",
    "\n",
    "universal_samples = ['Clontech Human Universal Reference Total RNA', 'SABiosciences XpressRef Human Universal Total RNA', 'CAGE:Universal RNA - Human Normal Tissues Biochain']\n",
    "track_cols = [k for k in basenji2_results.keys() if \"CAGE\" in k and k.endswith(\"landmark_sum\") and not any(x in k for x in universal_samples)]\n",
    "\n",
    "basenji2_gene_df = basenji2_results[[\"gene_id\",\"gene_name\"] + track_cols].rename(columns={t:t+\"_basenji2\" for t in track_cols})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1c70342f-f33c-4788-8820-375eab75b2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "basenji2_gene_df.to_csv(base_path_results + \"tss_sim-basenji2-gene_df.tsv\", sep=\"\\t\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39df503-88f8-4294-9cca-0ac657ccb0ab",
   "metadata": {},
   "source": [
    "# Manual matching"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b2d3bf-f986-46df-9630-1c0dfd8e1b26",
   "metadata": {},
   "source": [
    "## GTEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "07ba253a-0eda-4c86-aa5b-f3f7a85b1aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "matching = {\n",
    "    'Adipose - Subcutaneous':['Adipocyte - subcutaneous'], # good\n",
    "    'Adipose - Visceral (Omentum)':['Adipocyte - omental'], # good\n",
    "    'Adrenal Gland':['adrenal gland'], # good\n",
    "    'Artery - Aorta':['aorta'], # good\n",
    "    'Artery - Coronary':['Coronary Artery'], # ok\n",
    "    'Artery - Tibial':['artery, adult'], # bad\n",
    "    'Bladder':[':bladder, adult'], # good\n",
    "    'Brain - Amygdala':['amygdala'], # good\n",
    "    'Brain - Anterior cingulate cortex (BA24)':['brain,', 'cortex'], # bad\n",
    "    'Brain - Caudate (basal ganglia)':['caudate'], # good\n",
    "    'Brain - Cerebellar Hemisphere':['cerebellum'], # ok\n",
    "    'Brain - Cerebellum':['cerebellum'], # good\n",
    "    'Brain - Cortex':['brain,', 'cortex'], # bad\n",
    "    'Brain - Frontal Cortex (BA9)':['front'], # ok\n",
    "    'Brain - Hippocampus':['hippo'], # good\n",
    "    'Brain - Hypothalamus':['thalamus'], # ok\n",
    "    'Brain - Nucleus accumbens (basal ganglia)':['accumbens'], # good\n",
    "    'Brain - Putamen (basal ganglia)':['putamen'], # good \n",
    "    'Brain - Spinal cord (cervical c-1)':['spinal cord'], # good\n",
    "    'Brain - Substantia nigra':['nigra'], # good\n",
    "    'Breast - Mammary Tissue':['mamma', 'breast,'], # ok\n",
    "    'Cells - Cultured fibroblasts':['fibro'], # bad\n",
    "    'Cells - EBV-transformed lymphocytes':['lymphocyte', 'Natural Killer', '\\+ T cell', '\\+ B cell'], # bad\n",
    "    'Cervix - Ectocervix':['cervix'], # ok\n",
    "    'Cervix - Endocervix':['cervix'], # ok\n",
    "    'Colon - Sigmoid':['colon,'], # ok\n",
    "    'Colon - Transverse':['colon,'], # ok\n",
    "    'Esophagus - Gastroesophageal Junction':['esoph'], # bad \n",
    "    'Esophagus - Mucosa':['esoph'], # bad \n",
    "    'Esophagus - Muscularis':['esoph'], # bad \n",
    "    'Fallopian Tube':['uterus','uterine'], # none\n",
    "    'Heart - Atrial Appendage':['heart'], # bad\n",
    "    'Heart - Left Ventricle':['ventricle'], # good \n",
    "    'Kidney - Cortex':['kidney,'], # ok\n",
    "    'Kidney - Medulla':['kidney,'], # ok \n",
    "    'Liver':['liver,'], # good\n",
    "    'Lung':['lung,'], # good\n",
    "    'Minor Salivary Gland':['salivary gland'], # good\n",
    "    'Muscle - Skeletal':['skeletal muscle'], # good\n",
    "    'Nerve - Tibial':['nerve'], # bad\n",
    "    'Ovary':['ovary, a'], # good\n",
    "    'Pancreas':['pancreas,'], # good\n",
    "    'Pituitary':['pitu'], # good\n",
    "    'Prostate':['prostate, a'], # good\n",
    "    'Skin - Not Sun Exposed (Suprapubic)':['skin,'], # ok\n",
    "    'Skin - Sun Exposed (Lower leg)':['skin,','palm'], # ok\n",
    "    'Small Intestine - Terminal Ileum':['small intestine'], # ok\n",
    "    'Spleen':['spleen'], # good \n",
    "    'Stomach':['stomach'], # ok\n",
    "    'Testis':['testis'], # good \n",
    "    'Thyroid':['thyroid,'], # good\n",
    "    'Uterus':['uterus'], # good\n",
    "    'Vagina':['vagina'], # good\n",
    "    'Whole Blood':['blood,','Whole blood'] # good\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "226d7f61-0a51-4661-984c-bc674dd73ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtex_to_cage = {}\n",
    "for gtex_tissue in tissues:\n",
    "    keywords = matching[gtex_tissue]\n",
    "    matched_tracks = []\n",
    "    for key in keywords:\n",
    "        matched_tracks += list(target_df.loc[target_df.description.str.contains(key, case=False) &\n",
    "                                       target_df.description.str.contains(\"CAGE\", case=False)]['description'])\n",
    "    if len(matched_tracks) == 0:\n",
    "        print(gtex_tissue)\n",
    "    else:\n",
    "        gtex_to_cage[gtex_tissue] = matched_tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9840bf-8fa2-4a4d-9586-24ed35789174",
   "metadata": {},
   "source": [
    "### Enformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e75cb6b-c360-4aff-a7c8-1760eb83f0f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "manual_match_windowed(gene_df, gtex_molten, tissues, \"tissue\", gene_locs, windows, \n",
    "                      model_name = \"enformer\", dataset_name=\"gtex\",\n",
    "                      matching=gtex_to_cage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1e2627-3f23-4e75-bfd8-1d4cdb0a8e55",
   "metadata": {},
   "source": [
    "### Basenji2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "310bcea3-71a8-4129-bc0e-571d36a3a504",
   "metadata": {},
   "outputs": [],
   "source": [
    "manual_match_windowed(basenji2_gene_df, gtex_molten, tissues, \"tissue\", gene_locs, windows=[\"basenji2\"], \n",
    "                      model_name = \"basenji2\", dataset_name=\"gtex\",\n",
    "                      matching=gtex_to_cage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006f2b38-cccd-433b-a0d7-88d848268e6a",
   "metadata": {},
   "source": [
    "## Kaessman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "df3f57a2-6969-47b6-b44d-0648a35eb50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "kaessman_matching = {}\n",
    "for smpl in set(kaessmann_molten['sample']):\n",
    "    organ = smpl.split('.')[0]\n",
    "    period = smpl.split('.')[1]\n",
    "    if organ == \"Brain\":\n",
    "        if \"wpc\" in period or period in [\"newborn\", \"toddler\", \"infant\"]:\n",
    "            kaessman_matching[smpl] = [\"brain, fetal\"]\n",
    "        else:\n",
    "            kaessman_matching[smpl] = [\"brain, adult\"]\n",
    "    elif organ == \"Cerebellum\":\n",
    "        if \"wpc\" in period or period in [\"newborn\", \"toddler\", \"infant\"]:\n",
    "            kaessman_matching[smpl] = [\"cerebellum, newborn\"]\n",
    "        else:\n",
    "            kaessman_matching[smpl] = [\"cerebellum, adult\", \"cerebellum - adult\"]\n",
    "    elif organ == \"Heart\":\n",
    "        if \"wpc\" in period or period in [\"newborn\", \"toddler\", \"infant\"]:\n",
    "            kaessman_matching[smpl] = [\"heart, fetal\"]\n",
    "        else:\n",
    "            kaessman_matching[smpl] = [\"heart, adult, pool\"] # exclude diseased\n",
    "    elif organ == \"Kidney\":\n",
    "        if \"wpc\" in period or period in [\"newborn\", \"toddler\", \"infant\"]:\n",
    "            kaessman_matching[smpl] = [\"kidney, fetal\"]\n",
    "        else:\n",
    "            kaessman_matching[smpl] = [\"kidney, adult\"]\n",
    "    elif organ == \"Liver\":\n",
    "        if \"wpc\" in period or period in [\"newborn\", \"toddler\", \"infant\"]:\n",
    "            kaessman_matching[smpl] = [\"liver, fetal\"]\n",
    "        else:\n",
    "            kaessman_matching[smpl] = [\"liver, adult\"]\n",
    "    elif organ == \"Ovary\":\n",
    "        kaessman_matching[smpl] = [\"ovary, adult\"]\n",
    "    elif organ == \"Testis\":\n",
    "        kaessman_matching[smpl] = [\"testis, adult\"]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "13227258-e95b-4552-af0a-0c8c33c2f9d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "kaessman_to_cage = {}\n",
    "for smpl in set(kaessmann_molten['sample']):\n",
    "    keywords = kaessman_matching[smpl]\n",
    "    matched_tracks = []\n",
    "    for key in keywords:\n",
    "        matched_tracks += list(target_df.loc[target_df.description.str.contains(key, case=False) &\n",
    "                                       target_df.description.str.contains(\"CAGE\", case=False)]['description'])\n",
    "    if len(matched_tracks) == 0:\n",
    "        print(smpl)\n",
    "    else:\n",
    "        kaessman_to_cage[smpl] = matched_tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e84caa4-ba00-4464-8194-a20ab961a2c6",
   "metadata": {},
   "source": [
    "### Enformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2775ee3c-bb55-424f-9ef3-6ec2130845f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "manual_match_windowed(gene_df, kaessmann_molten, dev_tissues, \"sample\", gene_locs, windows, \n",
    "                     model_name = \"enformer\", dataset_name=\"kaessmann\",\n",
    "                      matching=kaessman_to_cage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fca05c5-ab8c-48b9-ad29-09f98d74e2d3",
   "metadata": {},
   "source": [
    "### Basenji2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b4f8b83-8182-4db7-8085-8d419eccc40c",
   "metadata": {},
   "outputs": [],
   "source": [
    "manual_match_windowed(basenji2_gene_df, kaessmann_molten, dev_tissues, \"sample\", gene_locs, windows=[\"basenji2\"], \n",
    "                     model_name = \"basenji2\", dataset_name=\"kaessmann\",\n",
    "                     matching=kaessman_to_cage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ddc08f-5dd4-4d74-86a6-3b68e5f7d266",
   "metadata": {},
   "source": [
    "# Analyze correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6e7dd3-2be6-4328-b56f-a6fce2a3a0e8",
   "metadata": {},
   "source": [
    "## GTEX - Between genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a83dbfcb-5e46-46a5-8b12-6e9dcaae5d7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "enformer_gtex_corrs = pd.read_csv(base_path_results + \"gtex_corrs_manualmatched_enformer.tsv\", sep=\"\\t\")[[\"tissue\",\"r\", \"window_size\"]]\n",
    "enformer_gtex_corrs[\"model\"] = \"Enformer\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f03cb71e-73da-4e98-a32d-a0bad8f07f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/tmp/karollus/ipykernel_2325407/3139867262.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "r    0.705987\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enformer_gtex_corrs.query('window_size == \"Full\"').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38a0a3a5-cae3-4d63-b4e6-feeb64efa30c",
   "metadata": {},
   "outputs": [],
   "source": [
    "basenji2_gtex_corrs = pd.read_csv(base_path_results + \"gtex_corrs_manualmatched_basenji2.tsv\", sep=\"\\t\")[[\"tissue\",\"r\"]]\n",
    "basenji2_gtex_corrs[\"model\"] = \"Basenji2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa065076-b9a7-4661-8b25-0d049ff2fb41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/tmp/karollus/ipykernel_2325407/650338664.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "r    0.63606\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basenji2_gtex_corrs.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a552afe1-8cca-4cba-8ac6-a8b625fb8edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtex_corrs = pd.concat([enformer_gtex_corrs.query('window_size == \"Full\"')[[\"tissue\",\"r\",\"model\"]], basenji2_gtex_corrs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1af0664a-09b8-44d0-b4d5-e631ec5e2ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 477,
       "width": 583
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (1422615737947)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = 1\n",
    "p9.options.figure_size = (6.4*scale, 4.8*scale)\n",
    "\n",
    "gtex_corrs[\"model_cat\"] = pd.Categorical(gtex_corrs[\"model\"],\n",
    "                                             categories=[\"Basenji2\", \"Enformer\"])\n",
    "\n",
    "p = (p9.ggplot(data=gtex_corrs,mapping=p9.aes(x=\"model_cat\",y=\"r\"))\n",
    " + p9.geom_boxplot()\n",
    " + p9.labs(x=\"Model\",y=\"Pearson correlation between predicted\\nand measured log expression\", title=\"GTEx\")\n",
    " + p9.theme(axis_title=p9.element_text(size=10), title=p9.element_text(size=10),\n",
    "           axis_text_x=p9.element_text(rotation=30, hjust=1))\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7864e258-09a7-4438-8a79-9b5b30dc3a49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/modules/i12g/anaconda/envs/karollus-tfhub/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/opt/modules/i12g/anaconda/envs/karollus-tfhub/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: Graphics/xsup_manual_match_gtex.png\n"
     ]
    }
   ],
   "source": [
    "p.save(\"Graphics/\" + \"xsup_manual_match_gtex\" + \".png\", width=6.4, height=4.8, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ee9972aa-3c8a-4599-b13c-4fa38fee152d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 659,
       "width": 583
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (1422514887241)>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = 1\n",
    "p9.options.figure_size = (6.4*scale, 4.8*scale)\n",
    "\n",
    "plot_df = gtex_corrs.query('model in [\"Basenji2\",\"Enformer\"]')\n",
    "plot_df[\"tissue_cat\"] = pd.Categorical(plot_df['tissue'],\n",
    "                                       plot_df.query('model == \"Enformer\"').sort_values('r')['tissue'])\n",
    "\n",
    "p = (p9.ggplot(data=plot_df,mapping=p9.aes(x=\"tissue_cat\",y=\"r\",color=\"model\"))\n",
    " #+ p9.geom_col(position=\"dodge\")\n",
    " + p9.geom_point(size=2.5)\n",
    " + p9.coords.coord_cartesian(ylim=(0.57,0.83))\n",
    " + p9.labs(x=\"\",y=\"Correlation of predicted with\\nmeasured log expression (GTEx)\")\n",
    " + p9.theme(axis_title=p9.element_text(size=10),\n",
    "           axis_text_x=p9.element_text(rotation=90, hjust=1),\n",
    "           legend_box_margin=0, legend_key_size=9, \n",
    "            legend_text=p9.element_text(size=9),\n",
    "            legend_background=p9.element_blank(),\n",
    "            legend_title=p9.element_text(size=9), legend_position=(0.25,0.8))\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "003f09ba-922f-4e36-8d61-dade1910303b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/modules/i12g/anaconda/envs/karollus-tfhub/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 7.2 x 4.8 in image.\n",
      "/opt/modules/i12g/anaconda/envs/karollus-tfhub/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: Graphics/xsup_manual_gtex_tissues.png\n"
     ]
    }
   ],
   "source": [
    "p.save(\"Graphics/\" + \"xsup_manual_gtex_tissues\" + \".png\", width=7.2, height=4.8, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cd4c3403-bbba-4e15-99c2-5cb0ab6cf6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "enformer_gtex_modelmatched_corrs = pd.read_csv(base_path_results + \"gtex_corrs_modelmatched_enformer.tsv\", sep=\"\\t\")[[\"tissue\",\"r\", \"window_size\"]].query('window_size == \"Full\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9a84839c-c990-40e9-bbc8-aafc30a939db",
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_df = gtex_corrs.query('model == \"Enformer\"')[['tissue','r']].merge(enformer_gtex_modelmatched_corrs[['tissue','r']], on='tissue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c87cf75a-4361-4faa-80b1-735572de031b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.7430150562226033, pvalue=1.2473168520783197e-10)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.spearmanr(compare_df['r_x'],compare_df['r_y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f946b508-e723-4acd-80a7-be1686e9df03",
   "metadata": {},
   "source": [
    "## GTEX - Between tissues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "846b7e77-f51c-4c54-ac3f-1eab667dac1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tissue_pred_df = (pd.read_csv(base_path_results + \"gtex_pred_manualmatched_enformer.tsv\", sep=\"\\t\")\n",
    "                  [[\"gene_id\",\"tissue\",\"log_obs\",\"log_pred_ingenome\",\"expressed\"]]\n",
    "                  .rename(columns={\"log_pred_ingenome\":\"log_pred_enformer\"})\n",
    "                 )\n",
    "tissue_pred_df_basenji2 = (pd.read_csv(base_path_results + \"gtex_pred_manualmatched_basenji2.tsv\", sep=\"\\t\")\n",
    "                          [[\"gene_id\",\"tissue\",\"log_pred_basenji2\"]]\n",
    "                         )\n",
    "tissue_pred_df = (tissue_pred_df\n",
    "                  .merge(tissue_pred_df_basenji2, on=[\"gene_id\", \"tissue\"])\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f0c056de-025a-4271-8e90-4aedae401a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tissue_var_df_molten = tissue_pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9786a043-77ae-4645-839e-d9afe3a310ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "\n",
    "tissue_var_df_molten_indexed = tissue_var_df_molten.set_index(\"gene_id\")\n",
    "for gene in set(tissue_var_df_molten.query('gene_id in @test_genes')[\"gene_id\"]):\n",
    "    subset = tissue_var_df_molten_indexed.loc[gene]\n",
    "    for col in [x for x in subset.keys() if x.startswith(\"log_pred\")]:\n",
    "        row_dict = {\"gene_id\":gene}\n",
    "        row_dict[\"model\"] = col.split(\"_\")[-1]\n",
    "        tpl = (subset[\"log_obs\"],subset[col])\n",
    "        corr_test =  scipy.stats.pearsonr(*tpl)\n",
    "        r = corr_test[0]\n",
    "        row_dict[\"R\"] = r\n",
    "        row_dict[\"p\"] = corr_test[1]\n",
    "        row_dict[\"rho\"] = scipy.stats.spearmanr(*tpl)[0]\n",
    "        row_dict[\"R-squared\"]  = sklearn.metrics.r2_score(*tpl)\n",
    "        row_dict[\"pred_mean_log_expr_enformer\"] = subset[\"log_pred_enformer\"].mean()\n",
    "        row_dict[\"pred_std_log_expr_enformer\"] = subset[\"log_pred_enformer\"].std()\n",
    "        row_dict[\"pred_mean_log_expr_basenji2\"] = subset[\"log_pred_basenji2\"].mean()\n",
    "        row_dict[\"pred_std_log_expr_basenji2\"] = subset[\"log_pred_basenji2\"].std()\n",
    "        row_dict[\"mean_log_expr\"] = subset[\"log_obs\"].mean()\n",
    "        row_dict[\"std_log_expr\"] = subset[\"log_obs\"].std()\n",
    "        row_dict[\"mae_log_expr\"] = np.abs(subset[\"log_obs\"] - subset[\"log_obs\"].mean()).mean()\n",
    "        row_dict[\"dyn_range_log_expr\"] = subset[\"log_obs\"].quantile([0.05,0.95]).max() - subset[\"log_obs\"].quantile([0.05,0.95]).min()\n",
    "        rows.append(row_dict)\n",
    "        \n",
    "between_tissue_corrs = pd.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7fb963cf-e801-49f6-9eaa-92969847e6e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.6870913137644379, 8.315651196213907e-199)\n",
      "(0.7497290763356315, 2.342178466518381e-256)\n",
      "(0.4742470498616209, 1.798901205560912e-80)\n",
      "(0.598091945718725, 2.110986046300726e-138)\n"
     ]
    }
   ],
   "source": [
    "gene_mean_df = (between_tissue_corrs\n",
    "           .groupby('gene_id')\n",
    "           [[\"pred_mean_log_expr_enformer\",\"pred_std_log_expr_enformer\",\"pred_mean_log_expr_basenji2\",\"pred_std_log_expr_basenji2\",\"mean_log_expr\" ,\"std_log_expr\"]]\n",
    "           .mean()\n",
    "           .reset_index()\n",
    "          )\n",
    "# prediction of gene mean\n",
    "print(scipy.stats.pearsonr(gene_mean_df[\"mean_log_expr\"],gene_mean_df[\"pred_mean_log_expr_basenji2\"]))\n",
    "print(scipy.stats.pearsonr(gene_mean_df[\"mean_log_expr\"],gene_mean_df[\"pred_mean_log_expr_enformer\"]))\n",
    "# prediction of gene standard deviation\n",
    "print(scipy.stats.pearsonr(gene_mean_df[\"std_log_expr\"],gene_mean_df[\"pred_std_log_expr_basenji2\"]))\n",
    "print(scipy.stats.pearsonr(gene_mean_df[\"std_log_expr\"],gene_mean_df[\"pred_std_log_expr_enformer\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "496bd437-189a-4f14-b7d0-b8005716a144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3411621084355805"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.r2_score(tissue_pred_df.query('gene_id in @test_genes')[\"log_obs\"],\n",
    "                         tissue_pred_df.query('gene_id in @test_genes')[\"log_pred_enformer\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f3431d54-595b-4586-80df-71069edf578d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_dev_df = tissue_pred_df.merge(gene_mean_df, on=\"gene_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b395679f-59e2-439d-8172-57f1d319de3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_dev_df[\"condition\"] = gene_dev_df[\"log_obs\"] - gene_dev_df['mean_log_expr']\n",
    "\n",
    "gene_dev_df['log2_fc'] = gene_dev_df['condition']/np.log10(2)\n",
    "gene_dev_df['pred_log2_fc'] = (gene_dev_df['log_pred_enformer'] - gene_dev_df['pred_mean_log_expr_enformer'])/np.log10(2)\n",
    "gene_dev_df['pred_log2_fc_basenji2'] = (gene_dev_df['log_pred_basenji2'] - gene_dev_df['pred_mean_log_expr_basenji2'])/np.log10(2)\n",
    "gene_dev_df[\"abs_log2_fc\"] = np.abs(gene_dev_df[\"log2_fc\"])\n",
    "gene_dev_df[\"abs_pred_log2_fc\"] = np.abs(gene_dev_df[\"pred_log2_fc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "83937cb6-0690-4068-a2d7-b14dc2efaf3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.4218120814517516, 0.0)\n",
      "(0.3359412890973684, 0.0)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(gene_dev_df['log2_fc'], gene_dev_df['pred_log2_fc']))\n",
    "print(scipy.stats.pearsonr(gene_dev_df['log2_fc'], gene_dev_df['pred_log2_fc_basenji2']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "abefb9ff-f6ef-4121-944c-8c6bcb602eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 443,
       "width": 637
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (1422515559885)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = 1\n",
    "p9.options.figure_size = (6.4*scale, 4.8*scale)\n",
    "\n",
    "p = (p9.ggplot(data=gene_dev_df,mapping=p9.aes(x=\"pred_log2_fc\",y=\"log2_fc\",))\n",
    " #+ p9.geom_col(position=\"dodge\")\n",
    " + p9.geom_bin2d(bins=100, raster=True)\n",
    " + p9.scale_fill_continuous(ListedColormap(cm.get_cmap('viridis', 512)(np.linspace(0.25, 1, 512))), trans=\"log10\")\n",
    " + p9.geom_smooth(method=\"lm\",color=\"blue\")\n",
    " + p9.geom_vline(xintercept = 1)\n",
    " + p9.geom_vline(xintercept = -1)\n",
    " + p9.geom_hline(yintercept = 1)\n",
    " + p9.geom_hline(yintercept = -1)\n",
    " + p9.labs(x=\"Predicted $\\mathregular{log_2}$ deviation\\nfrom gene mean\",y=\"Measured $\\mathregular{log_2}$ deviation\\nfrom gene mean\")\n",
    " + p9.theme(legend_text=p9.element_text(size=9),legend_title=p9.element_text(size=9),\n",
    "            legend_box_margin=0,legend_key_width=5,axis_title=p9.element_text(size=10))#\n",
    " #+ p9.scale_y_log10()\n",
    " #+ p9.labs(x=\"\",y=\"Density\")\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "10c27f12-d561-4dda-bdf6-ab62406c2d0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/modules/i12g/anaconda/envs/karollus-tfhub/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/opt/modules/i12g/anaconda/envs/karollus-tfhub/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: Graphics/xsup_manual_gtex_between.png\n"
     ]
    }
   ],
   "source": [
    "p.save(\"Graphics/\" + \"xsup_manual_gtex_between\" + \".png\", width=6.4, height=4.8, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "261f5179-e40f-4456-b9ee-7ebd80c76f79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GTEx</td>\n",
       "      <td>FPR</td>\n",
       "      <td>0.166610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GTEx</td>\n",
       "      <td>Precision</td>\n",
       "      <td>0.344615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GTEx</td>\n",
       "      <td>Recall</td>\n",
       "      <td>0.296588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GTEx</td>\n",
       "      <td>MCC</td>\n",
       "      <td>0.199327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GTEx</td>\n",
       "      <td>Prevalence</td>\n",
       "      <td>0.162190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset      Metric     Score\n",
       "0    GTEx         FPR  0.166610\n",
       "1    GTEx   Precision  0.344615\n",
       "2    GTEx      Recall  0.296588\n",
       "3    GTEx         MCC  0.199327\n",
       "4    GTEx  Prevalence  0.162190"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_df = gene_dev_df.copy()\n",
    "classify_df[\"differential\"] = classify_df['abs_log2_fc'] > 1\n",
    "classify_df[\"pred_differential\"] = classify_df['abs_pred_log2_fc'] > 1.15\n",
    "\n",
    "classify_rows_gtex = []\n",
    "classify_rows_gtex.append({\"Dataset\":\"GTEx\",\"Metric\":\"FPR\",\"Score\":np.sum(classify_df[\"pred_differential\"])/np.sum(~classify_df[\"differential\"])})\n",
    "classify_rows_gtex.append({\"Dataset\":\"GTEx\",\"Metric\":\"Precision\",\"Score\":sklearn.metrics.precision_score(classify_df[\"differential\"], classify_df[\"pred_differential\"])})\n",
    "classify_rows_gtex.append({\"Dataset\":\"GTEx\",\"Metric\":\"Recall\",\"Score\":sklearn.metrics.recall_score(classify_df[\"differential\"], classify_df[\"pred_differential\"])})\n",
    "classify_rows_gtex.append({\"Dataset\":\"GTEx\",\"Metric\":\"MCC\",\"Score\":sklearn.metrics.matthews_corrcoef(classify_df[\"differential\"], classify_df[\"pred_differential\"])})\n",
    "classify_rows_gtex.append({\"Dataset\":\"GTEx\",\"Metric\":\"Prevalence\",\"Score\":np.sum(classify_df[\"differential\"])/len(classify_df[\"differential\"])})\n",
    "pd.DataFrame(classify_rows_gtex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5454101-c111-47b1-9dfd-ab42b1d1f957",
   "metadata": {},
   "source": [
    "## Kaessmann - Between genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "53d57d44-e27e-4eb5-965f-f3e6e7d9617b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/tmp/karollus/ipykernel_2325407/546196250.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "r    0.691088\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enformer_kaessmann_corrs = pd.read_csv(base_path_results + \"kaessmann_corrs_manualmatched_enformer.tsv\", sep=\"\\t\")[[\"sample\",\"r\", \"window_size\"]]\n",
    "enformer_kaessmann_corrs[\"model\"] = \"Enformer\"\n",
    "enformer_kaessmann_corrs.query('window_size == \"Full\"').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "44931345-ff2e-49c5-aabd-8239bebc7d2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/tmp/karollus/ipykernel_2325407/2471711924.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "r    0.642293\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basenji2_kaessmann_corrs = pd.read_csv(base_path_results + \"kaessmann_corrs_manualmatched_basenji2.tsv\", sep=\"\\t\")[[\"sample\",\"r\"]]\n",
    "basenji2_kaessmann_corrs[\"model\"] = \"Basenji2\"\n",
    "basenji2_kaessmann_corrs.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bb64911f-293d-4f89-995e-bc03b8a2934a",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_corrs = pd.concat([enformer_kaessmann_corrs.query('window_size == \"Full\"')[[\"sample\",\"r\",\"model\"]], basenji2_kaessmann_corrs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "45afb91b-af51-4a5e-be08-43dd57fa80d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_corrs[\"tissue\"] = combined_corrs[\"sample\"].apply(lambda x: x.split('.')[0])\n",
    "combined_corrs[\"stage\"] = combined_corrs[\"sample\"].apply(lambda x: x.split('.')[1].lower())\n",
    "combined_corrs[\"stage_num\"] = combined_corrs[\"sample\"].apply(lambda x: int(x.split('.')[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "986b6be6-80b6-4193-b2d6-ecbf74fdac32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 428,
       "width": 575
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (1422515400445)>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = 1\n",
    "p9.options.figure_size = (6.4*scale, 4.8*scale)\n",
    "\n",
    "combined_corrs[\"model_cat\"] = pd.Categorical(combined_corrs[\"model\"],\n",
    "                                             categories=[\"Basenji2\", \"Enformer\"])\n",
    "\n",
    "p = (p9.ggplot(data=combined_corrs\n",
    "           ,mapping=p9.aes(x=\"model_cat\",y=\"r\"))\n",
    " + p9.geom_boxplot()\n",
    " + p9.labs(x=\"Model\",y=\"Correlation of predicted with measured\\nlog expression (Cardoso-Moreira)\")\n",
    " + p9.theme(axis_title=p9.element_text(size=10))\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fbab981d-a94f-4358-9768-266c80f41b0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 523,
       "width": 549
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (1422515306999)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stage_cats = ['4wpc','5wpc','6wpc','7wpc','8wpc','9wpc','10wpc','11wpc','12wpc','13wpc','16wpc','18wpc','19wpc','20wpc','newborn','infant','toddler',\n",
    "              'school','youngteenager','teenager','oldteenager','youngadult','youngmidage','oldermidage','senior']\n",
    "\n",
    "plot_df = combined_corrs.query('model in [\"Enformer\"]')\n",
    "\n",
    "\n",
    "\n",
    "scale = 1.0\n",
    "p9.options.figure_size = (6.4*scale, 4.8*scale)\n",
    "\n",
    "\n",
    "plot_df = plot_df.query('stage not in [\"youngteenager\",\"oldteenager\"]')\n",
    "plot_df[\"stage\"] = pd.Categorical(plot_df[\"stage\"],\n",
    "                                     categories=stage_cats)\n",
    "\n",
    "p = (p9.ggplot(data=plot_df\n",
    "           ,mapping=p9.aes(x=\"stage\",y=\"r\"))\n",
    " #+ p9.geom_col(position=\"dodge\")\n",
    " + p9.geom_boxplot(outlier_size=0.5)\n",
    " + p9.geom_point(size=0.5)\n",
    " #+ p9.coords.coord_cartesian(ylim=(0.7,0.84))\n",
    " + p9.labs(x=\"Development Stage\",y=\"\", title=\"Cardoso-Moreira et al.\")\n",
    " + p9.theme(axis_title=p9.element_text(size=10), title=p9.element_text(size=10),\n",
    "            legend_background=p9.element_blank(),\n",
    "            legend_text=p9.element_text(size=8),legend_key_size=8,\n",
    "            legend_title=p9.element_text(size=8), axis_text_x=p9.element_text(rotation=90, hjust=1))\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "72772c1d-a7e0-4ffa-b425-9d64ae67cb46",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/modules/i12g/anaconda/envs/karollus-tfhub/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/opt/modules/i12g/anaconda/envs/karollus-tfhub/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: Graphics/xsup_manual_kaessmann_stages.png\n"
     ]
    }
   ],
   "source": [
    "p.save(\"Graphics/\" + \"xsup_manual_kaessmann_stages\" + \".png\", width=6.4, height=4.8, dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd761cde-287d-4126-89f0-049c3fb4a04b",
   "metadata": {},
   "source": [
    "## Kaessmann - Between samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6e996da6-70c8-4abc-87cc-714bb4108770",
   "metadata": {},
   "outputs": [],
   "source": [
    "kaessmann_tissues = set([x.split(\".\")[0] for x in dev_tissues])\n",
    "\n",
    "kaessmann_pred_df = (pd.read_csv(base_path_results + \"kaessmann_pred_manualmatched_enformer.tsv\", sep=\"\\t\")\n",
    "                  [[\"gene_id\",\"sample\",\"log_obs\",\"log_pred_ingenome\"]]\n",
    "                  .rename(columns={\"log_pred_ingenome\":\"log_pred_enformer\"})\n",
    "                 )\n",
    "\n",
    "kaessmann_pred_df_basenji2 = (pd.read_csv(base_path_results + \"kaessmann_pred_manualmatched_basenji2.tsv\", sep=\"\\t\")\n",
    "                          [[\"gene_id\",\"sample\",\"log_pred_basenji2\"]]\n",
    "                         )\n",
    "\n",
    "kaessmann_pred_df = (kaessmann_pred_df\n",
    "                  .merge(kaessmann_pred_df_basenji2, on=[\"gene_id\", \"sample\"])\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8dd81330-66b3-421e-b2c8-b5e2085c4315",
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_var_df = kaessmann_pred_df\n",
    "\n",
    "dev_var_df[\"tissue\"] = dev_var_df[\"sample\"].apply(lambda x: x.split(\".\")[0])\n",
    "dev_var_df[\"dev_stage\"] = dev_var_df[\"sample\"].apply(lambda x: \"_\".join(x.split(\"_\")[0].split(\".\")[1:]))\n",
    "dev_var_df[\"dev_stage_number\"] = dev_var_df[\"dev_stage\"].apply(lambda x: int(x.split(\"_\")[1]))\n",
    "dev_var_df[\"dev_stage\"] = dev_var_df[\"dev_stage\"].apply(lambda x: x.split(\"_\")[0])\n",
    "\n",
    "dev_var_df_aggreg = dev_var_df.groupby([\"gene_id\",\"tissue\",\"dev_stage\"]).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f41edd8-1531-4b13-bb32-baed0b8dac05",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "\n",
    "dev_var_df_indexed = dev_var_df_aggreg.set_index(\"gene_id\")\n",
    "\n",
    "for tissue in kaessmann_tissues:\n",
    "    tissue_subset_df = dev_var_df_indexed.query('tissue == @tissue')\n",
    "    for gene in set(tissue_subset_df.index[tissue_subset_df.index.isin(test_genes)]):\n",
    "        subset = tissue_subset_df.loc[gene]\n",
    "        # skip genes which are constant across dev-stages (or never expressed)\n",
    "        if not (subset[\"log_obs\"].std() > 0):\n",
    "            continue\n",
    "        for col in [x for x in subset.keys() if x.startswith(\"log_pred\")]:\n",
    "            row_dict = {\"gene_id\":gene}\n",
    "            row_dict[\"model\"] = col.split(\"_\")[-1]\n",
    "            row_dict[\"tissue\"] = tissue\n",
    "            tpl = (subset[\"log_obs\"],subset[col])\n",
    "            corr_test =  scipy.stats.pearsonr(*tpl)\n",
    "            r = corr_test[0]\n",
    "            row_dict[\"R\"] = r\n",
    "            row_dict[\"p\"] = corr_test[1]\n",
    "            row_dict[\"rho\"] = scipy.stats.spearmanr(*tpl)[0]\n",
    "            row_dict[\"R-squared\"]  = sklearn.metrics.r2_score(*tpl)\n",
    "            row_dict[\"pred_mean_log_expr_enformer\"] = subset[\"log_pred_enformer\"].mean()\n",
    "            row_dict[\"pred_std_log_expr_enformer\"] = subset[\"log_pred_enformer\"].std()\n",
    "            row_dict[\"pred_mean_log_expr_basenji2\"] = subset[\"log_pred_basenji2\"].mean()\n",
    "            row_dict[\"pred_std_log_expr_basenji2\"] = subset[\"log_pred_basenji2\"].std()\n",
    "            row_dict[\"mean_log_expr\"] = subset[\"log_obs\"].mean()\n",
    "            row_dict[\"std_log_expr\"] = subset[\"log_obs\"].std()\n",
    "            rows.append(row_dict)\n",
    "        \n",
    "between_dev_corrs = pd.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "98e1e491-750e-4fbd-918c-cc343a68e949",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.7047161887591151, 2.275362564153913e-211)\n",
      "(0.740391443068654, 2.613525085185607e-244)\n",
      "(-0.3777642809375574, 6.916265608340385e-49)\n",
      "(-0.2585818147997015, 6.725454454001534e-23)\n"
     ]
    }
   ],
   "source": [
    "gene_mean_df = (between_dev_corrs\n",
    "           .groupby('gene_id')\n",
    "           [[\"pred_mean_log_expr_enformer\",\"pred_std_log_expr_enformer\",\"pred_mean_log_expr_basenji2\",\"pred_std_log_expr_basenji2\",\"mean_log_expr\" ,\"std_log_expr\"]]\n",
    "           .mean()\n",
    "           .reset_index()\n",
    "          )\n",
    "# prediction of gene mean\n",
    "print(scipy.stats.pearsonr(gene_mean_df[\"mean_log_expr\"],gene_mean_df[\"pred_mean_log_expr_basenji2\"]))\n",
    "print(scipy.stats.pearsonr(gene_mean_df[\"mean_log_expr\"],gene_mean_df[\"pred_mean_log_expr_enformer\"]))\n",
    "# prediction of gene standard deviation\n",
    "print(scipy.stats.pearsonr(gene_mean_df[\"std_log_expr\"],gene_mean_df[\"pred_std_log_expr_basenji2\"]))\n",
    "print(scipy.stats.pearsonr(gene_mean_df[\"std_log_expr\"],gene_mean_df[\"pred_std_log_expr_enformer\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a42008fe-4b04-42d6-8f20-ee9a870090ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_dev_df = dev_var_df_aggreg.merge(gene_mean_df, on=\"gene_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a29eb8e6-0bf8-4f9e-804d-f5a3508cc521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_id</th>\n",
       "      <th>tissue</th>\n",
       "      <th>dev_stage</th>\n",
       "      <th>log_obs</th>\n",
       "      <th>log_pred_enformer</th>\n",
       "      <th>log_pred_basenji2</th>\n",
       "      <th>dev_stage_number</th>\n",
       "      <th>pred_mean_log_expr_enformer</th>\n",
       "      <th>pred_std_log_expr_enformer</th>\n",
       "      <th>pred_mean_log_expr_basenji2</th>\n",
       "      <th>pred_std_log_expr_basenji2</th>\n",
       "      <th>mean_log_expr</th>\n",
       "      <th>std_log_expr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000004776</td>\n",
       "      <td>Brain</td>\n",
       "      <td>10wpc</td>\n",
       "      <td>-0.176226</td>\n",
       "      <td>0.057227</td>\n",
       "      <td>-0.372524</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.335470</td>\n",
       "      <td>0.063720</td>\n",
       "      <td>-0.027715</td>\n",
       "      <td>0.028481</td>\n",
       "      <td>0.382943</td>\n",
       "      <td>0.315678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000004776</td>\n",
       "      <td>Brain</td>\n",
       "      <td>11wpc</td>\n",
       "      <td>-0.188755</td>\n",
       "      <td>0.057227</td>\n",
       "      <td>-0.372524</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.335470</td>\n",
       "      <td>0.063720</td>\n",
       "      <td>-0.027715</td>\n",
       "      <td>0.028481</td>\n",
       "      <td>0.382943</td>\n",
       "      <td>0.315678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000004776</td>\n",
       "      <td>Brain</td>\n",
       "      <td>12wpc</td>\n",
       "      <td>-0.460609</td>\n",
       "      <td>0.057227</td>\n",
       "      <td>-0.372524</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.335470</td>\n",
       "      <td>0.063720</td>\n",
       "      <td>-0.027715</td>\n",
       "      <td>0.028481</td>\n",
       "      <td>0.382943</td>\n",
       "      <td>0.315678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000004776</td>\n",
       "      <td>Brain</td>\n",
       "      <td>13wpc</td>\n",
       "      <td>-0.387005</td>\n",
       "      <td>0.057227</td>\n",
       "      <td>-0.372524</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.335470</td>\n",
       "      <td>0.063720</td>\n",
       "      <td>-0.027715</td>\n",
       "      <td>0.028481</td>\n",
       "      <td>0.382943</td>\n",
       "      <td>0.315678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000004776</td>\n",
       "      <td>Brain</td>\n",
       "      <td>16wpc</td>\n",
       "      <td>-0.411204</td>\n",
       "      <td>0.057227</td>\n",
       "      <td>-0.372524</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.335470</td>\n",
       "      <td>0.063720</td>\n",
       "      <td>-0.027715</td>\n",
       "      <td>0.028481</td>\n",
       "      <td>0.382943</td>\n",
       "      <td>0.315678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188265</th>\n",
       "      <td>ENSG00000273173</td>\n",
       "      <td>Testis</td>\n",
       "      <td>olderMidAge</td>\n",
       "      <td>-0.633944</td>\n",
       "      <td>0.622998</td>\n",
       "      <td>0.140282</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.808182</td>\n",
       "      <td>0.031524</td>\n",
       "      <td>0.253899</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>-0.525667</td>\n",
       "      <td>0.053510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188266</th>\n",
       "      <td>ENSG00000273173</td>\n",
       "      <td>Testis</td>\n",
       "      <td>toddler</td>\n",
       "      <td>-0.456625</td>\n",
       "      <td>0.622998</td>\n",
       "      <td>0.140282</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.808182</td>\n",
       "      <td>0.031524</td>\n",
       "      <td>0.253899</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>-0.525667</td>\n",
       "      <td>0.053510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188267</th>\n",
       "      <td>ENSG00000273173</td>\n",
       "      <td>Testis</td>\n",
       "      <td>youngAdult</td>\n",
       "      <td>-0.595383</td>\n",
       "      <td>0.622998</td>\n",
       "      <td>0.140282</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.808182</td>\n",
       "      <td>0.031524</td>\n",
       "      <td>0.253899</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>-0.525667</td>\n",
       "      <td>0.053510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188268</th>\n",
       "      <td>ENSG00000273173</td>\n",
       "      <td>Testis</td>\n",
       "      <td>youngMidAge</td>\n",
       "      <td>-0.598957</td>\n",
       "      <td>0.622998</td>\n",
       "      <td>0.140282</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.808182</td>\n",
       "      <td>0.031524</td>\n",
       "      <td>0.253899</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>-0.525667</td>\n",
       "      <td>0.053510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188269</th>\n",
       "      <td>ENSG00000273173</td>\n",
       "      <td>Testis</td>\n",
       "      <td>youngTeenager</td>\n",
       "      <td>-0.547965</td>\n",
       "      <td>0.622998</td>\n",
       "      <td>0.140282</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.808182</td>\n",
       "      <td>0.031524</td>\n",
       "      <td>0.253899</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>-0.525667</td>\n",
       "      <td>0.053510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188270 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                gene_id  tissue      dev_stage   log_obs  log_pred_enformer  \\\n",
       "0       ENSG00000004776   Brain          10wpc -0.176226           0.057227   \n",
       "1       ENSG00000004776   Brain          11wpc -0.188755           0.057227   \n",
       "2       ENSG00000004776   Brain          12wpc -0.460609           0.057227   \n",
       "3       ENSG00000004776   Brain          13wpc -0.387005           0.057227   \n",
       "4       ENSG00000004776   Brain          16wpc -0.411204           0.057227   \n",
       "...                 ...     ...            ...       ...                ...   \n",
       "188265  ENSG00000273173  Testis    olderMidAge -0.633944           0.622998   \n",
       "188266  ENSG00000273173  Testis        toddler -0.456625           0.622998   \n",
       "188267  ENSG00000273173  Testis     youngAdult -0.595383           0.622998   \n",
       "188268  ENSG00000273173  Testis    youngMidAge -0.598957           0.622998   \n",
       "188269  ENSG00000273173  Testis  youngTeenager -0.547965           0.622998   \n",
       "\n",
       "        log_pred_basenji2  dev_stage_number  pred_mean_log_expr_enformer  \\\n",
       "0               -0.372524              16.0                     0.335470   \n",
       "1               -0.372524              18.5                     0.335470   \n",
       "2               -0.372524              21.0                     0.335470   \n",
       "3               -0.372524              23.0                     0.335470   \n",
       "4               -0.372524              25.5                     0.335470   \n",
       "...                   ...               ...                          ...   \n",
       "188265           0.140282              40.0                     0.808182   \n",
       "188266           0.140282              31.0                     0.808182   \n",
       "188267           0.140282              37.0                     0.808182   \n",
       "188268           0.140282              39.0                     0.808182   \n",
       "188269           0.140282              32.5                     0.808182   \n",
       "\n",
       "        pred_std_log_expr_enformer  pred_mean_log_expr_basenji2  \\\n",
       "0                         0.063720                    -0.027715   \n",
       "1                         0.063720                    -0.027715   \n",
       "2                         0.063720                    -0.027715   \n",
       "3                         0.063720                    -0.027715   \n",
       "4                         0.063720                    -0.027715   \n",
       "...                            ...                          ...   \n",
       "188265                    0.031524                     0.253899   \n",
       "188266                    0.031524                     0.253899   \n",
       "188267                    0.031524                     0.253899   \n",
       "188268                    0.031524                     0.253899   \n",
       "188269                    0.031524                     0.253899   \n",
       "\n",
       "        pred_std_log_expr_basenji2  mean_log_expr  std_log_expr  \n",
       "0                         0.028481       0.382943      0.315678  \n",
       "1                         0.028481       0.382943      0.315678  \n",
       "2                         0.028481       0.382943      0.315678  \n",
       "3                         0.028481       0.382943      0.315678  \n",
       "4                         0.028481       0.382943      0.315678  \n",
       "...                            ...            ...           ...  \n",
       "188265                    0.034800      -0.525667      0.053510  \n",
       "188266                    0.034800      -0.525667      0.053510  \n",
       "188267                    0.034800      -0.525667      0.053510  \n",
       "188268                    0.034800      -0.525667      0.053510  \n",
       "188269                    0.034800      -0.525667      0.053510  \n",
       "\n",
       "[188270 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_dev_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "31ea3a49-5d40-4960-9a7e-33f6397ad52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_dev_df[\"condition\"] = gene_dev_df[\"log_obs\"] - gene_dev_df['mean_log_expr']\n",
    "\n",
    "gene_dev_df['log2_fc'] = gene_dev_df['condition']/np.log10(2)\n",
    "gene_dev_df['pred_log2_fc'] = (gene_dev_df['log_pred_enformer'] - gene_dev_df['pred_mean_log_expr_enformer'])/np.log10(2)\n",
    "gene_dev_df['pred_log2_fc_basenji2'] = (gene_dev_df['log_pred_basenji2'] - gene_dev_df['pred_mean_log_expr_basenji2'])/np.log10(2)\n",
    "gene_dev_df[\"abs_log2_fc\"] = np.abs(gene_dev_df[\"log2_fc\"])\n",
    "gene_dev_df[\"abs_pred_log2_fc\"] = np.abs(gene_dev_df[\"pred_log2_fc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d5d9a4b5-9dfb-49c7-b9f8-52a3e06688f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.3007632572581953, 0.0)\n",
      "(0.3848346691798924, 0.0)\n"
     ]
    }
   ],
   "source": [
    "print(scipy.stats.pearsonr(gene_dev_df['log2_fc'], gene_dev_df['pred_log2_fc_basenji2']))\n",
    "print(scipy.stats.pearsonr(gene_dev_df['log2_fc'], gene_dev_df['pred_log2_fc']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e08e1b5f-0887-49df-a336-adc6faf38248",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 428,
       "width": 632
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (1431200202132)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = 1\n",
    "p9.options.figure_size = (6.4*scale, 4.8*scale)\n",
    "\n",
    "p = (p9.ggplot(data=gene_dev_df,mapping=p9.aes(x=\"pred_log2_fc\",y=\"log2_fc\",))\n",
    " #+ p9.geom_col(position=\"dodge\")\n",
    " + p9.geom_bin2d(bins=100, raster=True)\n",
    " + p9.scale_fill_continuous(ListedColormap(cm.get_cmap('viridis', 512)(np.linspace(0.25, 1, 512))), trans=\"log10\")\n",
    " + p9.geom_smooth(method=\"lm\",color=\"blue\")\n",
    " + p9.geom_vline(xintercept = 1)\n",
    " + p9.geom_vline(xintercept = -1)\n",
    " + p9.geom_hline(yintercept = 1)\n",
    " + p9.geom_hline(yintercept = -1)\n",
    " + p9.theme(legend_text=p9.element_text(size=8),legend_title=p9.element_text(size=8),\n",
    "            legend_box_margin=0,legend_key_width=5,axis_title=p9.element_text(size=10))#\n",
    " + p9.labs(x=\"Predicted log2 deviation from mean expression\",y=\"Measured log2 deviation\\nfrom mean expression\")\n",
    " #+ p9.scale_y_log10()\n",
    " #+ p9.labs(x=\"\",y=\"Density\")\n",
    ")\n",
    "p"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:anaconda-karollus-tfhub]",
   "language": "python",
   "name": "conda-env-anaconda-karollus-tfhub-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}