2022 - Signal profiles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": "# Signal profiles\n",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'\n",
    "\n",
    "from math import fabs\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "# sns.set_style(\"white\")\n",
    "from tqdm.auto import tqdm\n",
    "from itertools import product\n",
    "import os\n",
    "import pyBigWig\n",
    "from math import ceil\n",
    "from scipy.stats import pearsonr\n",
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# GSE26320 reprocessed"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "GSE26320_PATH = os.path.expanduser('~/data/2023_GSE26320')\n",
    "GSE26320_MODIFICATIONS = ['CTCF',  'H3K4me3', 'H3K4me1', 'H3K27ac', 'H3K36me3', 'H3K27me3']\n",
    "# GSE26320_CELLS = ['GM12878', 'HMEC', 'HSMM', 'K562', 'NHEK', 'NHLF', 'H1', 'Huvec', 'HepG2']\n",
    "GSE26320_CELLS = ['GM12878', 'K562', 'NHEK']\n",
    "# GSE26320_REPS = ['rep1', 'rep2']\n",
    "GSE26320_REPS = ['rep1']"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Peaks length for ENCODE dataset"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Autocorrection across all coverage - with control correction\n",
    "Paper: Retrieving Chromatin Patterns from Deep Sequencing Data Using Correlation Functions\n",
    "https://www.cell.com/biophysj/fulltext/S0006-3495(17)30032-2#sec2\n",
    "\n",
    "`bwext` folder is used to create bigwig visualization by enlarging coverage to fragment size.\n",
    "```\n",
    "# Visualization\n",
    "for F in *.bam; do echo $F; samtools index $F; bamCoverage --ignoreDuplicates --extendReads 200 -b $F -p 6 -o ${F/.bam/_ext.bw}; done\n",
    "```"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "CHROM_SIZES = {\n",
    "    c: s for _, (c, s) in pd.read_csv(os.path.join(GSE26320_PATH, 'hg38.chrom.sizes'),\n",
    "                                      sep='\\t', names=['chr', 'size']).iterrows()\n",
    "}"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "GSE26320_BW_PATH = os.path.join(GSE26320_PATH, 'bwext')\n",
    "\n",
    "def r0(o, c):\n",
    "    \"\"\" Correlation to estimate b \"\"\"\n",
    "    assert o.size == c.size\n",
    "    # o_mean = o.sum() / o.size\n",
    "    # c_mean = c.sum() / c.size\n",
    "    # on = o - o_mean\n",
    "    # cn = c - c_mean\n",
    "    # return np.dot(on, cn) / sqrt(np.dot(on, on) * np.dot(cn, cn))\n",
    "    return pearsonr(o, c)[0]\n",
    "\n",
    "def rDx(dx, o1, o2):\n",
    "    \"\"\" Slow shifted correlation function by formula from paper \"\"\"\n",
    "    assert o1.size == o2.size\n",
    "    # n = o1.size\n",
    "    # o1_mean = o1.sum() / o1.size\n",
    "    # o2_mean = o2.sum() / o2.size\n",
    "    # o1n = o1 - o1_mean\n",
    "    # o2n = o2 - o2_mean\n",
    "    # nominator = (1 / (2 * (n - dx))) * sum(\n",
    "    #     (o1[i] - o1_mean) * (o2[i + dx] - o2_mean) + (o1[i + dx] - o1_mean) * (o2[i] - o2_mean)\n",
    "    #     for i in range(n - dx)\n",
    "    # )\n",
    "    # denominator = (1 / n) * sqrt(np.dot(o1n, o1n) * np.dot(o2n, o2n))\n",
    "    # return nominator / denominator\n",
    "    return pearsonr(o1, np.roll(o2, dx))[0]\n",
    "\n",
    "# TODO: support blacklisted regions to avoid long-distance zero coverage\n",
    "\n",
    "def compute_autocorrelations_paper(modifications, cells, replicates, chrom_sizes, bin, ds, control='Input', bwpath=GSE26320_BW_PATH):\n",
    "    correlations = []\n",
    "    for mod, cell, rep in tqdm(list(product(modifications, cells, replicates))):\n",
    "        bwfile = next((f for f in os.listdir(bwpath) if '.bw' in f and mod in f and cell in f and rep in f), None)\n",
    "        bwcfile = next((f for f in os.listdir(bwpath) if '.bw' in f and control in f and cell in f and rep in f), None)\n",
    "        if bwfile is None or bwcfile is None:\n",
    "            continue\n",
    "        print(mod, cell, rep, bwfile, bwcfile)\n",
    "        for chr, chr_size in tqdm(chrom_sizes.items()):\n",
    "            chr_size = min(chr_size, 100_000_000)\n",
    "            print(f'Computing {mod} {cell} {rep} binned {bin}bp coverage correlations on chr {chr} {chr_size}')\n",
    "            with pyBigWig.open(os.path.join(bwpath, bwfile)) as bw:\n",
    "                coverage = np.asarray(bw.stats(chr, nBins=(int(ceil(chr_size / bin))), exact=True, type='sum'))\n",
    "            with pyBigWig.open(os.path.join(bwpath, bwcfile)) as bwc:\n",
    "                coveragec = np.asarray(bwc.stats(chr, nBins=(int(ceil(chr_size / bin))), exact=True, type='sum'))\n",
    "            # The coverage was initially calculated for each chromosome by extending the reads to\n",
    "            # fragment length, yielding a histogram with the genomic coordinate on the x axis\n",
    "            # and the number of reads per basepair on the y axis.\n",
    "\n",
    "            print(f'{chr} Coverage mln signal: {coverage.sum()} control: {coveragec.sum()}')\n",
    "            # First, the normalized coverage of the control Cnorm and of the specific IP Anorm\n",
    "            # were obtained by dividing by input signal I. Positions with zero input coverage were neglected.\n",
    "            # Subsequently, the coverage at these positions was set to the respective average value\n",
    "            # that was calculated for the remaining positions, thus eliminating fluctuations and\n",
    "            # corresponding contributions to the correlation coefficient from these positions.\n",
    "            coverage_mean = coverage.sum() / sum(coverage > 0)\n",
    "            coveragec_mean = coveragec.sum() / sum(coveragec > 0)\n",
    "            print(f'Mean signal {coverage_mean} control {coveragec_mean}')\n",
    "            coverage[coverage == 0] = coverage_mean\n",
    "            coveragec[coveragec == 0] = coveragec_mean\n",
    "            coverage = coverage / coverage_mean\n",
    "            coveragec = coveragec / coveragec_mean\n",
    "            # In the next step, nonspecific background signal was removed to obtain the\n",
    "            # normalized read occupancy O = Anorm − b × Cnorm\n",
    "            # The parameter b quantifies the contribution of the control signal present as\n",
    "            # background in the sample (IP).\n",
    "            # To estimate b, we minimized the absolute value of the Pearson correlation coefficient r0 at\n",
    "            # zero shift distance between the normalized occupancy O and the control coverage Cnorm.\n",
    "            # For the minimization procedure, b was changed between 0 and 1.\n",
    "            print('Estimating b between 0 and 1')\n",
    "            stepB = 0.01\n",
    "            bs = np.linspace(0, 1, num = int(1 / stepB) + 1)\n",
    "            bCs = [fabs(r0(coverage - b * coveragec, coveragec)) for b in bs]\n",
    "            bI = np.argmin(bCs)\n",
    "            b = bs[bI]\n",
    "            print(f'I={bI}, b={b}, correlation={bCs[bI]}')\n",
    "            coverageo = coverage - b * coveragec\n",
    "            print('Computing autocorrelations')\n",
    "            for d in ds:\n",
    "                corr, pval = rDx(d, coverageo, coverageo), None\n",
    "                correlations.append((mod, cell, rep, bwfile, chr, coverage_mean, coveragec_mean, b,\n",
    "                                     d * bin, corr, pval))\n",
    "    return pd.DataFrame(\n",
    "        columns=['modification', 'cell', 'replicate', 'file', 'chr', 'coverage mean', 'control mean', 'b',\n",
    "                 'd', 'correlation', 'pvalue'],\n",
    "        data=correlations\n",
    "    )"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# To sample the correlation function in a quasi-logarithmic manner (33),\n",
    "# profiles were binned by a factor of two after 25 shift operations to double the step size.\n",
    "# To preserve high resolution for small shift distances, the first binning operation was carried\n",
    "# out at a shift of Δx = 50 bp.\n",
    "BIN = 50\n",
    "DS = list(range(0, int(5000 / BIN))) + \\\n",
    "     list(range(int(5000 / BIN), int(100_000 / BIN), int(1000 / BIN))) + \\\n",
    "     list(range(int(100_000 / BIN), int(1_000_100 / BIN), int(100_000 / BIN)))\n",
    "print(len(DS))"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "df_correlations_paper = compute_autocorrelations_paper(GSE26320_MODIFICATIONS, GSE26320_CELLS, GSE26320_REPS,\n",
    "                                                       {'chr22': CHROM_SIZES['chr22']}, BIN, DS)\n",
    "df_correlations_paper.sample(3)"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(5, 3))\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t = df_correlations_paper.copy()\n",
    "t.loc[t['d'] == 0, 'd'] = BIN / 2\n",
    "t = t[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3'])\n",
    "ticks = [1, 20, 50, 100, 200, 500, 1000, 2000, 5000,\n",
    "         10_000, 20_000, 50_000, 100_000, 200_000, 500_000, 1_000_000]\n",
    "# ticks = [100, 1000, 10_000, 100_000, 1_000_000]\n",
    "\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'ENCODE d auto correlation bin={BIN}')\n",
    "ax.set_xlim(BIN / 2, 1_000_000)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{GSE26320_PATH}/pics/paper_signal_correlation.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(5, 3))\n",
    "# plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "             hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3'])\n",
    "ticks = [100, 1000, 10_000]\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.set_xlim(50, 20_000)\n",
    "ax.title.set_text(f'ENCODE d auto correlation bin={BIN}')\n",
    "ax.legend(loc='upper right')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{GSE26320_PATH}/pics/paper_signal_correlation_zoom.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Signal profile along peaks"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "RANGE = 50_000\n",
    "BIN = 50\n",
    "BINS = int(RANGE / BIN)\n",
    "PEAKS = 1000"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "GSE26320_SPAN_PATH = os.path.join(GSE26320_PATH, 'span')\n",
    "GSE26320_BW_PATH = os.path.join(GSE26320_PATH, 'bwext')\n",
    "\n",
    "RANGE = 10000\n",
    "BIN = 50\n",
    "BINS = int(RANGE / BIN)\n",
    "PEAKS = 100\n",
    "\n",
    "def intersect(c1, s1, e1, c2, s2, e2):\n",
    "    return c1 == c2 and (s1 <= s2 < e1 or s1 <= e2 - 1 < e2)\n",
    "\n",
    "def signal_peaks_profile(modifications, cells, replicates, peakspath=GSE26320_SPAN_PATH, bwpath=GSE26320_BW_PATH,\n",
    "                         control='Input'):\n",
    "    profiles = []\n",
    "    for mod, cell, rep in tqdm(list(product(modifications, cells, replicates))):\n",
    "        peaksfile = next((f for f in os.listdir(peakspath) if mod in f and cell in f and rep in f), None)\n",
    "        bwfile = next((f for f in os.listdir(bwpath) if '.bw' in f and mod in f and cell in f and rep in f), None)\n",
    "        bwcfile = next((f for f in os.listdir(bwpath) if '.bw' in f and control in f and cell in f and rep in f), None)\n",
    "        if peaksfile is None or bwfile is None:\n",
    "            continue\n",
    "        print(mod, cell, rep, peaksfile, bwfile)\n",
    "        # Load peaks file and sort by score\n",
    "        peaksfile_df = pd.read_csv(os.path.join(peakspath, peaksfile), sep='\\t',\n",
    "                                   names=['chr', 'start', 'end', '4', '5', '6', '7', '8', 'score'])\n",
    "        peaksfile_df = peaksfile_df[['chr', 'start', 'end', 'score']].copy()\n",
    "        peaksfile_df.sort_values(by=['score'], ascending=False, inplace=True)\n",
    "        # Ensure that peaks do not intersect and doesn't contain any significant peaks\n",
    "        intersects = [t for _, t in peaksfile_df.head(PEAKS)[['chr', 'start', 'end']].iterrows()]\n",
    "        added = 0\n",
    "        with pyBigWig.open(os.path.join(bwpath, bwfile)) as bw:\n",
    "            with pyBigWig.open(os.path.join(bwpath, bwcfile)) as bwc:\n",
    "                total_coverage = sum(bw.stats(f'chr{i}', exact=True)[0] for i in range(1, 22))\n",
    "                total_coveragec = sum(bwc.stats(f'chr{i}', exact=True)[0] for i in range(1, 22))\n",
    "                # print(total_coverage)\n",
    "                for _, (chr, start, end, score) in peaksfile_df.iterrows():\n",
    "                    # No more than single intersection\n",
    "                    if sum(intersect(chr, start, end, c, s, e) for c, s, e in intersects) > 1:\n",
    "                        continue\n",
    "                    # Find summit for narrow peak, center of peak for broad\n",
    "                    if mod in ['CTCF', 'H3K27ac', 'H3K4me3']:\n",
    "                        nBins = min(BINS, int((end - start) / 20))\n",
    "                        stats = bw.stats(chr, start, end, nBins=nBins, exact=True)\n",
    "                        max_offset = start + (np.argmax(stats) + 0.5) * (end - start) / nBins\n",
    "                        r_start = int(max_offset  - RANGE / 2)\n",
    "                    else:\n",
    "                        r_start = int((start + end) / 2 - RANGE / 2)\n",
    "                    if r_start < 0:\n",
    "                        continue\n",
    "                    r_end = r_start + RANGE\n",
    "                    # Check that no peak intersect others\n",
    "                    if sum(intersect(chr, r_start, r_end, c, s, e) for c, s, e in intersects) > 1:\n",
    "                        continue\n",
    "                    intersects.append((chr, r_start, r_end))\n",
    "                    # Analyse signal and control coverage\n",
    "                    try:\n",
    "                        for i, cov in enumerate(bw.stats(chr, r_start, r_end, nBins=BINS, exact=True)):\n",
    "                            profiles.append((mod, cell, rep, added, (-BINS / 2 + i) * BIN, cov / total_coverage))\n",
    "                        for i, cov in enumerate(bwc.stats(chr, r_start, r_end, nBins=BINS, exact=True)):\n",
    "                            profiles.append((control, cell, rep, added, (-BINS / 2 + i) * BIN, cov / total_coveragec))\n",
    "                    except:\n",
    "                        pass\n",
    "                    added += 1\n",
    "                    if added == PEAKS:\n",
    "                        break\n",
    "\n",
    "    return pd.DataFrame(columns=['modification', 'cell', 'replicate', 'peak', 'x', 'coverage'], data=profiles)"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "df_gse26320_cov = signal_peaks_profile(GSE26320_MODIFICATIONS, GSE26320_CELLS, GSE26320_REPS)\n",
    "df_gse26320_cov"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(5, 3))\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t = df_gse26320_cov[['modification', 'x', 'coverage']].groupby(['modification', 'x']).mean().reset_index()\n",
    "sns.lineplot(data=t, x=\"x\", y=\"coverage\", hue=\"modification\", err_style=None,\n",
    "             hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3'])\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{GSE26320_PATH}/pics/peaks_profile.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Peaks densities"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "GSE26320_SPAN_PATH = os.path.join(GSE26320_PATH, 'span')\n",
    "GSE26320_BW_PATH = os.path.join(GSE26320_PATH, 'bwext')\n",
    "\n",
    "data = []\n",
    "for mod, cell, rep in tqdm(list(product(GSE26320_MODIFICATIONS, GSE26320_CELLS, GSE26320_REPS))):\n",
    "    peaksfile = next((f for f in os.listdir(GSE26320_SPAN_PATH) if mod in f and cell in f and rep in f), None)\n",
    "    bwfile = next((f for f in os.listdir(GSE26320_BW_PATH) if '.bw' in f and mod in f and cell in f and rep in f), None)\n",
    "    bwcfile = next((f for f in os.listdir(GSE26320_BW_PATH) if '.bw' in f and 'Input' in f and cell in f and rep in f),\n",
    "                   None)\n",
    "    if peaksfile is None or bwfile is None or bwcfile is None:\n",
    "        continue\n",
    "    print(mod, cell, rep, peaksfile, bwfile, bwcfile)\n",
    "\n",
    "    # Load peaks file and sort by score\n",
    "    peaksfile_df = pd.read_csv(os.path.join(GSE26320_SPAN_PATH, peaksfile), sep='\\t',\n",
    "                               names=['chr', 'start', 'end', '4', '5', '6', '7', '8', 'score'])\n",
    "    with pyBigWig.open(os.path.join(GSE26320_BW_PATH, bwfile)) as bw:\n",
    "        with pyBigWig.open(os.path.join(GSE26320_BW_PATH, bwcfile)) as bwc:\n",
    "            # Resort top scored peaks by density and start with the most dense ones\n",
    "            total_coverage = sum(bw.stats(chr, exact=True, type='sum')[0] for chr in CHROM_SIZES.keys()) / 1e6\n",
    "            total_coveragec = sum(bwc.stats(chr, exact=True, type='sum')[0] for chr in CHROM_SIZES.keys()) / 1e6\n",
    "            for _, (chr, start, end) in peaksfile_df[['chr', 'start', 'end']].iterrows():\n",
    "                peak_coverage = bw.stats(chr, start, end, exact=True, type='sum')[0]\n",
    "                peak_coveragec = bwc.stats(chr, start, end, exact=True, type='sum')[0]\n",
    "                data.append((mod, cell, rep, total_coverage, total_coveragec,\n",
    "                             f'{chr}:{start}-{end}', end - start, peak_coverage, peak_coveragec))\n",
    "\n",
    "gse26320_peak_coverages_df = pd.DataFrame(\n",
    "    columns=['modification', 'cell', 'replicate', 'total_signal_coverage', 'total_control_coverage',\n",
    "             'peak', 'length', 'signal_coverage', 'control_coverage'],\n",
    "    data=data\n",
    ")\n",
    "del data\n",
    "gse26320_peak_coverages_df"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "t = pd.concat([gse26320_peak_coverages_df[gse26320_peak_coverages_df['modification'] == m].sample(2_000)\n",
    "               for m in gse26320_peak_coverages_df['modification'].unique()]).reset_index(drop=True).sample(frac=1).reset_index(drop=True)\n",
    "t['RPKM'] = t['signal_coverage'] / t['length'] * 1e3\n",
    "t['RPKM'] /= t['total_signal_coverage']\n",
    "t['RPKM'].clip(lower=0.2, inplace=True)\n",
    "t['length'].clip(upper=3e5, inplace=True)\n",
    "ticks = [100, 1000, 10_000, 100_000]\n",
    "g_results = sns.scatterplot(data=t, x='length', y='RPKM', hue='modification', alpha=0.3,\n",
    "                            hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3'])\n",
    "# g_results = sns.kdeplot(data=t, x='length', y='RPKM', hue='modification', alpha=0.3,\n",
    "#                         hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3'], fill='true')\n",
    "\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "g_results.set(yscale='log')\n",
    "# Put a legend to the right of the current axis\n",
    "g_results.axes.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{GSE26320_PATH}/pics/peaks_length_vs_rpkm.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Autocorrelations - without control correction\n",
    "Simplified autocorrelation computation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def compute_autocorrelations(modifications, cells, replicates, chrom_sizes, bin, ds, bwpath=GSE26320_BW_PATH):\n",
    "    correlations = []\n",
    "    for mod, cell, rep in tqdm(list(product(modifications, cells, replicates))):\n",
    "        bwfile = next((f for f in os.listdir(bwpath) if '.bw' in f and mod in f and cell in f and rep in f), None)\n",
    "        if bwfile is None:\n",
    "            continue\n",
    "        print(mod, cell, rep, bwfile)\n",
    "        print(f'Computing binned coverage correlations')\n",
    "        with pyBigWig.open(os.path.join(bwpath, bwfile)) as bw:\n",
    "            # Some chromosomes may be missing\n",
    "            total_coverage = 0\n",
    "            for chr in CHROM_SIZES.keys():\n",
    "                try:\n",
    "                    total_coverage += bw.stats(chr, exact=True, type='sum')[0]\n",
    "                except:\n",
    "                    pass\n",
    "            total_coverage = total_coverage / 1e6\n",
    "            print(f'Signal coverage mln {total_coverage}')\n",
    "            for chr, chr_size in tqdm(chrom_sizes.items()):\n",
    "                chr_size = min(chr_size, 100_000_000)\n",
    "                print(f'Computing {mod} {cell} binned {bin}bp coverage correlations on chr {chr} {chr_size}')\n",
    "                # TODO: support blacklisted regions to avoid long-distance zero coverage\n",
    "                coverage = np.asarray(bw.stats(chr, 0, chr_size, nBins=(int(ceil((chr_size) / bin))),\n",
    "                                               exact=True, type='sum'))\n",
    "                # Ignore non-covered regions\n",
    "                coverage = coverage[coverage > 0]\n",
    "                for d in ds:\n",
    "                    corr, pval = pearsonr(coverage, np.roll(coverage, d))\n",
    "                    correlations.append((mod, cell, rep, bwfile, chr, d * bin, corr, pval))\n",
    "    return pd.DataFrame(\n",
    "        columns=['modification', 'cell', 'replicate', 'file', 'chr', 'd', 'correlation', 'pvalue'],\n",
    "        data=correlations\n",
    "    )"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# To sample the correlation function in a quasi-logarithmic manner (33),\n",
    "# profiles were binned by a factor of two after 25 shift operations to double the step size.\n",
    "# To preserve high resolution for small shift distances, the first binning operation was carried\n",
    "# out at a shift of Δx = 50 bp.\n",
    "BIN = 50\n",
    "DS = list(range(0, int(5000 / BIN))) + \\\n",
    "     list(range(int(5000 / BIN), int(100_000 / BIN), int(1000 / BIN))) + \\\n",
    "     list(range(int(100_000 / BIN), int(1_000_100 / BIN), int(100_000 / BIN)))\n",
    "print(len(DS))"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "df_correlations = compute_autocorrelations(['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3', 'Input'],\n",
    "                                           GSE26320_CELLS, GSE26320_REPS, {'chr22': CHROM_SIZES['chr22']}, BIN, DS)\n",
    "df_correlations"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(5, 3))\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t = df_correlations.copy()\n",
    "t.loc[t['d'] == 0, 'd'] = BIN / 2  \n",
    "t = t[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3', 'Input'])\n",
    "# ticks = [10, 100, 1000, 10_000, 100_000, 1_000_000]\n",
    "ticks = [1, 20, 50, 100, 200, 500, 1000, 2000, 5000,\n",
    "         10_000, 20_000, 50_000, 100_000, 200_000, 500_000, 1_000_000]\n",
    "\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'ENCODE auto correlation bin={BIN}')\n",
    "ax.set_xlim(BIN / 2, 1_000_000)\n",
    "ax.legend(loc='upper right')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{GSE26320_PATH}/pics/signal_correlation.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(10, 6))\n",
    "plt.figure(figsize=(5, 3))\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "             hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3', 'Input'])\n",
    "ticks = [100, 1000, 10_000]\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax = g_results.axes\n",
    "ax.title.set_text(f'ENCODE auto correlation bin={BIN}')\n",
    "ax.set_xlim(50, 20_000)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{GSE26320_PATH}/pics/signal_correlation_zoom.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# RoadmapEpigenomics autocorrelation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "PATH = os.path.expanduser('~/data/2023_Immune')\n",
    "\n",
    "IMMUNE_CELLS = ['BCell', 'TCell', 'Monocyte']\n",
    "MODIFICATIONS = ['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3', 'Control']"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "df_correlations_roadmap_paper = compute_autocorrelations_paper(\n",
    "    GSE26320_MODIFICATIONS, IMMUNE_CELLS, [''],\n",
    "    {'chr22': CHROM_SIZES['chr22']}, BIN, DS, control='Control', bwpath=PATH + '/bwext'\n",
    ")\n",
    "df_correlations_roadmap_paper.sample(3)"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(5, 3))\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t = df_correlations_roadmap_paper.copy()\n",
    "t.loc[t['d'] == 0, 'd'] = BIN / 2\n",
    "t = t[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=MODIFICATIONS)\n",
    "# ticks = [10, 100, 1000, 10_000, 100_000, 1_000_000]\n",
    "ticks = [1, 20, 50, 100, 200, 500, 1000, 2000, 5000,\n",
    "         10_000, 20_000, 50_000, 100_000, 200_000, 500_000, 1_000_000]\n",
    "\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'Roadmap d auto correlation bin={BIN}')\n",
    "ax.set_xlim(BIN / 2, 1_000_000)\n",
    "ax.legend(loc='upper right')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{PATH}/pics/signal_correlation_paper.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(10, 6))\n",
    "plt.figure(figsize=(5, 3))\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3', 'Input'])\n",
    "ticks = [100,  1000, 10_000]\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax = g_results.axes\n",
    "ax.title.set_text(f'Roadmap d auto correlation bin={BIN}')\n",
    "ax.set_xlim(50, 20_000)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{PATH}/pics/signal_correlation_paper_zoom.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "t1 = df_correlations_paper.copy()\n",
    "t1 = t1[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "t1['dataset'] = 'ENCODE'\n",
    "\n",
    "t2 = df_correlations_roadmap_paper.copy()\n",
    "t2 = t2[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "t2['dataset'] = 'Roadmap'\n",
    "\n",
    "t = pd.concat([t1, t2]).reset_index()\n",
    "\n",
    "plt.figure(figsize=(16, 4))\n",
    "axs = [plt.subplot(1, 5, i + 1) for i in range(5)]\n",
    "for i, m in enumerate(['H3K4me3', 'H3K27ac', 'H3K4me1', 'H3K27me3', 'H3K36me3']):\n",
    "    ax = axs[i]\n",
    "    g_results = sns.lineplot(data=t[t['modification'] == m], x=\"d\", y=\"correlation\", hue='dataset', ax=ax)\n",
    "    ticks = [100, 1000, 10_000]\n",
    "    g_results.set(xscale='log')\n",
    "    g_results.set(xticks=ticks)\n",
    "    g_results.set(xticklabels=ticks)\n",
    "    ax.title.set_text(f'{m}')\n",
    "    ax.set_xlim(50, 20_000)\n",
    "    # Put a legend to the right of the current axis\n",
    "    if i == 4:\n",
    "        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    else:\n",
    "        ax.legend().set_visible(False)    \n",
    "    if i > 0:\n",
    "        ax.set_ylabel(None)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "chr_sizes = {'chr22': CHROM_SIZES['chr22']}\n",
    "df_correlations_roadmap = compute_autocorrelations(MODIFICATIONS, IMMUNE_CELLS, [''],\n",
    "                                                   {'chr22': CHROM_SIZES['chr22']},\n",
    "                                                   BIN, DS, bwpath=PATH + '/bwext')\n",
    "df_correlations_roadmap"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(5, 3))\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t = df_correlations_roadmap.copy()\n",
    "t.loc[t['d'] == 0, 'd'] = BIN / 2\n",
    "t = t[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3', 'Control'])\n",
    "# ticks = [10, 100, 1000, 10_000, 100_000, 1_000_000]\n",
    "ticks = [1, 20, 50, 100, 200, 500, 1000, 2000, 5000,\n",
    "         10_000, 20_000, 50_000, 100_000, 200_000, 500_000, 1_000_000]\n",
    "\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'Roadmap auto correlation bin={BIN}')\n",
    "ax.set_xlim(BIN / 2, 1_000_000)\n",
    "ax.legend(loc='upper right')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{PATH}/pics/signal_correlation.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(10, 6))\n",
    "plt.figure(figsize=(5, 3))\n",
    "ax = plt.axes()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3', 'Control'])\n",
    "ticks = [100, 1000, 10_000]\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'Roadmap auto correlation bin={BIN}')\n",
    "ax.set_xlim(50, 20_000)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{PATH}/pics/signal_correlation_zoom.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "t1 = df_correlations.copy()\n",
    "t1 = t1[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "t1['dataset'] = 'ENCODE'\n",
    "\n",
    "t2 = df_correlations_roadmap.copy()\n",
    "t2 = t2[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "t2['dataset'] = 'Roadmap'\n",
    "\n",
    "t = pd.concat([t1, t2]).reset_index()\n",
    "\n",
    "plt.figure(figsize=(16, 4))\n",
    "axs = [plt.subplot(1, 5, i + 1) for i in range(5)]\n",
    "for i, m in enumerate(['H3K4me3', 'H3K27ac', 'H3K4me1', 'H3K27me3', 'H3K36me3']):\n",
    "    ax = axs[i]\n",
    "    g_results = sns.lineplot(data=t[t['modification'] == m], x=\"d\", y=\"correlation\", hue='dataset', ax=ax)\n",
    "    ticks = [100, 1000, 10_000, 100_000]\n",
    "    g_results.set(xscale='log')\n",
    "    g_results.set(xticks=ticks)\n",
    "    g_results.set(xticklabels=ticks)\n",
    "    ax.title.set_text(f'{m}')\n",
    "    ax.set_xlim(100, 100_000)\n",
    "    # Put a legend to the right of the current axis\n",
    "    if i == 4:\n",
    "        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    else:\n",
    "        ax.legend().set_visible(False)    \n",
    "    if i > 0:\n",
    "        ax.set_ylabel(None)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Peaks profile"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "IMMUNE_SPAN_PATH = os.path.join(PATH, 'span')\n",
    "IMMUNE_BW_PATH = os.path.join(PATH, 'bwext')\n",
    "\n",
    "df_immune_cov = signal_peaks_profile(MODIFICATIONS, IMMUNE_CELLS, [''], IMMUNE_SPAN_PATH, IMMUNE_BW_PATH, 'Control')\n",
    "df_immune_cov"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(5, 3))\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t = df_gse26320_cov[['modification', 'x', 'coverage']].groupby(['modification', 'x']).mean().reset_index()\n",
    "sns.lineplot(data=t, x=\"x\", y=\"coverage\", hue=\"modification\", err_style=None,\n",
    "             hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3'])\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{PATH}/pics/peaks_profile.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Peaks intensities"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data = []\n",
    "for mod, cell, rep in tqdm(list(product(MODIFICATIONS, IMMUNE_CELLS, ['']))):\n",
    "    peaksfile = next((f for f in os.listdir(IMMUNE_SPAN_PATH) if mod in f and cell in f and rep in f), None)\n",
    "    bwfile = next((f for f in os.listdir(IMMUNE_BW_PATH) if '.bw' in f and mod in f and cell in f and rep in f), None)\n",
    "    bwcfile = next((f for f in os.listdir(IMMUNE_BW_PATH) if '.bw' in f and 'Control' in f and cell in f and rep in f), None)\n",
    "    if peaksfile is None or bwfile is None or bwcfile is None:\n",
    "        continue\n",
    "    print(mod, cell, rep, peaksfile, bwfile, bwcfile)\n",
    "\n",
    "    # Load peaks file and sort by score\n",
    "    peaksfile_df = pd.read_csv(os.path.join(IMMUNE_SPAN_PATH, peaksfile), sep='\\t',\n",
    "                               names=['chr', 'start', 'end', '4', '5', '6', '7', '8', 'score'])\n",
    "    with pyBigWig.open(os.path.join(IMMUNE_BW_PATH, bwfile)) as bw:\n",
    "        with pyBigWig.open(os.path.join(IMMUNE_BW_PATH, bwcfile)) as bwc:\n",
    "            # Resort top scored peaks by density and start with the most dense ones\n",
    "            total_coverage = sum(bw.stats(chr, exact=True, type='sum')[0] for chr in CHROM_SIZES.keys() if '_' not in chr) / 1e6\n",
    "            total_coveragec = sum(bwc.stats(chr, exact=True, type='sum')[0] for chr in CHROM_SIZES.keys() if '_' not in chr) / 1e6\n",
    "            for _, (chr, start, end) in peaksfile_df[['chr', 'start', 'end']].iterrows():\n",
    "                if '_' in chr:\n",
    "                    continue\n",
    "                peak_coverage = bw.stats(chr, start, end, exact=True, type='sum')[0]\n",
    "                peak_coveragec = bwc.stats(chr, start, end, exact=True, type='sum')[0]\n",
    "                data.append((mod, cell, rep, total_coverage, total_coveragec,\n",
    "                             f'{chr}:{start}-{end}', end - start, peak_coverage, peak_coveragec))\n",
    "\n",
    "immune_peak_coverages_df = pd.DataFrame(\n",
    "    columns=['modification', 'cell', 'replicate', 'total_signal_coverage', 'total_control_coverage',\n",
    "             'peak', 'length', 'signal_coverage', 'control_coverage'],\n",
    "    data=data\n",
    ")\n",
    "del data\n",
    "immune_peak_coverages_df"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "t = pd.concat([immune_peak_coverages_df[immune_peak_coverages_df['modification'] == m].sample(2_000)\n",
    "               for m in immune_peak_coverages_df['modification'].unique()]).reset_index(drop=True).sample(frac=1).reset_index(drop=True)\n",
    "t['RPKM'] = t['signal_coverage'] / t['length'] * 1e3\n",
    "t['RPKM'] /= t['total_signal_coverage']\n",
    "t['RPKM'].clip(lower=0.2, inplace=True)\n",
    "t['length'].clip(upper=3e5, inplace=True)\n",
    "ticks = [100, 1000, 10_000, 100_000]\n",
    "g_results = sns.scatterplot(data=t, x='length', y='RPKM', hue='modification', alpha=0.3,\n",
    "                            hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3'])\n",
    "# g_results = sns.kdeplot(data=t, x='length', y='RPKM', hue='modification', alpha=0.3,\n",
    "#                             hue_order=['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3'], fill='true')\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "g_results.set(yscale='log')\n",
    "# Put a legend to the right of the current axis\n",
    "g_results.axes.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{PATH}/pics/peaks_length_vs_rpkm.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# ULI ChIPseq signal autocorrelation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "ABF_PATH = os.path.expanduser('~/data/2018_chipseq_y20o20')\n",
    "ABF_BW_PATH = os.path.join(ABF_PATH, 'bwext')\n",
    "ABF_MODIFICATIONS = ['k27ac', 'k4me3', 'k4me1', 'k36me3', 'k27me3']\n",
    "\n",
    "CHROM_SIZES_HG19 = {\n",
    "    c: s for _, (c, s) in pd.read_csv(os.path.join(ABF_PATH, 'hg19.chrom.sizes'),\n",
    "                                      sep='\\t', names=['chr', 'size']).iterrows()\n",
    "}\n",
    "! mkdir {ABF_PATH}/pics"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "df_correlations_abf = compute_autocorrelations(ABF_MODIFICATIONS, ['OD11', 'OD12', 'OD13'], [''],\n",
    "                                               {'chr22': CHROM_SIZES_HG19['chr22']},\n",
    "                                               BIN, DS, bwpath=ABF_BW_PATH)\n",
    "df_correlations_abf"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(5, 3))\n",
    "plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t = df_correlations_abf.copy()\n",
    "t.loc[t['d'] == 0, 'd'] = BIN / 2\n",
    "t = t[['modification', 'd', 'correlation']].groupby(['modification', 'd']).mean().reset_index()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=ABF_MODIFICATIONS)\n",
    "# ticks = [10, 100, 1000, 10_000, 100_000, 1_000_000]\n",
    "ticks = [1, 20, 50, 100, 200, 500, 1000, 2000, 5000,\n",
    "         10_000, 20_000, 50_000, 100_000, 200_000, 500_000, 1_000_000]\n",
    "\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'ABF auto correlation bin={BIN}')\n",
    "ax.set_xlim(BIN / 2, 1_000_000)\n",
    "ax.legend(loc='upper right')\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{ABF_PATH}/pics/signal_correlation.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(5, 3))\n",
    "# plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=ABF_MODIFICATIONS)\n",
    "ticks = [100, 1000, 10_000]\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'ABF auto correlation bin={BIN}')\n",
    "ax.set_xlim(50, 20_000)\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{ABF_PATH}/pics/signal_correlation_zoom.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Summary"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# t1 = df_correlations_paper.copy()\n",
    "# t1['dataset'] = 'ENCODE d'\n",
    "# \n",
    "# t2 = df_correlations_roadmap_paper.copy()\n",
    "# t2['dataset'] = 'Roadmap d'\n",
    "# \n",
    "t3 = df_correlations.copy()\n",
    "t3['dataset'] = 'ENCODE'\n",
    "\n",
    "t4 = df_correlations_roadmap.copy()\n",
    "t4['dataset'] = 'Roadmap'\n",
    "\n",
    "t5 = df_correlations_abf.copy()\n",
    "for m1, m2 in zip(['k27ac', 'k4me3', 'k4me1', 'k36me3', 'k27me3'],\n",
    "                  ['H3K27ac', 'H3K4me3', 'H3K4me1', 'H3K36me3', 'H3K27me3']):\n",
    "    t5.loc[t5['modification'] == m1, 'modification'] = m2\n",
    "t5['dataset'] = 'ABF'\n",
    "\n",
    "# summary = pd.concat([t1, t2, t3, t4, t5]).reset_index()\n",
    "summary = pd.concat([t3, t4, t5]).reset_index()\n",
    "summary.loc[summary['modification'] == 'Input', 'modification'] = 'Control'\n",
    "summary"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "t = summary[['dataset','modification', 'd', 'correlation']].groupby(['dataset', 'modification', 'd']).mean().reset_index()\n",
    "\n",
    "plt.figure(figsize=(20, 4))\n",
    "axs = [plt.subplot(1, 6, i + 1) for i in range(6)]\n",
    "for i, m in enumerate(['H3K4me3', 'H3K27ac', 'H3K4me1', 'H3K27me3', 'H3K36me3', 'Control']):\n",
    "    ax = axs[i]\n",
    "    g_results = sns.lineplot(data=t[t['modification'] == m], x=\"d\", y=\"correlation\", hue='dataset', ax=ax,\n",
    "                             hue_order=['ENCODE', 'Roadmap', 'ABF'], alpha=0.8)\n",
    "    ticks = ['1e2', '1e3', '1e4', '1e5', '1e6']\n",
    "    g_results.set(xscale='log')\n",
    "    g_results.set(xticks=[float(t) for t in ticks])\n",
    "    g_results.set(xticklabels=ticks)\n",
    "    ax = g_results.axes\n",
    "    ax.title.set_text(f'{m}')\n",
    "    ax.set_xlim(50, 2_000_000)\n",
    "    # Put a legend to the right of the current axis\n",
    "    if i == 5:\n",
    "        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    else:\n",
    "        ax.legend().set_visible(False)\n",
    "    if i > 0:\n",
    "        ax.set_ylabel(None)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(20, 4))\n",
    "axs = [plt.subplot(1, 6, i + 1) for i in range(6)]\n",
    "for i, m in enumerate(['H3K4me3', 'H3K27ac', 'H3K4me1', 'H3K27me3', 'H3K36me3', 'Control']):\n",
    "    ax = axs[i]\n",
    "    g_results = sns.lineplot(data=t[t['modification'] == m], x=\"d\", y=\"correlation\", hue='dataset', ax=ax,\n",
    "                             hue_order=['ENCODE', 'Roadmap', 'ABF'], alpha=0.8)\n",
    "    ticks = [100, 1000, 10_000]\n",
    "    g_results.set(xscale='log')\n",
    "    g_results.set(xticks=ticks)\n",
    "    g_results.set(xticklabels=ticks)\n",
    "    ax = g_results.axes\n",
    "    ax.title.set_text(f'{m}')\n",
    "    ax.set_xlim(50, 20_000)\n",
    "    # ax.set_ylim(0, 0.8)\n",
    "    # Put a legend to the right of the current axis\n",
    "    if i == 5:\n",
    "        ax.legend(loc='center left', bbox_to_anchor=(1, 0.8))\n",
    "    else:\n",
    "        ax.legend().set_visible(False)\n",
    "    if i > 0:\n",
    "        ax.set_ylabel(None)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "# END",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": "",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/3 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "01e15ec4e90b4fc48e3be812e710f60d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mix GM12878  GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw\n",
      "Computing binned coverage correlations\n",
      "Signal coverage mln 1730.700136\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "32052d0e7af14b23bb39ce4a50ef6afd"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing mix GM12878 binned 50bp coverage correlations on chr chr22 50818468\n",
      "wiggle GM12878  GSM646316_GM12878_H3K27ac_rep1_wiggle_80_100_ext.bw\n",
      "Computing binned coverage correlations\n",
      "Signal coverage mln 1708.1593\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "23efb6fc10d448e2ad60efadfa6afa6a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing wiggle GM12878 binned 50bp coverage correlations on chr chr22 50818468\n",
      "Input GM12878  GSM646332_GM12878_Input_rep1_ext.bw\n",
      "Computing binned coverage correlations\n",
      "Signal coverage mln 1404.343019\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9861f07659c54f7b8054c4f8c25d93af"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing Input GM12878 binned 50bp coverage correlations on chr chr22 50818468\n"
     ]
    },
    {
     "data": {
      "text/plain": "    modification     cell replicate  \\\n0            mix  GM12878             \n1            mix  GM12878             \n2            mix  GM12878             \n3            mix  GM12878             \n4            mix  GM12878             \n..           ...      ...       ...   \n610        Input  GM12878             \n611        Input  GM12878             \n612        Input  GM12878             \n613        Input  GM12878             \n614        Input  GM12878             \n\n                                          file    chr        d  correlation  \\\n0    GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw  chr22        0     1.000000   \n1    GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw  chr22       50     0.972065   \n2    GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw  chr22      100     0.915685   \n3    GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw  chr22      150     0.851984   \n4    GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw  chr22      200     0.785440   \n..                                         ...    ...      ...          ...   \n610        GSM646332_GM12878_Input_rep1_ext.bw  chr22   600000     0.001446   \n611        GSM646332_GM12878_Input_rep1_ext.bw  chr22   700000    -0.003046   \n612        GSM646332_GM12878_Input_rep1_ext.bw  chr22   800000     0.003374   \n613        GSM646332_GM12878_Input_rep1_ext.bw  chr22   900000     0.001388   \n614        GSM646332_GM12878_Input_rep1_ext.bw  chr22  1000000    -0.000347   \n\n       pvalue  \n0    0.000000  \n1    0.000000  \n2    0.000000  \n3    0.000000  \n4    0.000000  \n..        ...  \n610  0.444543  \n611  0.107156  \n612  0.074357  \n613  0.462930  \n614  0.854305  \n\n[615 rows x 8 columns]",
      "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>modification</th>\n      <th>cell</th>\n      <th>replicate</th>\n      <th>file</th>\n      <th>chr</th>\n      <th>d</th>\n      <th>correlation</th>\n      <th>pvalue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>mix</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw</td>\n      <td>chr22</td>\n      <td>0</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>mix</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw</td>\n      <td>chr22</td>\n      <td>50</td>\n      <td>0.972065</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>mix</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw</td>\n      <td>chr22</td>\n      <td>100</td>\n      <td>0.915685</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>mix</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw</td>\n      <td>chr22</td>\n      <td>150</td>\n      <td>0.851984</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>mix</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_mix_ext.bw</td>\n      <td>chr22</td>\n      <td>200</td>\n      <td>0.785440</td>\n      <td>0.000000</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    </tr>\n    <tr>\n      <th>610</th>\n      <td>Input</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646332_GM12878_Input_rep1_ext.bw</td>\n      <td>chr22</td>\n      <td>600000</td>\n      <td>0.001446</td>\n      <td>0.444543</td>\n    </tr>\n    <tr>\n      <th>611</th>\n      <td>Input</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646332_GM12878_Input_rep1_ext.bw</td>\n      <td>chr22</td>\n      <td>700000</td>\n      <td>-0.003046</td>\n      <td>0.107156</td>\n    </tr>\n    <tr>\n      <th>612</th>\n      <td>Input</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646332_GM12878_Input_rep1_ext.bw</td>\n      <td>chr22</td>\n      <td>800000</td>\n      <td>0.003374</td>\n      <td>0.074357</td>\n    </tr>\n    <tr>\n      <th>613</th>\n      <td>Input</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646332_GM12878_Input_rep1_ext.bw</td>\n      <td>chr22</td>\n      <td>900000</td>\n      <td>0.001388</td>\n      <td>0.462930</td>\n    </tr>\n    <tr>\n      <th>614</th>\n      <td>Input</td>\n      <td>GM12878</td>\n      <td></td>\n      <td>GSM646332_GM12878_Input_rep1_ext.bw</td>\n      <td>chr22</td>\n      <td>1000000</td>\n      <td>-0.000347</td>\n      <td>0.854305</td>\n    </tr>\n  </tbody>\n</table>\n<p>615 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_correlations_wiggle_mix = compute_autocorrelations(['mix', 'wiggle', 'Input'], ['GM12878'], [''],\n",
    "                                                      {'chr22': CHROM_SIZES['chr22']},\n",
    "                                                      BIN, DS, bwpath=WIGGLE_MIX_BW_PATH)\n",
    "df_correlations_wiggle_mix"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-22T13:14:07.982937Z",
     "start_time": "2023-09-22T13:09:23.544277Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "df_correlations_wiggle_mix.loc[df_correlations_wiggle_mix['file'].str.contains('mix'), 'modification'] = 'H3K27ac mix'\n",
    "df_correlations_wiggle_mix.loc[df_correlations_wiggle_mix['file'].str.contains('wiggle'), 'modification'] = 'H3K27ac wiggle'"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-22T13:14:07.985408Z",
     "start_time": "2023-09-22T13:14:07.966308Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 360x216 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 351,
       "height": 204
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "encode_k27ac = summary[(summary['modification'] == 'H3K27ac') & (summary['dataset'] == 'ENCODE')].copy()\n",
    "encode_k27ac['modification'] = 'H3K27ac ENCODE'\n",
    "roadmap_k27ac = summary[(summary['modification'] == 'H3K27ac') & (summary['dataset'] == 'Roadmap')].copy()\n",
    "roadmap_k27ac['modification'] = 'H3K27ac Roadmap'\n",
    "df_correlations_wiggle_mix['dataset'] = 'wiggle_mix'\n",
    "t = pd.concat([df_correlations_wiggle_mix, encode_k27ac, roadmap_k27ac]).reset_index(drop=True)\n",
    "# t = pd.concat([encode_k27ac, roadmap_k27ac]).reset_index(drop=True)\n",
    "\n",
    "t = t[['dataset','modification', 'd', 'correlation']].groupby(['dataset', 'modification', 'd']).mean().reset_index()\n",
    "plt.figure(figsize=(5, 3))\n",
    "# plt.figure(figsize=(10, 6))\n",
    "ax = plt.axes()\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t.loc[t['d'] == 0, 'd'] = BIN / 2\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=['H3K27ac mix', 'H3K27ac wiggle', 'H3K27ac ENCODE', 'H3K27ac Roadmap'],\n",
    "                         alpha=0.8)\n",
    "# ticks = [10, 100, 1000, 10_000, 100_000, 1_000_000]\n",
    "# ticks = [1, 20, 50, 100, 200, 500, 1000, 2000, 5000,\n",
    "#          10_000, 20_000, 50_000, 100_000, 200_000, 500_000, 1_000_000]\n",
    "\n",
    "g_results.set(xscale='log')\n",
    "# g_results.set(xticks=ticks)\n",
    "# g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'H3K27ac auto correlation bin={BIN}')\n",
    "ax.set_xlim(BIN / 2, 1_000_000)\n",
    "ax.legend(loc='upper right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'{WIGGLE_MIX_PATH}/pics/signal_correlation.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-22T13:14:08.593156Z",
     "start_time": "2023-09-22T13:14:07.970690Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 360x216 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 352,
       "height": 207
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(10, 6))\n",
    "plt.figure(figsize=(5, 3))\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\",\n",
    "                         hue_order=['H3K27ac mix', 'H3K27ac wiggle', 'H3K27ac ENCODE', 'H3K27ac Roadmap'],\n",
    "                         alpha=0.8)\n",
    "ticks = [1000, 10_000]\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax = g_results.axes\n",
    "ax.title.set_text(f'H3K27ac auto correlation bin={BIN}')\n",
    "ax.set_xlim(500, 20_000)\n",
    "ax.set_ylim(0, 0.7)\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'{WIGGLE_MIX_PATH}/pics/signal_correlation_zoom.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-22T13:14:08.907954Z",
     "start_time": "2023-09-22T13:14:08.592920Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Noise mix"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "ENCODE_NOISE_PATH = os.path.expanduser('~/data/2023_GSE26320_noise')\n",
    "ENCODE_NOISE_BW_PATH = ENCODE_NOISE_PATH + '/bwext'\n",
    "! mkdir -p {ENCODE_NOISE_PATH}/pics"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-14T07:43:38.218803Z",
     "start_time": "2023-09-14T07:43:38.100056Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/4 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "213aa84bf2eb49cca2782692658dbec8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1mln GM12878 rep1 GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw\n",
      "Computing binned coverage correlations\n",
      "Signal coverage mln 1651.295185\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "684b4b21db17420992414b7f3f1e327c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing 1mln GM12878 binned 50bp coverage correlations on chr chr22 50818468\n",
      "2mln GM12878 rep1 GSM646316_GM12878_H3K27ac_rep1_2mln_noise_ext.bw\n",
      "Computing binned coverage correlations\n",
      "Signal coverage mln 1895.529335\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "cbb06320b156488d9594d3c9f1e9077d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing 2mln GM12878 binned 50bp coverage correlations on chr chr22 50818468\n",
      "5mln GM12878 rep1 GSM646316_GM12878_H3K27ac_rep1_5mln_noise_ext.bw\n",
      "Computing binned coverage correlations\n",
      "Signal coverage mln 2441.440753\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "4b4a8afc18da4ffb81a120ac4c327ee2"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing 5mln GM12878 binned 50bp coverage correlations on chr chr22 50818468\n",
      "7mln GM12878 rep1 GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw\n",
      "Computing binned coverage correlations\n",
      "Signal coverage mln 2416.593939\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "18a10be1d5f5411492166653d3b6aeb9"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing 7mln GM12878 binned 50bp coverage correlations on chr chr22 50818468\n"
     ]
    },
    {
     "data": {
      "text/plain": "    modification     cell replicate  \\\n0           1mln  GM12878      rep1   \n1           1mln  GM12878      rep1   \n2           1mln  GM12878      rep1   \n3           1mln  GM12878      rep1   \n4           1mln  GM12878      rep1   \n..           ...      ...       ...   \n815         7mln  GM12878      rep1   \n816         7mln  GM12878      rep1   \n817         7mln  GM12878      rep1   \n818         7mln  GM12878      rep1   \n819         7mln  GM12878      rep1   \n\n                                                 file    chr        d  \\\n0    GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw  chr22        0   \n1    GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw  chr22       50   \n2    GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw  chr22      100   \n3    GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw  chr22      150   \n4    GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw  chr22      200   \n..                                                ...    ...      ...   \n815  GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw  chr22   600000   \n816  GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw  chr22   700000   \n817  GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw  chr22   800000   \n818  GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw  chr22   900000   \n819  GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw  chr22  1000000   \n\n     correlation        pvalue  \n0       1.000000  0.000000e+00  \n1       0.898472  0.000000e+00  \n2       0.738226  0.000000e+00  \n3       0.600387  0.000000e+00  \n4       0.471514  0.000000e+00  \n..           ...           ...  \n815     0.002598  1.482105e-01  \n816    -0.011304  3.144763e-10  \n817     0.008243  4.479169e-06  \n818     0.012101  1.640527e-11  \n819    -0.001704  3.429186e-01  \n\n[820 rows x 8 columns]",
      "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>modification</th>\n      <th>cell</th>\n      <th>replicate</th>\n      <th>file</th>\n      <th>chr</th>\n      <th>d</th>\n      <th>correlation</th>\n      <th>pvalue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>0</td>\n      <td>1.000000</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>50</td>\n      <td>0.898472</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>100</td>\n      <td>0.738226</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>150</td>\n      <td>0.600387</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_1mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>200</td>\n      <td>0.471514</td>\n      <td>0.000000e+00</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    </tr>\n    <tr>\n      <th>815</th>\n      <td>7mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>600000</td>\n      <td>0.002598</td>\n      <td>1.482105e-01</td>\n    </tr>\n    <tr>\n      <th>816</th>\n      <td>7mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>700000</td>\n      <td>-0.011304</td>\n      <td>3.144763e-10</td>\n    </tr>\n    <tr>\n      <th>817</th>\n      <td>7mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>800000</td>\n      <td>0.008243</td>\n      <td>4.479169e-06</td>\n    </tr>\n    <tr>\n      <th>818</th>\n      <td>7mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>900000</td>\n      <td>0.012101</td>\n      <td>1.640527e-11</td>\n    </tr>\n    <tr>\n      <th>819</th>\n      <td>7mln</td>\n      <td>GM12878</td>\n      <td>rep1</td>\n      <td>GSM646316_GM12878_H3K27ac_rep1_7mln_noise_ext.bw</td>\n      <td>chr22</td>\n      <td>1000000</td>\n      <td>-0.001704</td>\n      <td>3.429186e-01</td>\n    </tr>\n  </tbody>\n</table>\n<p>820 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_correlations_noise = compute_autocorrelations(['1mln', '2mln', '5mln', '7mln'], ['GM12878'], ['rep1'],\n",
    "                                                 {'chr22': CHROM_SIZES['chr22']},\n",
    "                                                 BIN, DS, bwpath=ENCODE_NOISE_BW_PATH)\n",
    "df_correlations_noise"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-14T07:52:22.933785Z",
     "start_time": "2023-09-14T07:43:38.237272Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "df_correlations_noise.loc[df_correlations_noise['file'].str.contains('1mln'), 'modification'] = 'H3K27ac_1mln'\n",
    "df_correlations_noise.loc[df_correlations_noise['file'].str.contains('2mln'), 'modification'] = 'H3K27ac_2mln'\n",
    "df_correlations_noise.loc[df_correlations_noise['file'].str.contains('5mln'), 'modification'] = 'H3K27ac_5mln'\n",
    "df_correlations_noise.loc[df_correlations_noise['file'].str.contains('7mln'), 'modification'] = 'H3K27ac_7mln'"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-14T07:52:22.950565Z",
     "start_time": "2023-09-14T07:52:22.931312Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 712,
       "height": 423
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_correlations_noise['dataset'] = 'mix_noise'\n",
    "t = pd.concat([df_correlations_noise,\n",
    "               encode_k27ac,\n",
    "               # df_correlations_wiggle_mix[(df_correlations_wiggle_mix['modification'] == 'H3K27ac_wiggle')|\n",
    "               #                            (df_correlations_wiggle_mix['modification'] == 'H3K27ac_mix')]\n",
    "               ]).reset_index(drop=True)\n",
    "plt.figure(figsize=(10, 6))\n",
    "# plt.figure(figsize=(5, 3))\n",
    "ax = plt.axes()\n",
    "# Show aggregated data, since we don't want error plots\n",
    "t.loc[t['d'] == 0, 'd'] = BIN / 2\n",
    "t = t[['dataset', 'modification', 'd', 'correlation']].groupby(['dataset', 'modification', 'd']).mean().reset_index()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\", alpha=0.8)\n",
    "# ticks = [10, 100, 1000, 10_000, 100_000, 1_000_000]\n",
    "ticks = [1, 20, 50, 100, 200, 500, 1000, 2000, 5000,\n",
    "         10_000, 20_000, 50_000, 100_000, 200_000, 500_000, 1_000_000]\n",
    "\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'ENCODE H3K27ac auto correlation bin={BIN}')\n",
    "ax.set_xlim(BIN / 2, 1_000_000)\n",
    "ax.legend(loc='upper right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'{WIGGLE_MIX_PATH}/pics/signal_correlation.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-14T07:52:23.824599Z",
     "start_time": "2023-09-14T07:52:22.945888Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 360x216 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 352,
       "height": 207
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(10, 6))\n",
    "plt.figure(figsize=(5, 3))\n",
    "ax = plt.axes()\n",
    "g_results = sns.lineplot(data=t, x=\"d\", y=\"correlation\", hue=\"modification\") \n",
    "ticks = [100, 1000, 10_000]\n",
    "g_results.set(xscale='log')\n",
    "g_results.set(xticks=ticks)\n",
    "g_results.set(xticklabels=ticks)\n",
    "ax.title.set_text(f'H3K27ac auto correlation bin={BIN}')\n",
    "ax.set_xlim(50, 20_000)\n",
    "plt.tight_layout()\n",
    "# plt.savefig(f'{WIGGLE_MIX_PATH}/pics/signal_correlation_zoom.pdf', bbox_inches='tight', dpi=300)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-14T07:52:24.418855Z",
     "start_time": "2023-09-14T07:52:23.824013Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# END"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}