PhIP_wt_analysis.ipynb

{
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   "source": [
    "import phippery\n",
    "from phippery.utils import *\n",
    "\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import seaborn as sns\n",
    "\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "Dimensions:                      (peptide_id: 24840, peptide_metadata: 6, sample_id: 45, sample_metadata: 31)\n",
       "Coordinates:\n",
       "  * peptide_id                   (peptide_id) int64 0 1 2 ... 24837 24838 24839\n",
       "  * sample_id                    (sample_id) int64 1461 1421 1432 ... 1467 1420\n",
       "  * sample_metadata              (sample_metadata) object &#x27;Cohort&#x27; ... &#x27;visit...\n",
       "  * peptide_metadata             (peptide_metadata) object &#x27;Oligo&#x27; ... &#x27;is_wt&#x27;\n",
       "Data variables:\n",
       "    counts                       (peptide_id, sample_id) int64 0 0 0 ... 71 1270\n",
       "    sample_table                 (sample_id, sample_metadata) object nan ... nan\n",
       "    peptide_table                (peptide_id, peptide_metadata) object &#x27;aggaa...\n",
       "    cpm                          (peptide_id, sample_id) float64 0.0 ... 1.35...\n",
       "    size_factors                 (peptide_id, sample_id) float64 0.0 ... 1.04...\n",
       "    enrichment                   (peptide_id, sample_id) float64 0.1664 ... 8...\n",
       "    neg_binom_mlxp               (peptide_id, sample_id) float64 0.0 ... 3.37\n",
       "    smooth_flank_1_enr_diff_sel  (peptide_id, sample_id) float64 0.4527 ... -...\n",
       "Attributes:\n",
       "    collapsed_variable:  None</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c99ce099-4610-4805-b501-7ce30379f7d2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c99ce099-4610-4805-b501-7ce30379f7d2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>peptide_id</span>: 24840</li><li><span class='xr-has-index'>peptide_metadata</span>: 6</li><li><span class='xr-has-index'>sample_id</span>: 45</li><li><span class='xr-has-index'>sample_metadata</span>: 31</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e36f4e4e-b8e2-4c43-ab0b-cc51885e9186' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e36f4e4e-b8e2-4c43-ab0b-cc51885e9186' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>peptide_id</span></div><div class='xr-var-dims'>(peptide_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 24836 24837 24838 24839</div><input id='attrs-b07d1f1d-defb-455c-b159-11e83444a805' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b07d1f1d-defb-455c-b159-11e83444a805' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1d92179-0b83-4b38-a6d8-73f426efed2f' class='xr-var-data-in' type='checkbox'><label for='data-f1d92179-0b83-4b38-a6d8-73f426efed2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 24837, 24838, 24839])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample_id</span></div><div class='xr-var-dims'>(sample_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1461 1421 1432 ... 1466 1467 1420</div><input id='attrs-83a1b37b-3eba-4e2a-88a9-218006933445' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83a1b37b-3eba-4e2a-88a9-218006933445' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5e68ce5-0cc2-4b79-9034-1a0eba4f6ffa' class='xr-var-data-in' type='checkbox'><label for='data-d5e68ce5-0cc2-4b79-9034-1a0eba4f6ffa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1461, 1421, 1432, 1483, 1463, 1441, 1445, 1447, 1416, 1468, 1422, 1423,\n",
       "       1424, 1474, 1426, 1476, 1477, 1478, 1413, 1430, 1431, 1482, 1435, 1436,\n",
       "       1437, 1438, 1439, 1440, 1491, 1492, 1444, 1446, 1464, 1448, 1449, 1450,\n",
       "       1451, 1452, 1453, 1454, 1455, 1505, 1466, 1467, 1420])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample_metadata</span></div><div class='xr-var-dims'>(sample_metadata)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Cohort&#x27; ... &#x27;visit_number&#x27;</div><input id='attrs-b4202d1d-ad1a-4106-bfff-6294a17d5b0c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4202d1d-ad1a-4106-bfff-6294a17d5b0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b77d334-1c49-4cbb-bec9-8dac2965640f' class='xr-var-data-in' type='checkbox'><label for='data-4b77d334-1c49-4cbb-bec9-8dac2965640f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cohort&#x27;, &#x27;Vaccine&#x27;, &#x27;age&#x27;, &#x27;age_range&#x27;, &#x27;average quality:&#x27;,\n",
       "       &#x27;control_status&#x27;, &#x27;days_post_infection&#x27;, &#x27;days_post_vaccination&#x27;,\n",
       "       &#x27;dose&#x27;, &#x27;error rate:&#x27;, &#x27;experiment&#x27;, &#x27;fastq_filename&#x27;, &#x27;library_batch&#x27;,\n",
       "       &#x27;manuscript_cohort&#x27;, &#x27;participant_ID&#x27;, &#x27;patient_status_infection&#x27;,\n",
       "       &#x27;patient_status_vaccination&#x27;, &#x27;race&#x27;, &#x27;raw total sequences:&#x27;,\n",
       "       &#x27;reads mapped:&#x27;, &#x27;reference&#x27;, &#x27;sample_ID&#x27;, &#x27;sample_group&#x27;,\n",
       "       &#x27;sample_type&#x27;, &#x27;seq_dir&#x27;, &#x27;sex&#x27;, &#x27;source&#x27;, &#x27;species&#x27;, &#x27;submitted_by&#x27;,\n",
       "       &#x27;technical_replicate_id&#x27;, &#x27;visit_number&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>peptide_metadata</span></div><div class='xr-var-dims'>(peptide_metadata)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Oligo&#x27; &#x27;Virus&#x27; ... &#x27;is_wt&#x27;</div><input id='attrs-c64d7649-f973-466f-b72a-8c8c98edebeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c64d7649-f973-466f-b72a-8c8c98edebeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff71aa6-3e97-402e-87c5-a31b7dc744e3' class='xr-var-data-in' type='checkbox'><label for='data-dff71aa6-3e97-402e-87c5-a31b7dc744e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Oligo&#x27;, &#x27;Virus&#x27;, &#x27;Protein&#x27;, &#x27;Loc&#x27;, &#x27;aa_sub&#x27;, &#x27;is_wt&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43cfb3b1-ed08-4a2e-9c1f-d43b2e52d181' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43cfb3b1-ed08-4a2e-9c1f-d43b2e52d181' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>counts</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 1 4 ... 295 436 68 71 1270</div><input id='attrs-80619d8f-88e6-470b-92a4-7d506ea1c7c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80619d8f-88e6-470b-92a4-7d506ea1c7c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a14e4b31-4b85-4108-af4c-618ea4419b4f' class='xr-var-data-in' type='checkbox'><label for='data-a14e4b31-4b85-4108-af4c-618ea4419b4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   0,    0,    0, ...,    0,    1,    7],\n",
       "       [   0,    0,    0, ...,    0,    0,    1],\n",
       "       [   0,    0,    1, ...,    0,    1,    2],\n",
       "       ...,\n",
       "       [ 183,   61,  229, ...,  357,  298, 1296],\n",
       "       [  33,   33,  128, ...,  257,  278,  837],\n",
       "       [  22,   73,  487, ...,   68,   71, 1270]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sample_table</span></div><div class='xr-var-dims'>(sample_id, sample_metadata)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan nan ... &#x27;caitlin&#x27; 954 nan</div><input id='attrs-cefe208f-058d-4c48-9a7d-802ff43f7a49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cefe208f-058d-4c48-9a7d-802ff43f7a49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14abeedf-69d2-442a-b5a8-c2a5efbcbdbe' class='xr-var-data-in' type='checkbox'><label for='data-14abeedf-69d2-442a-b5a8-c2a5efbcbdbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., &#x27;caitlin&#x27;, 995, nan],\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 955, nan],\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 966, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 1000, nan],\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 1001, nan],\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 954, nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peptide_table</span></div><div class='xr-var-dims'>(peptide_id, peptide_metadata)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;aggaattctacgctgagtGGAGGAGGTGGTT...</div><input id='attrs-5ae7d432-bf0e-44ea-a162-948b155de14f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ae7d432-bf0e-44ea-a162-948b155de14f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffdf419c-b48e-436e-b186-750fef408a72' class='xr-var-data-in' type='checkbox'><label for='data-ffdf419c-b48e-436e-b186-750fef408a72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;aggaattctacgctgagtGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTGGTTTTGTGTTTCTGGTGCTGCTGCCGCTGGTGAGCAGCCAGTGCGTGtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S1&#x27;, 1, &#x27;G&#x27;, False],\n",
       "       [&#x27;aggaattctacgctgagtGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTGAATTTGTGTTTCTGGTGCTGCTGCCGCTGGTGAGCAGCCAGTGCGTGtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S1&#x27;, 1, &#x27;E&#x27;, False],\n",
       "       [&#x27;aggaattctacgctgagtGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTGACTTTGTGTTTCTGGTGCTGCTGCCGCTGGTGAGCAGCCAGTGCGTGtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S1&#x27;, 1, &#x27;D&#x27;, False],\n",
       "       ...,\n",
       "       [&#x27;aggaattctacgctgagtAGCCTGATTGATCTGCAGGAACTGGGCAAGTATGAGCAGTACATCCAGGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S2&#x27;, 1211, &#x27;Q&#x27;, False],\n",
       "       [&#x27;aggaattctacgctgagtAGCCTGATTGATCTGCAGGAACTGGGCAAGTATGAGCAGTACATCCACGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S2&#x27;, 1211, &#x27;H&#x27;, False],\n",
       "       [&#x27;aggaattctacgctgagtAGCCTGATTGATCTGCAGGAACTGGGCAAGTATGAGCAGTACATCCCGGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S2&#x27;, 1211, &#x27;P&#x27;, False]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cpm</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 68.31 1.351e+03</div><input id='attrs-f230a034-52af-4477-8d48-8136beb466a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f230a034-52af-4477-8d48-8136beb466a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a28d032-84af-4ffe-823c-c8949574d005' class='xr-var-data-in' type='checkbox'><label for='data-0a28d032-84af-4ffe-823c-c8949574d005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        9.60000e-01, 7.45000e+00],\n",
       "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        0.00000e+00, 1.06000e+00],\n",
       "       [0.00000e+00, 0.00000e+00, 9.90000e-01, ..., 0.00000e+00,\n",
       "        9.60000e-01, 2.13000e+00],\n",
       "       ...,\n",
       "       [1.92160e+02, 7.07800e+01, 2.27140e+02, ..., 5.39200e+02,\n",
       "        2.86690e+02, 1.37891e+03],\n",
       "       [3.46500e+01, 3.82900e+01, 1.26960e+02, ..., 3.88170e+02,\n",
       "        2.67450e+02, 8.90550e+02],\n",
       "       [2.31000e+01, 8.47100e+01, 4.83050e+02, ..., 1.02710e+02,\n",
       "        6.83100e+01, 1.35125e+03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>size_factors</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 41.78 1.048e+03</div><input id='attrs-55961ebf-cfd9-4569-bbb3-2529816205c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55961ebf-cfd9-4569-bbb3-2529816205c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8199ae25-d4a4-47e7-a17a-002d2545c275' class='xr-var-data-in' type='checkbox'><label for='data-8199ae25-d4a4-47e7-a17a-002d2545c275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        5.90000e-01, 5.78000e+00],\n",
       "       [0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,\n",
       "        0.00000e+00, 8.30000e-01],\n",
       "       [0.00000e+00, 0.00000e+00, 8.10000e-01, ..., 0.00000e+00,\n",
       "        5.90000e-01, 1.65000e+00],\n",
       "       ...,\n",
       "       [2.59710e+02, 5.88400e+01, 1.85460e+02, ..., 1.03811e+03,\n",
       "        1.75360e+02, 1.06932e+03],\n",
       "       [4.68300e+01, 3.18300e+01, 1.03660e+02, ..., 7.47320e+02,\n",
       "        1.63590e+02, 6.90600e+02],\n",
       "       [3.12200e+01, 7.04200e+01, 3.94400e+02, ..., 1.97740e+02,\n",
       "        4.17800e+01, 1.04786e+03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>enrichment</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1664 0.1664 ... 0.4103 8.004</div><input id='attrs-24a8f4db-2027-404e-bf7e-57ca604c36bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24a8f4db-2027-404e-bf7e-57ca604c36bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-167bc963-2463-4784-9237-91d01cacbbda' class='xr-var-data-in' type='checkbox'><label for='data-167bc963-2463-4784-9237-91d01cacbbda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.16643313, 0.16643345, 0.16643227, ..., 0.1664331 , 0.32620836,\n",
       "        1.40635519],\n",
       "       [0.16948167, 0.169482  , 0.1694808 , ..., 0.16948164, 0.16948137,\n",
       "        0.34913106],\n",
       "       [0.08795143, 0.08795161, 0.17502235, ..., 0.08795141, 0.17238446,\n",
       "        0.27528695],\n",
       "       ...,\n",
       "       [1.24999127, 0.4645091 , 1.47634729, ..., 3.49578137, 1.86171682,\n",
       "        8.92973931],\n",
       "       [0.3601754 , 0.39695158, 1.29278431, ..., 3.93182133, 2.71217059,\n",
       "        9.00737958],\n",
       "       [0.14264988, 0.5073257 , 2.86511401, ..., 0.61386788, 0.41025071,\n",
       "        8.0040464 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neg_binom_mlxp</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 0.8382 0.2683 3.37</div><input id='attrs-ceeb52fe-94a8-401b-a54f-04861b0dd0c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ceeb52fe-94a8-401b-a54f-04861b0dd0c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e264c355-adfb-4470-a346-47cf67042353' class='xr-var-data-in' type='checkbox'><label for='data-e264c355-adfb-4470-a346-47cf67042353' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.        , 0.        , 0.        , ..., 0.        , 0.53731528,\n",
       "        1.26535013],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.59475867],\n",
       "       [0.        , 0.        , 0.55735328, ..., 0.        , 0.55735328,\n",
       "        0.69301118],\n",
       "       ...,\n",
       "       [1.34050574, 0.40677521, 1.01296125, ..., 4.56670603, 0.96781057,\n",
       "        4.69202037],\n",
       "       [0.61077374, 0.46874261, 1.08942708, ..., 5.62317931, 1.55080273,\n",
       "        5.23711263],\n",
       "       [0.22245126, 0.38888933, 1.45950568, ..., 0.8382433 , 0.26831438,\n",
       "        3.36971612]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smooth_flank_1_enr_diff_sel</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4527 0.08707 ... -4.141 -0.9426</div><input id='attrs-ae7f7b74-924e-474b-a4f2-59efe9a9506b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae7f7b74-924e-474b-a4f2-59efe9a9506b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf3cee2e-e13e-472f-931c-a16df7dfa5a4' class='xr-var-data-in' type='checkbox'><label for='data-bf3cee2e-e13e-472f-931c-a16df7dfa5a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 4.52742394e-01,  8.70679521e-02, -3.41055336e-01, ...,\n",
       "         8.70677725e-02, -8.94431928e-02,  4.22377249e-01],\n",
       "       [ 4.62527849e-01,  8.89498155e-02, -3.34506364e-01, ...,\n",
       "         8.89496323e-02, -9.73654999e-01, -2.41178925e+00],\n",
       "       [ 1.08894016e-01,  2.09416652e-02, -3.22897925e-01, ...,\n",
       "         2.09416203e-02, -9.50720053e-01, -2.89516548e+00],\n",
       "       ...,\n",
       "       [-2.68748764e+00, -1.11608141e-01, -3.10680366e+00, ...,\n",
       "         1.11468314e+00, -4.44187934e-01,  3.64456113e-01],\n",
       "       [-6.42218738e+00, -2.48051221e-01, -3.72791520e+00, ...,\n",
       "         1.47286671e+00,  4.75343045e-01,  4.67844800e-01],\n",
       "       [-9.20211915e+00, -3.50623226e-02, -5.11212881e-03, ...,\n",
       "        -4.18598889e+00, -4.14060505e+00, -9.42563293e-01]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3457997d-5eaa-4e30-b944-6727575b289d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3457997d-5eaa-4e30-b944-6727575b289d' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>collapsed_variable :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                      (peptide_id: 24840, peptide_metadata: 6, sample_id: 45, sample_metadata: 31)\n",
       "Coordinates:\n",
       "  * peptide_id                   (peptide_id) int64 0 1 2 ... 24837 24838 24839\n",
       "  * sample_id                    (sample_id) int64 1461 1421 1432 ... 1467 1420\n",
       "  * sample_metadata              (sample_metadata) object 'Cohort' ... 'visit...\n",
       "  * peptide_metadata             (peptide_metadata) object 'Oligo' ... 'is_wt'\n",
       "Data variables:\n",
       "    counts                       (peptide_id, sample_id) int64 0 0 0 ... 71 1270\n",
       "    sample_table                 (sample_id, sample_metadata) object nan ... nan\n",
       "    peptide_table                (peptide_id, peptide_metadata) object 'aggaa...\n",
       "    cpm                          (peptide_id, sample_id) float64 0.0 ... 1.35...\n",
       "    size_factors                 (peptide_id, sample_id) float64 0.0 ... 1.04...\n",
       "    enrichment                   (peptide_id, sample_id) float64 0.1664 ... 8...\n",
       "    neg_binom_mlxp               (peptide_id, sample_id) float64 0.0 ... 3.37\n",
       "    smooth_flank_1_enr_diff_sel  (peptide_id, sample_id) float64 0.4527 ... -...\n",
       "Attributes:\n",
       "    collapsed_variable:  None"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output directory\n",
    "outdir = \"results\"\n",
    "if not os.path.exists(outdir): os.mkdir(outdir)\n",
    "\n",
    "# Okabe-Ito colors\n",
    "oi_black         = '#000000'\n",
    "oi_orange        = '#E69F00'\n",
    "oi_skyblue       = '#56B4E9'\n",
    "oi_bluishgreen   = '#009E73'\n",
    "oi_yellow        = '#F0E442'\n",
    "oi_blue          = '#0072B2'\n",
    "oi_vermillion    = '#D55E00'\n",
    "oi_reddishpurple = '#CC79A7'\n",
    "\n",
    "# epitope region position boundaries\n",
    "epitope_limits = {\n",
    "    \"NTD\"   : [ 305, 325],\n",
    "    \"RBD1\"  : [ 405, 439],\n",
    "    \"RBD2\"  : [ 440, 475],\n",
    "    \"CTD\"   : [ 660, 700],\n",
    "    \"preFP\" : [ 770, 804],\n",
    "    \"FP\"    : [ 805, 835],\n",
    "    \"SHH\"   : [1135,1170]\n",
    "}\n",
    "\n",
    "# input file\n",
    "ds = phippery.load(\"LK_DMS_1rep_layered.phip\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample IDs for LK individuals\n",
    "lk_infant_sams = sample_id_coordinate_subset(ds,where='sample_group',is_equal_to='LK African Infant')\n",
    "lk_mother_sams = sample_id_coordinate_subset(ds,where='sample_group',is_equal_to='LK African Mother')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                      (peptide_id: 1211, peptide_metadata: 6, sample_id: 45, sample_metadata: 31)\n",
       "Coordinates:\n",
       "  * peptide_id                   (peptide_id) int64 9 36 43 ... 24810 24827\n",
       "  * sample_id                    (sample_id) int64 1461 1421 1432 ... 1467 1420\n",
       "  * sample_metadata              (sample_metadata) object &#x27;Cohort&#x27; ... &#x27;visit...\n",
       "  * peptide_metadata             (peptide_metadata) object &#x27;Oligo&#x27; ... &#x27;is_wt&#x27;\n",
       "Data variables:\n",
       "    counts                       (peptide_id, sample_id) int64 0 0 ... 503 1767\n",
       "    sample_table                 (sample_id, sample_metadata) object nan ... nan\n",
       "    peptide_table                (peptide_id, peptide_metadata) object &#x27;aggaa...\n",
       "    cpm                          (peptide_id, sample_id) float64 0.0 ... 1.88...\n",
       "    size_factors                 (peptide_id, sample_id) float64 0.0 ... 1.45...\n",
       "    enrichment                   (peptide_id, sample_id) float64 0.07187 ... ...\n",
       "    neg_binom_mlxp               (peptide_id, sample_id) float64 0.0 ... 3.511\n",
       "    smooth_flank_1_enr_diff_sel  (peptide_id, sample_id) float64 0.0 0.0 ... 0.0\n",
       "Attributes:\n",
       "    collapsed_variable:  None</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f0751101-cdfb-4ef4-9d0a-9c28483448d1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f0751101-cdfb-4ef4-9d0a-9c28483448d1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>peptide_id</span>: 1211</li><li><span class='xr-has-index'>peptide_metadata</span>: 6</li><li><span class='xr-has-index'>sample_id</span>: 45</li><li><span class='xr-has-index'>sample_metadata</span>: 31</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8926f7bb-5298-4188-aaff-2beb6bfe8d31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8926f7bb-5298-4188-aaff-2beb6bfe8d31' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>peptide_id</span></div><div class='xr-var-dims'>(peptide_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>9 36 43 76 ... 24794 24810 24827</div><input id='attrs-139ccaf6-6877-4405-9fe0-917c16288e2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-139ccaf6-6877-4405-9fe0-917c16288e2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3103b9ab-ef90-4732-88e2-c04bb09551f3' class='xr-var-data-in' type='checkbox'><label for='data-3103b9ab-ef90-4732-88e2-c04bb09551f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    9,    36,    43, ..., 24794, 24810, 24827])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample_id</span></div><div class='xr-var-dims'>(sample_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1461 1421 1432 ... 1466 1467 1420</div><input id='attrs-f253fdd6-bbff-4c5f-870c-c5641ba9ad6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f253fdd6-bbff-4c5f-870c-c5641ba9ad6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d884a8b-2e0c-4f7b-8d74-d9e886a607ea' class='xr-var-data-in' type='checkbox'><label for='data-5d884a8b-2e0c-4f7b-8d74-d9e886a607ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1461, 1421, 1432, 1483, 1463, 1441, 1445, 1447, 1416, 1468, 1422, 1423,\n",
       "       1424, 1474, 1426, 1476, 1477, 1478, 1413, 1430, 1431, 1482, 1435, 1436,\n",
       "       1437, 1438, 1439, 1440, 1491, 1492, 1444, 1446, 1464, 1448, 1449, 1450,\n",
       "       1451, 1452, 1453, 1454, 1455, 1505, 1466, 1467, 1420])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample_metadata</span></div><div class='xr-var-dims'>(sample_metadata)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Cohort&#x27; ... &#x27;visit_number&#x27;</div><input id='attrs-5fcdac9b-6520-4a2e-8e3b-fa91dc0afc98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5fcdac9b-6520-4a2e-8e3b-fa91dc0afc98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b4f20a6-1fb0-4b68-8867-83a135c7577b' class='xr-var-data-in' type='checkbox'><label for='data-8b4f20a6-1fb0-4b68-8867-83a135c7577b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cohort&#x27;, &#x27;Vaccine&#x27;, &#x27;age&#x27;, &#x27;age_range&#x27;, &#x27;average quality:&#x27;,\n",
       "       &#x27;control_status&#x27;, &#x27;days_post_infection&#x27;, &#x27;days_post_vaccination&#x27;,\n",
       "       &#x27;dose&#x27;, &#x27;error rate:&#x27;, &#x27;experiment&#x27;, &#x27;fastq_filename&#x27;, &#x27;library_batch&#x27;,\n",
       "       &#x27;manuscript_cohort&#x27;, &#x27;participant_ID&#x27;, &#x27;patient_status_infection&#x27;,\n",
       "       &#x27;patient_status_vaccination&#x27;, &#x27;race&#x27;, &#x27;raw total sequences:&#x27;,\n",
       "       &#x27;reads mapped:&#x27;, &#x27;reference&#x27;, &#x27;sample_ID&#x27;, &#x27;sample_group&#x27;,\n",
       "       &#x27;sample_type&#x27;, &#x27;seq_dir&#x27;, &#x27;sex&#x27;, &#x27;source&#x27;, &#x27;species&#x27;, &#x27;submitted_by&#x27;,\n",
       "       &#x27;technical_replicate_id&#x27;, &#x27;visit_number&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>peptide_metadata</span></div><div class='xr-var-dims'>(peptide_metadata)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Oligo&#x27; &#x27;Virus&#x27; ... &#x27;is_wt&#x27;</div><input id='attrs-4a015a98-0d7c-4518-9b74-d3c7790de090' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a015a98-0d7c-4518-9b74-d3c7790de090' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67310300-ab84-4bcb-bf9c-d91121248ca7' class='xr-var-data-in' type='checkbox'><label for='data-67310300-ab84-4bcb-bf9c-d91121248ca7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Oligo&#x27;, &#x27;Virus&#x27;, &#x27;Protein&#x27;, &#x27;Loc&#x27;, &#x27;aa_sub&#x27;, &#x27;is_wt&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-227a7bb3-c47c-4d68-8b35-3f4947b446c7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-227a7bb3-c47c-4d68-8b35-3f4947b446c7' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>counts</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 5 0 2 ... 456 276 348 503 1767</div><input id='attrs-b63c9389-7e20-468e-bf65-43c9cad2a126' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b63c9389-7e20-468e-bf65-43c9cad2a126' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd145538-f741-4563-8595-c73088e317f9' class='xr-var-data-in' type='checkbox'><label for='data-dd145538-f741-4563-8595-c73088e317f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   0,    0,    5, ...,    0,    4,   14],\n",
       "       [   8,    0,    0, ...,    0,   21,   21],\n",
       "       [   0,    0,    0, ...,    0,    6,   10],\n",
       "       ...,\n",
       "       [ 236,   98,  639, ...,   79,  304, 1783],\n",
       "       [ 377,  209, 1314, ...,  429,  432, 2675],\n",
       "       [ 632,   98,  627, ...,  348,  503, 1767]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sample_table</span></div><div class='xr-var-dims'>(sample_id, sample_metadata)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan nan ... &#x27;caitlin&#x27; 954 nan</div><input id='attrs-182ba4a6-76ed-4854-aea4-0e1b7e874393' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-182ba4a6-76ed-4854-aea4-0e1b7e874393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3d48301-2000-4668-80a1-35aa9ba6a254' class='xr-var-data-in' type='checkbox'><label for='data-f3d48301-2000-4668-80a1-35aa9ba6a254' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., &#x27;caitlin&#x27;, 995, nan],\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 955, nan],\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 966, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 1000, nan],\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 1001, nan],\n",
       "       [nan, nan, nan, ..., &#x27;caitlin&#x27;, 954, nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>peptide_table</span></div><div class='xr-var-dims'>(peptide_id, peptide_metadata)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;aggaattctacgctgagtGGAGGAGGTGGTT...</div><input id='attrs-e31c27ed-ab06-414f-90ba-01afd1cbb474' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e31c27ed-ab06-414f-90ba-01afd1cbb474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b574b968-968f-4553-beb8-be5034b99e82' class='xr-var-data-in' type='checkbox'><label for='data-b574b968-968f-4553-beb8-be5034b99e82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;aggaattctacgctgagtGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTATGTTTGTGTTTCTGGTGCTGCTGCCGCTGGTGAGCAGCCAGTGCGTGtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S1&#x27;, 1, &#x27;M&#x27;, True],\n",
       "       [&#x27;aggaattctacgctgagtGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTATGTTCGTGTTTCTGGTGCTGCTGCCGCTGGTGAGCAGCCAGTGCGTGAACtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S1&#x27;, 2, &#x27;F&#x27;, True],\n",
       "       [&#x27;aggaattctacgctgagtGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTATGTTTGTTTTTCTGGTGCTGCTGCCGCTGGTGAGCAGCCAGTGCGTGAACCTGtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S1&#x27;, 3, &#x27;V&#x27;, True],\n",
       "       ...,\n",
       "       [&#x27;aggaattctacgctgagtAACGAAAGCCTGATTGATCTGCAGGAACTGGGCAAGTATGAGCAGTACATCAAGGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S2&#x27;, 1209, &#x27;Y&#x27;, True],\n",
       "       [&#x27;aggaattctacgctgagtGAAAGCCTGATTGATCTGCAGGAACTGGGCAAGTATGAGCAGTACATCAAGGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S2&#x27;, 1210, &#x27;I&#x27;, True],\n",
       "       [&#x27;aggaattctacgctgagtAGCCTGATTGATCTGCAGGAACTGGGCAAGTATGAGCAGTACATCAAAGGAGGAGGTGGTTCTGGTGGTGGAGGTTCAGGTGGTGGTGGAAGTtgatagcaagcttgcc&#x27;,\n",
       "        &#x27;SARSCoV2&#x27;, &#x27;S2&#x27;, 1211, &#x27;K&#x27;, True]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cpm</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 4.96 ... 483.9 1.88e+03</div><input id='attrs-636aedef-e4a0-405f-a7af-06e22890ed6c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-636aedef-e4a0-405f-a7af-06e22890ed6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33e53170-4582-4c59-8877-8a9464d9cafd' class='xr-var-data-in' type='checkbox'><label for='data-33e53170-4582-4c59-8877-8a9464d9cafd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   0.  ,    0.  ,    4.96, ...,    0.  ,    3.85,   14.9 ],\n",
       "       [   8.4 ,    0.  ,    0.  , ...,    0.  ,   20.2 ,   22.34],\n",
       "       [   0.  ,    0.  ,    0.  , ...,    0.  ,    5.77,   10.64],\n",
       "       ...,\n",
       "       [ 247.82,  113.72,  633.82, ...,  119.32,  292.46, 1897.07],\n",
       "       [ 395.88,  242.52, 1303.35, ...,  647.95,  415.61, 2846.14],\n",
       "       [ 663.64,  113.72,  621.92, ...,  525.61,  483.91, 1880.05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>size_factors</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 4.05 ... 296.0 1.458e+03</div><input id='attrs-503b3d38-02d4-4d36-98b0-3229668559e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-503b3d38-02d4-4d36-98b0-3229668559e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6131364c-09ec-4cad-b22c-20e946263377' class='xr-var-data-in' type='checkbox'><label for='data-6131364c-09ec-4cad-b22c-20e946263377' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   0.  ,    0.  ,    4.05, ...,    0.  ,    2.35,   11.55],\n",
       "       [  11.35,    0.  ,    0.  , ...,    0.  ,   12.36,   17.33],\n",
       "       [   0.  ,    0.  ,    0.  , ...,    0.  ,    3.53,    8.25],\n",
       "       ...,\n",
       "       [ 334.93,   94.53,  517.5 , ...,  229.72,  178.89, 1471.13],\n",
       "       [ 535.04,  201.61, 1064.15, ..., 1247.48,  254.21, 2207.11],\n",
       "       [ 896.93,   94.53,  507.78, ..., 1011.94,  295.99, 1457.93]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>enrichment</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07187 0.07187 ... 2.233 8.661</div><input id='attrs-23247458-d14a-4b63-94d1-f58b8c171ef0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23247458-d14a-4b63-94d1-f58b8c171ef0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0c964b6-0026-4f02-9936-c7176ded6346' class='xr-var-data-in' type='checkbox'><label for='data-f0c964b6-0026-4f02-9936-c7176ded6346' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.07186546, 0.07186561, 0.42831567, ..., 0.07186545, 0.34854679,\n",
       "        1.14265649],\n",
       "       [0.67549799, 0.07186164, 0.07186107, ..., 0.07186147, 1.52346052,\n",
       "        1.67724078],\n",
       "       [0.12709059, 0.12709085, 0.1270899 , ..., 0.12709056, 0.86040168,\n",
       "        1.47932919],\n",
       "       ...,\n",
       "       [1.05238775, 0.48521099, 2.68496361, ..., 0.50889504, 1.24119061,\n",
       "        8.02788151],\n",
       "       [1.10051965, 0.67526497, 3.61684613, ..., 1.79949119, 1.15522704,\n",
       "        7.89488194],\n",
       "       [3.06036621, 0.52823486, 2.86824672, ..., 2.42480001, 2.23278618,\n",
       "        8.66134814]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>neg_binom_mlxp</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.5897 ... 0.8915 3.511</div><input id='attrs-8444d707-2d41-4ec4-93c2-3bb36132317e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8444d707-2d41-4ec4-93c2-3bb36132317e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a309988-281d-4092-b210-ea752a21a790' class='xr-var-data-in' type='checkbox'><label for='data-2a309988-281d-4092-b210-ea752a21a790' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.        , 0.        , 0.58969287, ..., 0.        , 0.47299505,\n",
       "        0.88599333],\n",
       "       [0.71457769, 0.        , 0.        , ..., 0.        , 0.74340127,\n",
       "        0.87744647],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.54211505,\n",
       "        0.84382947],\n",
       "       ...,\n",
       "       [0.87438582, 0.27317108, 1.31835664, ..., 0.61567121, 0.48814809,\n",
       "        3.58578971],\n",
       "       [0.86901871, 0.33866158, 1.6969091 , ..., 1.98190209, 0.42372146,\n",
       "        3.47166653],\n",
       "       [2.27725338, 0.36703924, 1.39506337, ..., 2.53278743, 0.89154906,\n",
       "        3.51145123]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smooth_flank_1_enr_diff_sel</span></div><div class='xr-var-dims'>(peptide_id, sample_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-79fcda85-30c6-4746-910c-d05e8f3211a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79fcda85-30c6-4746-910c-d05e8f3211a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfdb5f36-becd-40c1-a01c-abab59b2df58' class='xr-var-data-in' type='checkbox'><label for='data-cfdb5f36-becd-40c1-a01c-abab59b2df58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da2a6d88-0334-4eca-9d65-6692fcac067c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da2a6d88-0334-4eca-9d65-6692fcac067c' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>collapsed_variable :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                      (peptide_id: 1211, peptide_metadata: 6, sample_id: 45, sample_metadata: 31)\n",
       "Coordinates:\n",
       "  * peptide_id                   (peptide_id) int64 9 36 43 ... 24810 24827\n",
       "  * sample_id                    (sample_id) int64 1461 1421 1432 ... 1467 1420\n",
       "  * sample_metadata              (sample_metadata) object 'Cohort' ... 'visit...\n",
       "  * peptide_metadata             (peptide_metadata) object 'Oligo' ... 'is_wt'\n",
       "Data variables:\n",
       "    counts                       (peptide_id, sample_id) int64 0 0 ... 503 1767\n",
       "    sample_table                 (sample_id, sample_metadata) object nan ... nan\n",
       "    peptide_table                (peptide_id, peptide_metadata) object 'aggaa...\n",
       "    cpm                          (peptide_id, sample_id) float64 0.0 ... 1.88...\n",
       "    size_factors                 (peptide_id, sample_id) float64 0.0 ... 1.45...\n",
       "    enrichment                   (peptide_id, sample_id) float64 0.07187 ... ...\n",
       "    neg_binom_mlxp               (peptide_id, sample_id) float64 0.0 ... 3.511\n",
       "    smooth_flank_1_enr_diff_sel  (peptide_id, sample_id) float64 0.0 0.0 ... 0.0\n",
       "Attributes:\n",
       "    collapsed_variable:  None"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# restrict to wildtype peptides\n",
    "wt_pids      = set(peptide_id_coordinate_subset(ds,where='is_wt',is_equal_to=True))\n",
    "S1D614G_pids = set(peptide_id_coordinate_subset(ds,where='Protein',is_equal_to='S1D614G'))\n",
    "pids_to_keep = list(wt_pids - S1D614G_pids)\n",
    "pids_to_keep.sort()\n",
    "wt_ds = ds.loc[dict(peptide_id=pids_to_keep)]\n",
    "wt_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peptide enrichment heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 706,
       "width": 1027
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ordered list of participant IDs for arranging along y-axis\n",
    "yticklabels = [\n",
    "    # LK Infants\n",
    "      '8', '18', '25', '41', '50','142','169','185','186','133',\n",
    "    # LK Mothers\n",
    "      '8', '18', '25', '41', '50','142','169','185','186',\n",
    "     '27', '75', '79','101','105','106','111','112','115','132',\n",
    "    '139','146','149','155','166','168','172','183','189','194',\n",
    "    '199','200','202','203','204','205'\n",
    "]\n",
    "    \n",
    "ytickorder = []\n",
    "istart=0\n",
    "iend=0\n",
    "for group in ['LK African Infant', 'LK African Mother']:\n",
    "    group_sams = sample_id_coordinate_subset(wt_ds,where='sample_group',is_equal_to=group)\n",
    "    group_ds = wt_ds.loc[dict(sample_id=group_sams)]\n",
    "    iend = iend + len(group_sams)\n",
    "    for yticklabel in yticklabels[istart:iend]:\n",
    "        part_id = 'ptnum_'+yticklabel\n",
    "        ytickorder.append(sample_id_coordinate_subset(group_ds,where='participant_ID',is_equal_to=part_id)[0])\n",
    "    istart = istart + len(group_sams)\n",
    "\n",
    "# set x-axis tick labels to show\n",
    "xticklabels = [0,200,400,600,800,1000,1200]\n",
    "\n",
    "#\n",
    "# Organize data for heatmap plotting\n",
    "#\n",
    "heatmap_df = pd.DataFrame(columns=wt_ds.peptide_table.loc[:,'Loc'].values.tolist())\n",
    "for sid in wt_ds.sample_id.values:\n",
    "    sam_ds  = wt_ds.loc[dict(sample_id=sid)]\n",
    "    enr     = sam_ds.enrichment.values.tolist()\n",
    "    heatmap_df.loc[sid] = enr\n",
    "\n",
    "heatmap_df = heatmap_df.reindex(ytickorder)  \n",
    "\n",
    "#\n",
    "# Plot heatmap\n",
    "#\n",
    "vmin=0\n",
    "vmax=40\n",
    "\n",
    "fig, ax = plt.subplots(figsize=[15,10])\n",
    "fig.patch.set_facecolor('white')\n",
    "cmap = sns.diverging_palette(240, 10, s=100, l=50, sep=10, as_cmap=True)\n",
    "sns_ax = sns.heatmap(heatmap_df, cmap=cmap, center=0, square=False, linewidths=0.0, vmin=vmin, vmax=vmax,\n",
    "                     cbar_kws={\"shrink\": 0.8, \"label\": \"Enrichment\", \"pad\": 0.02})\n",
    "\n",
    "# x-axis labeling\n",
    "ax.tick_params(axis=\"x\", labelsize=22, rotation=0)\n",
    "ax.set_xlabel('\\nAmino Acid Position', fontsize=24, verticalalignment='top', labelpad=20)\n",
    "ax.xaxis.set_major_locator(plt.FixedLocator(xticklabels))\n",
    "ax.set_xticklabels(xticklabels)\n",
    "plt.plot([14,14,685,685],[47.5,50.5,50.5,47.5], color='k', linewidth=2,clip_on=False)\n",
    "plt.plot([686,686,1211,1211],[47.5,50.5,50.5,47.5], color='k', linewidth=2,clip_on=False)\n",
    "ax.text(350, 49.2, 'S1', fontsize=28, color='k', ha='center', va='center')\n",
    "ax.text(949, 49.2, 'S2', fontsize=28, color='k', ha='center', va='center')\n",
    "\n",
    "# y-axis labeling\n",
    "ax.set_yticks([])\n",
    "ax.set_yticklabels([])\n",
    "plt.axhline(y=10, color='k', linestyle=':')\n",
    "ax.text(-35,  4.5, 'Infants', fontsize=28, color='k', ha='center', va='center', rotation=90)\n",
    "ax.text(-35, 28, 'Mothers', fontsize=28, color='k', ha='center', va='center', rotation=90)\n",
    "\n",
    "# color-bar settings\n",
    "sns_ax.figure.axes[-1].yaxis.label.set_size(24)\n",
    "sns_ax.collections[0].colorbar.ax.tick_params(labelsize=20)\n",
    "sns_ax.collections[0].colorbar.ax.set_yticklabels([0,5,10,15,20,25,30,35,f'$\\geq40$'])\n",
    "\n",
    "# labeling epitope regions\n",
    "ax.add_patch(Rectangle((1162,-1.8),(1204-1162),1.8,clip_on=False,fill=False))\n",
    "ax.text(1200, -5.2, 'HR2\\n1162-\\n 1204', fontsize=24, color='k', ha='center', va='center')\n",
    "ax.text(1100, -2.2, 'SH-H', fontsize=24, color='k', ha='center', va='center')\n",
    "ax.add_patch(Rectangle(( 910,-1.8),( 987- 910),1.8,clip_on=False,fill=False))\n",
    "ax.text(949, -5.2, 'HR1\\n910-\\n 987', fontsize=24, color='k', ha='center', va='center')\n",
    "ax.add_patch(Rectangle(( 816,-1.8),( 833- 816),1.8,clip_on=False,fill=False))\n",
    "ax.text(825, -5.2, 'FP\\n816-\\n 833', fontsize=24, color='k', ha='center', va='center')\n",
    "ax.add_patch(Rectangle(( 530,-1.8),( 681- 530),1.8,clip_on=False,fill=False))\n",
    "ax.text(605, -5.2, 'CTD\\n530-\\n 681', fontsize=24, color='k', ha='center', va='center')\n",
    "ax.add_patch(Rectangle(( 332,-1.8),( 523- 332),1.8,clip_on=False,fill=False))\n",
    "ax.text(426, -5.2, 'RBD\\n332-\\n 523', fontsize=24, color='k', ha='center', va='center')\n",
    "ax.add_patch(Rectangle((  29,-1.8),( 294-  29),1.8,clip_on=False,fill=False))\n",
    "ax.text(162, -5.2, 'NTD\\n29-\\n 294', fontsize=24, color='k', ha='center', va='center')\n",
    "ax.add_patch(Rectangle((1135,-1),(1170-1135),1,clip_on=False,fill=True,edgecolor='k',facecolor='gray'))\n",
    "ax.add_patch(Rectangle(( 805,-1),( 835- 805),1,clip_on=False,fill=True,edgecolor='k',facecolor='gray'))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{outdir}/LK_enrichments_short.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summed enrichments in epitope regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Organize participant IDs by mother/infant and HIV status\n",
    "\n",
    "HIV_pos_mothers = [\n",
    "      'ptnum_8', 'ptnum_25', 'ptnum_41', 'ptnum_79','ptnum_106','ptnum_111','ptnum_112','ptnum_115','ptnum_139','ptnum_155',\n",
    "    'ptnum_183','ptnum_185','ptnum_186','ptnum_189','ptnum_199','ptnum_200','ptnum_202','ptnum_203','ptnum_204','ptnum_205'\n",
    "]\n",
    "\n",
    "HIV_neg_mothers = [\n",
    "     'ptnum_18', 'ptnum_27', 'ptnum_50', 'ptnum_75','ptnum_101','ptnum_105','ptnum_132','ptnum_142','ptnum_146','ptnum_149',\n",
    "    'ptnum_166','ptnum_168','ptnum_169','ptnum_172','ptnum_194'\n",
    "]\n",
    "\n",
    "lk_mother_ds = ds.loc[dict(sample_id=lk_mother_sams)]\n",
    "HIV_pos_mother_sams = sample_id_coordinate_subset(lk_mother_ds,where='participant_ID',is_in=HIV_pos_mothers)\n",
    "HIV_neg_mother_sams = sample_id_coordinate_subset(lk_mother_ds,where='participant_ID',is_in=HIV_neg_mothers)\n",
    "\n",
    "\n",
    "HIV_pos_infants = [\n",
    "    'ptnum_8', 'ptnum_25', 'ptnum_41','ptnum_133','ptnum_185','ptnum_186'\n",
    "]\n",
    "\n",
    "HIV_neg_infants = [\n",
    "    'ptnum_18', 'ptnum_50', 'ptnum_142','ptnum_169'\n",
    "]\n",
    "\n",
    "lk_infant_ds = ds.loc[dict(sample_id=lk_infant_sams)]\n",
    "HIV_pos_infant_sams = sample_id_coordinate_subset(lk_infant_ds,where='participant_ID',is_in=HIV_pos_infants)\n",
    "HIV_neg_infant_sams = sample_id_coordinate_subset(lk_infant_ds,where='participant_ID',is_in=HIV_neg_infants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FP epitope\n",
      "SHH epitope\n"
     ]
    }
   ],
   "source": [
    "# Group summed enrichments by mother/infant and HIV status\n",
    "\n",
    "HIV_pos_mother_sums   = {}\n",
    "HIV_neg_mother_sums   = {}\n",
    "HIV_pos_infant_sums   = {}\n",
    "HIV_neg_infant_sums   = {}\n",
    "\n",
    "HIV_pos_mother_pid   = {}\n",
    "HIV_neg_mother_pid   = {}\n",
    "HIV_pos_infant_pid   = {}\n",
    "HIV_neg_infant_pid   = {}\n",
    "\n",
    "for region in ['FP','SHH']:\n",
    "    print(region,'epitope')\n",
    "    region_locs = [loc for loc in range(epitope_limits[region][0], epitope_limits[region][1]+1)]\n",
    "\n",
    "    HIV_pos_mother_sums[region]   = []\n",
    "    HIV_neg_mother_sums[region]   = []\n",
    "    HIV_pos_infant_sums[region]   = []\n",
    "    HIV_neg_infant_sums[region]   = []\n",
    "    \n",
    "    HIV_pos_mother_pid[region]   = []\n",
    "    HIV_neg_mother_pid[region]   = []\n",
    "    HIV_pos_infant_pid[region]   = []\n",
    "    HIV_neg_infant_pid[region]   = []\n",
    "    \n",
    "    group_sams_list = [HIV_pos_mother_sams, HIV_neg_mother_sams, HIV_pos_infant_sams, HIV_neg_infant_sams]\n",
    "    group_sums_list = [HIV_pos_mother_sums[region], HIV_neg_mother_sums[region],\n",
    "                       HIV_pos_infant_sums[region], HIV_neg_infant_sums[region]]\n",
    "    group_pid_list  = [HIV_pos_mother_pid[region], HIV_neg_mother_pid[region],\n",
    "                       HIV_pos_infant_pid[region], HIV_neg_infant_pid[region]]\n",
    "    for group_sams, group_sums, group_pid in zip(group_sams_list, group_sums_list, group_pid_list):\n",
    "        for sid in group_sams:\n",
    "            region_pids = peptide_id_coordinate_subset(wt_ds,where='Loc',is_in=region_locs)\n",
    "            enr = wt_ds.loc[dict(sample_id=sid, peptide_id=region_pids)].enrichment.values\n",
    "            pid = wt_ds.loc[dict(sample_id=sid)].sample_table.loc['participant_ID'].item()\n",
    "            group_sums.append(np.sum(enr))\n",
    "            group_pid.append(pid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write out summed enrichments in CSV, annotate with\n",
    "# participant ID, person type, HIV status, and epitope region\n",
    "\n",
    "sums_df = pd.DataFrame(columns=['participant_ID','person','status','region','enrichment'])\n",
    "for region in ['FP','SHH']:\n",
    "    for p,x in zip(HIV_pos_infant_pid[region], HIV_pos_infant_sums[region]):\n",
    "        sums_df.loc[len(sums_df)] = [p,'infant', 'positive', region, x]\n",
    "    for p,x in zip(HIV_neg_infant_pid[region], HIV_neg_infant_sums[region]):\n",
    "        sums_df.loc[len(sums_df)] = [p,'infant', 'negative', region, x]\n",
    "        \n",
    "    for p,x in zip(HIV_pos_mother_pid[region], HIV_pos_mother_sums[region]):\n",
    "        sums_df.loc[len(sums_df)] = [p,'mother', 'positive', region, x]\n",
    "    for p,x in zip(HIV_neg_mother_pid[region], HIV_neg_mother_sums[region]):\n",
    "        sums_df.loc[len(sums_df)] = [p,'mother', 'negative', region, x]\n",
    "        \n",
    "sums_df.to_csv(f'{outdir}/summed_enrichment.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FP infants vs mothers: pval = 0.014548205913238396\n",
      "SHH infants vs mothers: pval = 0.7953582759212796\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 567,
       "width": 712
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Plot summed enrichments for FP and SH-H epitopes\n",
    "#\n",
    "\n",
    "plot_data=[]\n",
    "for region in ['FP','SHH']:\n",
    "    for person in ['infant','mother']:\n",
    "        plot_data.append(sums_df[(sums_df['person']==person) & (sums_df['region']==region)]['enrichment'].to_numpy())\n",
    "\n",
    "# random jitter x-axis positions \n",
    "np.random.seed(25)\n",
    "infant_x = np.random.normal(1, 0.04, size=len(lk_infant_sams))\n",
    "mother_x = np.random.normal(2, 0.04, size=len(lk_mother_sams))\n",
    "\n",
    "\n",
    "x_data     = [infant_x, mother_x]\n",
    "box_colors = ['indigo', oi_skyblue]\n",
    "        \n",
    "linew=2\n",
    "fig = plt.figure(figsize=[10,8])\n",
    "for xvals, yvals, color, marker in zip([x_data[0], x_data[1], x_data[0]+2, x_data[1]+2],\n",
    "                                       plot_data,\n",
    "                                       box_colors+box_colors,\n",
    "                                       ['s','^','s','^']\n",
    "                                      ):\n",
    "    plt.scatter(xvals, yvals, color=color, marker=marker, alpha=0.6, s=100)\n",
    "\n",
    "boxes = plt.boxplot(plot_data,\n",
    "                    labels=['Infants', 'Mothers', 'Infants', 'Mothers'],\n",
    "                    sym='',\n",
    "                    patch_artist=True,\n",
    "                    #widths=0.35,\n",
    "                    boxprops=dict(linewidth=linew),\n",
    "                    whiskerprops=dict(linewidth=linew),\n",
    "                    capprops=dict(linewidth=linew),\n",
    "                    medianprops=dict(color='k', linewidth=linew, linestyle='--'),\n",
    "                    )\n",
    "for patch in boxes['boxes']:\n",
    "    patch.set_facecolor((0,0,0,0))\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.ylabel('Summed enrichment', fontsize=24, labelpad=20)\n",
    "plt.axvline(x=2.5, color='k', linestyle='--')\n",
    "\n",
    "basey = 850\n",
    "plt.ylim(ymax=basey+150)\n",
    "\n",
    "plt.text(1.5,basey+160,f'FP Epitope\\n{epitope_limits[\"FP\"][0]} - {epitope_limits[\"FP\"][1]}',\n",
    "         ha='center',va='bottom',fontsize=24)\n",
    "plt.text(3.5,basey+160,f'SH-H Epitope\\n{epitope_limits[\"SHH\"][0]} - {epitope_limits[\"SHH\"][1]}',\n",
    "         ha='center',va='bottom',fontsize=24)\n",
    "\n",
    "# p-values\n",
    "us1,up1 = st.mannwhitneyu(plot_data[0], plot_data[1], alternative='two-sided')\n",
    "print('FP infants vs mothers: pval =',up1)\n",
    "plt.plot([1.02,1.02,1.98,1.98], [basey,basey+20,basey+20,basey], c='black')\n",
    "plt.text(1.5,basey+30,f'p={round(up1,3)}',ha='center',va='bottom',fontsize=18)\n",
    "\n",
    "us2,up2 = st.mannwhitneyu(plot_data[2], plot_data[3], alternative='two-sided')\n",
    "print('SHH infants vs mothers: pval =',up2)\n",
    "plt.plot([3.02,3.02,3.98,3.98], [basey,basey+20,basey+20,basey], c='black')\n",
    "plt.text(3.5,basey+30,f'p={round(up2,3)}',ha='center',va='bottom',fontsize=18)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig(f\"{outdir}/FP_and_SHH_LK_boxplot.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}