From 7450654c5a2d8d740ef0f196e2e5bfda69f9f7e7 Mon Sep 17 00:00:00 2001
From: Niko Papadopoulos <nikolaos.papadopoulos@embl.de>
Date: Thu, 12 Jan 2023 10:47:38 +0100
Subject: [PATCH] finally removing Untitled.ipynb

---
 analysis/Untitled.ipynb                       | 898 ------------------
 ...vision-single_cell_DEG_with_profiles.ipynb |  14 +-
 2 files changed, 7 insertions(+), 905 deletions(-)
 delete mode 100644 analysis/Untitled.ipynb

diff --git a/analysis/Untitled.ipynb b/analysis/Untitled.ipynb
deleted file mode 100644
index 2267673..0000000
--- a/analysis/Untitled.ipynb
+++ /dev/null
@@ -1,898 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "273c6a8d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-07-29 15:06\n"
-     ]
-    }
-   ],
-   "source": [
-    "from datetime import datetime, timezone\n",
-    "import pytz\n",
-    "\n",
-    "utc_dt = datetime.now(timezone.utc) # UTC time\n",
-    "dt = utc_dt.astimezone()\n",
-    "tz = pytz.timezone('Europe/Berlin')\n",
-    "berlin_now = datetime.now(tz)\n",
-    "print(f'{berlin_now:%Y-%m-%d %H:%M}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "09defaef-09b9-4d8e-84a1-dc3c7a56b80a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "from os.path import exists\n",
-    "from tqdm import tqdm\n",
-    "import requests\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8aaa0695",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "structural_annotation = pd.read_parquet('../old_data/structure_annotation.parquet')\n",
-    "sequence_annotation = pd.read_csv('../old_data/Slacustris_eggnog.tsv', sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "95457088",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mmseqs = pd.read_csv('../data/cfres_mmseqs_s75_e1.m8', sep=\"\\s+\", header=None)\n",
-    "mmseqs.columns = [\"query\", \"target\", \"seq. id.\", \"alignment length\", \"no. mismatches\",\n",
-    "                       \"no. gap open\", \"query start\", \"query end\", \"target start\", \"target end\",\n",
-    "                       \"e value\", \"bit score\"]\n",
-    "\n",
-    "mmseqs['gene_id'] = mmseqs['query'].str.split('_').str[:2].apply(lambda x: '_'.join(x))\n",
-    "mmseqs['normalized bit score'] = mmseqs['bit score'] / mmseqs['alignment length']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "093bff38",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def best_bit_score(df, sort_by='normalized bit score', tiebreak='alignment length'):\n",
-    "    have_max = df[sort_by] == np.max(df[sort_by])\n",
-    "    max_ali = df[have_max][tiebreak] == np.max(df[have_max][tiebreak])\n",
-    "    return df[have_max][max_ali].index.values[0]\n",
-    "\n",
-    "def keep_best(df, groupby='gene_id'):\n",
-    "    df.reset_index(inplace=True)\n",
-    "    idx = df.groupby(groupby).apply(best_bit_score)\n",
-    "    res = df.loc[idx].copy()\n",
-    "    return res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "042ff08e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mmseqs_filtered = keep_best(mmseqs)\n",
-    "del mmseqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9645fec8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hhblits = pd.read_csv('../data/cfres_hhblits_e1.m8', sep=\"\\s+\", header=None)\n",
-    "hhblits.columns = [\"query\", \"target\", \"seq. id.\", \"alignment length\", \"no. mismatches\",\n",
-    "                       \"no. gap open\", \"query start\", \"query end\", \"target start\", \"target end\",\n",
-    "                       \"e value\", \"bit score\"]\n",
-    "\n",
-    "hhblits['gene_id'] = hhblits['query'].str.split('_').str[:2].apply(lambda x: '_'.join(x))\n",
-    "hhblits['normalized bit score'] = hhblits['bit score'] / hhblits['alignment length']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7e9d0601",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hhblits_filtered = keep_best(hhblits)\n",
-    "del hhblits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "da58c74f-5cd2-435a-8281-6195ed7f15af",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mmseqs_filtered.drop(columns=['index'], inplace=True)\n",
-    "hhblits_filtered.drop(columns=['index'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "260c26ec",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['evalue', 'score', 'eggNOG_OGs', 'max_annot_lvl', 'COG_category',\n",
-       "       'Description', 'Preferred_name', 'GOs', 'PFAMs', 'gene_id',\n",
-       "       'protein_id'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence_annotation.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c92b4a01",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "structural_annotation['normalized bit score'] = structural_annotation['bit score'] / structural_annotation['alignment length']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "5ceaaff2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f7975465070>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.hist(mmseqs_filtered['normalized bit score'], bins=100, label='mmseqs', alpha=0.3, density=True);\n",
-    "ax.hist(hhblits_filtered['normalized bit score'], bins=100, label='hhblits', alpha=0.3, density=True);\n",
-    "ax.hist(structural_annotation['normalized bit score'], bins=100, label='coffe', alpha=0.3, density=True);\n",
-    "ax.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "7649fb1a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unique_up_id = pd.concat([hhblits_filtered['target'].drop_duplicates(),\n",
-    "                          mmseqs_filtered['target'].drop_duplicates()])\n",
-    "unique_up_id.drop_duplicates(inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "e8d0cfb7-c86d-41a6-99e7-d514bc1f3873",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "148"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "request_size = 500\n",
-    "no_chunks = np.ceil((len(unique_up_id) / request_size) * 3).astype(int)\n",
-    "no_chunks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "85231f72-12ad-4cc7-897a-544e0be27228",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 148/148 [01:45<00:00,  1.41it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "request_size = 500\n",
-    "no_chunks = np.ceil((len(unique_up_id) / request_size) * 3).astype(int)\n",
-    "\n",
-    "with open('../data/profile_sequences.fasta', 'w') as result:\n",
-    "    for i in tqdm(range(no_chunks)):\n",
-    "        a = (i * request_size) // 3\n",
-    "        b = ((i+1) * request_size) // 3\n",
-    "        chunk = unique_up_id[a:b]\n",
-    "        bait50 = ['UniRef50_' + c for c in chunk]\n",
-    "        bait90 = ['UniRef90_' + c for c in chunk]\n",
-    "        bait100 = ['UniRef100_' + c for c in chunk]\n",
-    "        expanded_chunk = bait50 + bait90 + bait100\n",
-    "        url = f\"https://rest.uniprot.org/uniref/ids?ids={','.join(expanded_chunk)}&format=fasta\"\n",
-    "        response = requests.get(url)\n",
-    "        result.write(response.text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3d17ee4-6176-443a-b320-a54aaa7ff8cd",
-   "metadata": {},
-   "source": [
-    "(wait for EggNOG-mapper to annotate the sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "eb0c48d8-ef71-440f-8ea0-6e7f3118f957",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file = '/g/arendt/npapadop/data/spongfold_publish/MM_ffr33uy6.emapper.annotations.tsv'\n",
-    "sensitive = pd.read_csv(file, sep='\\t', skiprows=4, skipfooter=3, engine='python')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "0a97dfb7-4783-44fa-a330-f07b0585fcf8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test = sensitive['#query'].str.split('_').str[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "942d4a2d-2a8b-44ff-89b4-8525f3ad926e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "A0A6A6LEY7    3\n",
-       "A0A1X7U3S9    3\n",
-       "A0A3N5U8U0    3\n",
-       "A0A6J0B743    3\n",
-       "A0A482W475    3\n",
-       "             ..\n",
-       "A0A6I8SCD7    1\n",
-       "A0A1A8MN43    1\n",
-       "A0A0C2FDV2    1\n",
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-       "Name: #query, Length: 20360, dtype: int64"
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-    "test.value_counts()"
-   ]
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-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "909804dd-4006-4dcc-a4ba-308a5b3975d0",
-   "metadata": {},
-   "outputs": [
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-     "data": {
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>#query</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>seed_ortholog</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>evalue</th>\n",
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-       "      <th>score</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eggNOG_OGs</th>\n",
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-       "      <th>max_annot_lvl</th>\n",
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-       "    <tr>\n",
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-       "    <tr>\n",
-       "      <th>Description</th>\n",
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-       "    <tr>\n",
-       "      <th>Preferred_name</th>\n",
-       "    </tr>\n",
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-       "      <th>GOs</th>\n",
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-       "    <tr>\n",
-       "      <th>EC</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KEGG_ko</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KEGG_Pathway</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KEGG_Module</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KEGG_Reaction</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KEGG_rclass</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BRITE</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KEGG_TC</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAZy</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BiGG_Reaction</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>PFAMs</th>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
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-       "Empty DataFrame\n",
-       "Columns: []\n",
-       "Index: [#query, seed_ortholog, evalue, score, eggNOG_OGs, max_annot_lvl, COG_category, Description, Preferred_name, GOs, EC, KEGG_ko, KEGG_Pathway, KEGG_Module, KEGG_Reaction, KEGG_rclass, BRITE, KEGG_TC, CAZy, BiGG_Reaction, PFAMs]"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "sensitive[sensitive['#query'].str.contains('UPI00005B2EF3')].T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e6d1eb54-d942-465e-a868-517d442fe1dd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "1c156c27-d2ec-47d6-b764-2b54816922a0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sensitive['uniprot'] = sensitive['#query'].str.split('_').str[1]\n",
-    "sensitive.reset_index(drop=True, inplace=True)\n",
-    "# remove unnecessary columns\n",
-    "dead_weight = ['#query', 'seed_ortholog', 'EC', 'KEGG_ko', 'KEGG_Pathway',\n",
-    "               'KEGG_Module', 'KEGG_Reaction', 'KEGG_rclass', 'BRITE',\n",
-    "               'KEGG_TC', 'CAZy', 'BiGG_Reaction']\n",
-    "sensitive.drop(dead_weight, axis=1, inplace=True)\n",
-    "# convert rest to categorical to save space\n",
-    "to_categorical = ['uniprot', 'eggNOG_OGs', 'max_annot_lvl', 'COG_category',\n",
-    "                  'Description', 'Preferred_name', 'GOs', 'PFAMs']\n",
-    "sensitive[to_categorical] = sensitive[to_categorical].astype(\"category\")\n",
-    "# finally save in parquet format\n",
-    "sensitive.drop_duplicates(inplace=True)\n",
-    "sensitive.to_parquet('../data/uniprot_profiles.parquet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "d68690ea-060a-480f-802c-aac6baae570d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "(20360, 10)"
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-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
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-    "sensitive.shape"
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-   "execution_count": 33,
-   "id": "f60ac7bf-f031-4bd6-99f3-eb3117b50cf1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7417224       A0A409VGI9\n",
-       "7453017       A0A6A6LEY7\n",
-       "3711344       A0A1A8QDB6\n",
-       "5432158       A0A1X7UJF6\n",
-       "1658532           S9X2M4\n",
-       "               ...      \n",
-       "144364        A0A2F0BPH8\n",
-       "144949            L1J317\n",
-       "4535244       A0A0K0D6S2\n",
-       "2883206    UPI0003F0A19A\n",
-       "2883636       A0A7S2RZY7\n",
-       "Name: target, Length: 24540, dtype: object"
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-    "unique_up_id"
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-   "execution_count": 35,
-   "id": "fd08dceb-c307-4ad6-bd23-91b70aaf7ef4",
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-   "outputs": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>query</th>\n",
-       "      <th>target</th>\n",
-       "      <th>seq. id.</th>\n",
-       "      <th>alignment length</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>3537669</th>\n",
-       "      <td>c100005_g1_i4_m.41851</td>\n",
-       "      <td>UPI000C6D6B72</td>\n",
-       "      <td>0.262</td>\n",
-       "      <td>138</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>1.464000e-04</td>\n",
-       "      <td>53</td>\n",
-       "      <td>c100005_g1</td>\n",
-       "      <td>0.384058</td>\n",
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-       "    <tr>\n",
-       "      <th>3711020</th>\n",
-       "      <td>c100012_g4_i1_m.41921</td>\n",
-       "      <td>A0A4V1IVK5</td>\n",
-       "      <td>0.681</td>\n",
-       "      <td>133</td>\n",
-       "      <td>41</td>\n",
-       "      <td>0</td>\n",
-       "      <td>32</td>\n",
-       "      <td>164</td>\n",
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-       "      <td>3.106000e-49</td>\n",
-       "      <td>183</td>\n",
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-       "      <td>1.375940</td>\n",
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-       "      <th>3108519</th>\n",
-       "      <td>c100014_g2_i1_m.41927</td>\n",
-       "      <td>A0A1D1W000</td>\n",
-       "      <td>0.456</td>\n",
-       "      <td>59</td>\n",
-       "      <td>32</td>\n",
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-       "      <td>4</td>\n",
-       "      <td>62</td>\n",
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-       "      <td>4.196000e-04</td>\n",
-       "      <td>52</td>\n",
-       "      <td>c100014_g2</td>\n",
-       "      <td>0.881356</td>\n",
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-       "    <tr>\n",
-       "      <th>7530038</th>\n",
-       "      <td>c100023_g1_i1_m.41965</td>\n",
-       "      <td>UPI0009E61F51</td>\n",
-       "      <td>0.342</td>\n",
-       "      <td>90</td>\n",
-       "      <td>57</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>92</td>\n",
-       "      <td>2743</td>\n",
-       "      <td>2829</td>\n",
-       "      <td>4.556000e-04</td>\n",
-       "      <td>53</td>\n",
-       "      <td>c100023_g1</td>\n",
-       "      <td>0.588889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5277692</th>\n",
-       "      <td>c100036_g1_i4_m.42040</td>\n",
-       "      <td>A0A7S3BMQ4</td>\n",
-       "      <td>0.523</td>\n",
-       "      <td>99</td>\n",
-       "      <td>46</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71</td>\n",
-       "      <td>169</td>\n",
-       "      <td>1</td>\n",
-       "      <td>98</td>\n",
-       "      <td>2.166000e-19</td>\n",
-       "      <td>100</td>\n",
-       "      <td>c100036_g1</td>\n",
-       "      <td>1.010101</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8219227</th>\n",
-       "      <td>c99972_g1_i2_m.41694</td>\n",
-       "      <td>UPI0009E28D88</td>\n",
-       "      <td>0.466</td>\n",
-       "      <td>63</td>\n",
-       "      <td>33</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>70</td>\n",
-       "      <td>392</td>\n",
-       "      <td>453</td>\n",
-       "      <td>1.130000e-05</td>\n",
-       "      <td>57</td>\n",
-       "      <td>c99972_g1</td>\n",
-       "      <td>0.904762</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6087296</th>\n",
-       "      <td>c99980_g3_i1_m.41746</td>\n",
-       "      <td>UPI00005B2EF3</td>\n",
-       "      <td>0.434</td>\n",
-       "      <td>62</td>\n",
-       "      <td>35</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>63</td>\n",
-       "      <td>7</td>\n",
-       "      <td>68</td>\n",
-       "      <td>4.330000e-05</td>\n",
-       "      <td>51</td>\n",
-       "      <td>c99980_g3</td>\n",
-       "      <td>0.822581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3238079</th>\n",
-       "      <td>c99987_g1_i2_m.41772</td>\n",
-       "      <td>A0A1X7URN4</td>\n",
-       "      <td>0.529</td>\n",
-       "      <td>220</td>\n",
-       "      <td>97</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>221</td>\n",
-       "      <td>37</td>\n",
-       "      <td>243</td>\n",
-       "      <td>1.868000e-61</td>\n",
-       "      <td>221</td>\n",
-       "      <td>c99987_g1</td>\n",
-       "      <td>1.004545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>574005</th>\n",
-       "      <td>c99993_g2_i1_m.41786</td>\n",
-       "      <td>UPI00177B4952</td>\n",
-       "      <td>0.663</td>\n",
-       "      <td>137</td>\n",
-       "      <td>41</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>137</td>\n",
-       "      <td>23</td>\n",
-       "      <td>145</td>\n",
-       "      <td>1.384000e-49</td>\n",
-       "      <td>183</td>\n",
-       "      <td>c99993_g2</td>\n",
-       "      <td>1.335766</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>574028</th>\n",
-       "      <td>c99993_g3_i1_m.41790</td>\n",
-       "      <td>UPI00106CF215</td>\n",
-       "      <td>0.265</td>\n",
-       "      <td>133</td>\n",
-       "      <td>95</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>135</td>\n",
-       "      <td>93</td>\n",
-       "      <td>222</td>\n",
-       "      <td>5.938000e-04</td>\n",
-       "      <td>52</td>\n",
-       "      <td>c99993_g3</td>\n",
-       "      <td>0.390977</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3347 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         query         target  seq. id.  alignment length  \\\n",
-       "3537669  c100005_g1_i4_m.41851  UPI000C6D6B72     0.262               138   \n",
-       "3711020  c100012_g4_i1_m.41921     A0A4V1IVK5     0.681               133   \n",
-       "3108519  c100014_g2_i1_m.41927     A0A1D1W000     0.456                59   \n",
-       "7530038  c100023_g1_i1_m.41965  UPI0009E61F51     0.342                90   \n",
-       "5277692  c100036_g1_i4_m.42040     A0A7S3BMQ4     0.523                99   \n",
-       "...                        ...            ...       ...               ...   \n",
-       "8219227   c99972_g1_i2_m.41694  UPI0009E28D88     0.466                63   \n",
-       "6087296   c99980_g3_i1_m.41746  UPI00005B2EF3     0.434                62   \n",
-       "3238079   c99987_g1_i2_m.41772     A0A1X7URN4     0.529               220   \n",
-       "574005    c99993_g2_i1_m.41786  UPI00177B4952     0.663               137   \n",
-       "574028    c99993_g3_i1_m.41790  UPI00106CF215     0.265               133   \n",
-       "\n",
-       "         no. mismatches  no. gap open  query start  query end  target start  \\\n",
-       "3537669             100             0           13        150             7   \n",
-       "3711020              41             0           32        164            73   \n",
-       "3108519              32             0            4         62           118   \n",
-       "7530038              57             0            3         92          2743   \n",
-       "5277692              46             0           71        169             1   \n",
-       "...                 ...           ...          ...        ...           ...   \n",
-       "8219227              33             0            8         70           392   \n",
-       "6087296              35             0            2         63             7   \n",
-       "3238079              97             0            2        221            37   \n",
-       "574005               41             0            1        137            23   \n",
-       "574028               95             0            3        135            93   \n",
-       "\n",
-       "         target end       e value  bit score     gene_id  normalized bit score  \n",
-       "3537669         142  1.464000e-04         53  c100005_g1              0.384058  \n",
-       "3711020         203  3.106000e-49        183  c100012_g4              1.375940  \n",
-       "3108519         176  4.196000e-04         52  c100014_g2              0.881356  \n",
-       "7530038        2829  4.556000e-04         53  c100023_g1              0.588889  \n",
-       "5277692          98  2.166000e-19        100  c100036_g1              1.010101  \n",
-       "...             ...           ...        ...         ...                   ...  \n",
-       "8219227         453  1.130000e-05         57   c99972_g1              0.904762  \n",
-       "6087296          68  4.330000e-05         51   c99980_g3              0.822581  \n",
-       "3238079         243  1.868000e-61        221   c99987_g1              1.004545  \n",
-       "574005          145  1.384000e-49        183   c99993_g2              1.335766  \n",
-       "574028          222  5.938000e-04         52   c99993_g3              0.390977  \n",
-       "\n",
-       "[3347 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hhblits_filtered[~hhblits_filtered['target'].isin(sensitive['uniprot'])]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c0f35e75-1a3f-4bab-8afe-160d3347738f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mmseqs_filtered"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f99bd75f-ae5a-4d00-8a01-5588c711f45c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "foldseek_full = pd.read_parquet('../old_data/fs_targets.parquet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1359934-a10c-4301-9ffa-000acb4159e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mmseqs_filtered = mmseqs_filtered.merge(sensitive, on='uniprot', how='left')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "72eed180-8cb0-4f76-b316-f5717f56b026",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "hhblits = hhblits.merge(sensitive, on='uniprot', how='left')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e5b356e4-10f9-4782-a39c-4f19a221ebdb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/analysis/revision-single_cell_DEG_with_profiles.ipynb b/analysis/revision-single_cell_DEG_with_profiles.ipynb
index c5f394e..fc17e5b 100644
--- a/analysis/revision-single_cell_DEG_with_profiles.ipynb
+++ b/analysis/revision-single_cell_DEG_with_profiles.ipynb
@@ -12,7 +12,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-01-12 09:33:39.395283+01:00\n"
+      "2023-01-12 09:48:46.359533+01:00\n"
      ]
     }
    ],
@@ -107,7 +107,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/md/d6lwwbv97xb6g6ddypntnprh0000gp/T/ipykernel_20464/3664721023.py:5: DtypeWarning: Columns (6,7,29) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "/var/folders/md/d6lwwbv97xb6g6ddypntnprh0000gp/T/ipykernel_21230/3664721023.py:5: DtypeWarning: Columns (6,7,29) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  annot = pd.read_csv(\"../data/revision/spongilla_lut.tsv\", sep='\\t')\n"
      ]
     }
@@ -541,7 +541,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x28b10cf10>"
+       "<matplotlib.legend.Legend at 0x16d033ac0>"
       ]
      },
      "execution_count": 22,
@@ -839,12 +839,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "id": "5c63c05d-502d-4137-b913-51a263649a2a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "melted = melted.append({'level': 'whole organism', 'variable': 'standard mapper', 'value': 17990/41943}, ignore_index=True)\n",
+    "melted = melted.append({'level': 'whole organism', 'variable': 'standard emapper', 'value': 17990/41943}, ignore_index=True)\n",
     "melted = melted.append({'level': 'whole organism', 'variable': 'MorF', 'value': 25232/41943}, ignore_index=True)\n",
     "melted = melted.append({'level': 'whole organism', 'variable': 'emapper + BLASTp', 'value': (17990 + 682)/41943}, ignore_index=True)\n",
     "melted = melted.append({'level': 'whole organism', 'variable': 'emapper-hmmer', 'value': (28897-2473)/41943}, ignore_index=True)\n",
@@ -855,13 +855,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "id": "e96a39fb-13c3-44b2-81b0-f4f8cc4d74c0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 320x320 with 1 Axes>"
       ]
-- 
GitLab