From e0ed005723dc7fc14d7a5b4e21c6ead9f376e875 Mon Sep 17 00:00:00 2001
From: ibludau <isabell.bludau@gmail.com>
Date: Mon, 14 Mar 2022 23:22:10 +0100
Subject: [PATCH] feature: reformatting data for 3D alphamap visualization

---
 nbs/tutorial.ipynb         | 143 +++++++++++++++++++++++++------------
 structuremap/processing.py |  81 +++++++++++++++++++++
 2 files changed, 180 insertions(+), 44 deletions(-)

diff --git a/nbs/tutorial.ipynb b/nbs/tutorial.ipynb
index 6ee5cf9..21fa649 100644
--- a/nbs/tutorial.ipynb
+++ b/nbs/tutorial.ipynb
@@ -96,16 +96,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████| 12/12 [00:00<00:00, 12922.12it/s]"
+      "100%|█████████████████████████████████████████| 12/12 [00:00<00:00, 3549.23it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-02-22 19:23:45> Valid proteins: 0\n",
-      "2022-02-22 19:23:45> Invalid proteins: 0\n",
-      "2022-02-22 19:23:45> Existing proteins: 12\n"
+      "2022-03-10 09:16:30> Valid proteins: 0\n",
+      "2022-03-10 09:16:30> Invalid proteins: 0\n",
+      "2022-03-10 09:16:30> Existing proteins: 12\n"
      ]
     },
     {
@@ -131,16 +131,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████████| 12/12 [00:00<00:00, 8533.68it/s]"
+      "100%|█████████████████████████████████████████| 12/12 [00:00<00:00, 2500.58it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-02-22 19:23:45> Valid proteins: 0\n",
-      "2022-02-22 19:23:45> Invalid proteins: 0\n",
-      "2022-02-22 19:23:45> Existing proteins: 12\n"
+      "2022-03-10 09:16:30> Valid proteins: 0\n",
+      "2022-03-10 09:16:30> Invalid proteins: 0\n",
+      "2022-03-10 09:16:30> Existing proteins: 12\n"
      ]
     },
     {
@@ -174,7 +174,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████| 12/12 [00:02<00:00,  4.71it/s]\n"
+      "100%|██████████████████████████████████████| 2162/2162 [00:02<00:00, 850.74it/s]\n"
      ]
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    ],
@@ -355,7 +355,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████| 12/12 [00:03<00:00,  4.00it/s]\n"
+      "100%|███████████████████████████████████████████| 12/12 [00:03<00:00,  3.72it/s]\n"
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    ],
@@ -386,7 +386,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████| 12/12 [00:00<00:00, 60.22it/s]\n"
+      "100%|███████████████████████████████████████████| 12/12 [00:00<00:00, 68.08it/s]\n"
      ]
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    ],
@@ -434,7 +434,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████| 12/12 [00:00<00:00, 39.99it/s]\n"
+      "100%|███████████████████████████████████████████| 12/12 [00:00<00:00, 41.54it/s]\n"
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    ],
@@ -471,7 +471,7 @@
      "name": "stderr",
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      "text": [
-      "100%|██████████████████████████████████████████| 12/12 [00:00<00:00, 538.46it/s]\n"
+      "100%|██████████████████████████████████████████| 12/12 [00:00<00:00, 601.71it/s]\n"
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@@ -491,7 +491,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████| 12/12 [00:00<00:00, 30.45it/s]\n"
+      "100%|███████████████████████████████████████████| 12/12 [00:00<00:00, 28.79it/s]\n"
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@@ -2049,9 +2049,9 @@
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-      "100%|██████████████████████████████████████████| 12/12 [00:00<00:00, 117.79it/s]\n"
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-      "100%|███████████████████████████████████████████| 12/12 [00:00<00:00, 70.49it/s]\n"
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\"matches\": \"x\", \"title\": {\"text\": \"distance bin\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"showticklabels\": true, \"title\": {\"text\": \"Fraction of modified acceptors\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 1.0], \"showticklabels\": true}},                        {\"toImageButtonOptions\": {\"format\": \"svg\", \"filename\": \"Fraction of modified acceptor residues\"}, \"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('9d476917-d728-4293-a032-15bbcff89682');\n",
+       "var gd = document.getElementById('cf3601a6-af23-43dc-9e3e-f52bd4eefbf8');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -4959,6 +5005,7 @@
        "      <th>pvalue</th>\n",
        "      <th>variable</th>\n",
        "      <th>value</th>\n",
+       "      <th>variable_sig</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -4971,6 +5018,7 @@
        "      <td>0.000017</td>\n",
        "      <td>Observed</td>\n",
        "      <td>0.625000</td>\n",
+       "      <td>Observed (p &lt;= 0.01)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -4981,6 +5029,7 @@
        "      <td>0.000002</td>\n",
        "      <td>Observed</td>\n",
        "      <td>0.498371</td>\n",
+       "      <td>Observed (p &lt;= 0.01)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -4991,16 +5040,22 @@
        "      <td>0.000002</td>\n",
        "      <td>Observed</td>\n",
        "      <td>0.423792</td>\n",
+       "      <td>Observed (p &lt;= 0.01)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  context ptm_types  cutoff  std_random_fraction    pvalue  variable     value\n",
-       "0      3D         p       6                  0.0  0.000017  Observed  0.625000\n",
-       "1      3D         p      11                  0.0  0.000002  Observed  0.498371\n",
-       "2      3D         p      16                  0.0  0.000002  Observed  0.423792"
+       "  context ptm_types  cutoff  std_random_fraction    pvalue  variable  \\\n",
+       "0      3D         p       6                  0.0  0.000017  Observed   \n",
+       "1      3D         p      11                  0.0  0.000002  Observed   \n",
+       "2      3D         p      16                  0.0  0.000002  Observed   \n",
+       "\n",
+       "      value          variable_sig  \n",
+       "0  0.625000  Observed (p <= 0.01)  \n",
+       "1  0.498371  Observed (p <= 0.01)  \n",
+       "2  0.423792  Observed (p <= 0.01)  "
       ]
      },
      "execution_count": 37,
@@ -5028,7 +5083,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████| 12/12 [00:13<00:00,  1.16s/it]\n"
+      "100%|███████████████████████████████████████████| 12/12 [00:14<00:00,  1.19s/it]\n"
      ]
     }
    ],
diff --git a/structuremap/processing.py b/structuremap/processing.py
index 688be85..8e19d79 100644
--- a/structuremap/processing.py
+++ b/structuremap/processing.py
@@ -1781,3 +1781,84 @@ def extract_motifs_in_proteome(
         'motif_end': end_list,
         'sequence_window': sequence_window_list})
     return motif_res
+
+
+def import_ptms_for_structuremap(
+    file: str,
+    organism: str
+) -> pd.DataFrame:
+    """
+    Function to import PTM datasets.
+
+    Parameters
+    ----------
+    file : str
+        Path to the PTM dataset to load.
+        This can be processed by AlphaPept, Spectronaut, MaxQuant, DIA-NN or
+        FragPipe.
+    organism : str
+        Organism for which a fasta file should be imported.
+
+    Returns
+    -------
+    : pd.DataFrame
+        Dataframe with PTM information. It contains following columns:
+        protein_id: a unique UniProt identifier;
+        AA: the one letter amino acid abbreviation of the PTM acceptor;
+        position: the sequence position of the PTM acceptor
+        (the first amino acid has position 1);
+        <PTM types>: N columns for N different PTM types where 1 indicates that
+        the PTM is present at the given amino acid postition
+        and 0 indicates no modification
+    """
+    try:
+        from alphamap.organisms_data import import_fasta
+        from alphamap.importing import import_data
+        from alphamap.preprocessing import format_input_data
+    except ModuleNotFoundError:
+        raise ModuleNotFoundError(f"Please install alphamap. Subsequently install pandas==1.4.0.")
+    fasta_in = import_fasta('Human')
+    df = import_data(file)
+    df = format_input_data(df=df,
+                           fasta=fasta_in,
+                           modification_exp=r'\[.*?\]')
+    ptm_df = df.explode(['PTMsites', 'PTMtypes'])
+    ptm_df = ptm_df.dropna(subset=['PTMsites', 'PTMtypes'])
+    ptm_df = ptm_df.astype({'PTMsites': 'int32'})
+    ptm_df["AA"] = ptm_df.apply(
+        lambda x: x["naked_sequence"][x["PTMsites"]],
+        axis=1)
+    ptm_df["position"] = ptm_df.apply(
+        lambda x: x["start"]+x["PTMsites"]+1,
+        axis=1)
+    ptm_df = ptm_df[["unique_protein_id", "AA", "position", "PTMtypes"]]
+    ptm_df = pd.get_dummies(
+        ptm_df, prefix="", prefix_sep='', columns=["PTMtypes"])
+    ptm_df = ptm_df.rename(columns={"unique_protein_id": "protein_id"})
+    ptm_df = ptm_df.groupby(['protein_id', 'AA', 'position'])
+    ptm_df = ptm_df.max()
+    ptm_df = ptm_df.reset_index()
+    ptm_df = ptm_df.drop_duplicates()
+    ptm_df = ptm_df.reset_index(drop=True)
+    return ptm_df
+
+
+def format_for_3Dviz(
+    df: pd.DataFrame,
+    ptm_dataset: str
+) -> pd.DataFrame:
+    df_mod = df[["protein_id", "AA", "position", ptm_dataset]]
+    df_mod = df_mod.rename(columns={"protein_id": "unique_protein_id",
+                                    "AA": "modified_sequence",
+                                    "position": "start"})
+    df_mod["modified_sequence"] = [mod+"_"+str(i) for i,mod in enumerate(df_mod["modified_sequence"])]
+    df_mod["all_protein_ids"] = df_mod["unique_protein_id"]
+    df_mod["PTMsites"] = 0
+    df_mod["start"] = df_mod["start"]-1
+    df_mod["end"] = df_mod["start"]
+    df_mod["PTMsites"] = [[i] for i in df_mod["PTMsites"]]
+    df_mod = df_mod[df_mod[ptm_dataset] == 1]
+    df_mod["marker_symbol"] = 1
+    df_mod["PTMtypes"] = [[ptm_dataset] for i in df_mod["PTMsites"]]
+    df_mod = df_mod.dropna(subset=['PTMtypes']).reset_index(drop=True)
+    return df_mod