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Bowen999
/
lipids_ms2

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Bowen999 commited on
Commit
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·
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1 Parent(s): 9b57e6f

Refresh entire dataset

Browse files
Files changed (7) hide show
  1. .DS_Store +0 -0
  2. .ipynb_checkpoints/Untitled-checkpoint.ipynb +493 -0
  3. README.md +1 -6
  4. test_set.csv +2 -2
  5. test_set_no_sn.csv +3 -0
  6. train_set.csv +3 -0
  7. train_set_no_sn.csv +3 -0
.DS_Store CHANGED
Binary files a/.DS_Store and b/.DS_Store differ
 
.ipynb_checkpoints/Untitled-checkpoint.ipynb ADDED
@@ -0,0 +1,493 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "id": "5613d246",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "/var/folders/q1/mfxy01nj293b7bbnnhjkt_6w0000gn/T/ipykernel_18006/1302780579.py:3: DtypeWarning: Columns (6,9,20,21,22,23,26,30,32) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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+ " train_df = pd.read_csv('training_set.csv')\n"
15
+ ]
16
+ }
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+ ],
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+ "source": [
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+ "import pandas as pd\n",
20
+ "import numpy as np\n",
21
+ "train_df = pd.read_csv('training_set.csv')\n",
22
+ "\n",
23
+ "# Identify columns that start with 'extra'\n",
24
+ "extra_cols = [col for col in train_df.columns if col.startswith('extra')]\n",
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+ "\n",
26
+ "# Create a mask where all 'extra' columns are either empty or None\n",
27
+ "mask = (train_df[extra_cols].apply(lambda x: x.isna() | (x == ''), axis=1)).all(axis=1)\n",
28
+ "\n",
29
+ "# Keep only rows where the mask is True (all extra columns are empty/None)\n",
30
+ "train_df = train_df[mask]"
31
+ ]
32
+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "id": "234f9b68",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ " <th>extra_1</th>\n",
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+ " <th>num_c_2</th>\n",
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+ " <th>num_db_2</th>\n",
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+ " <th>extra_2</th>\n",
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+ " <th>...</th>\n",
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+ " <th>retention_time</th>\n",
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+ " <th>lib_quality</th>\n",
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+ " <th>source</th>\n",
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+ " <th>spectrum_id</th>\n",
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+ " <th>ms_level</th>\n",
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+ " <th>instrument</th>\n",
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+ " <th>energy</th>\n",
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+ " <th>num_peaks</th>\n",
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+ " <th>MS2</th>\n",
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+ " <th>MS2_norm</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>BMP 2:0_2:0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>3</td>\n",
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+ " <td>[[117.0546, 1998], [159.0663, 50], [348.1054, ...</td>\n",
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+ " <td>[[117.0546, 100.0], [159.0663, 2.5025025025025...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>BMP 2:0_3:0</td>\n",
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+ " <td>BMP 5:0</td>\n",
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+ " <td>BMP</td>\n",
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+ " <td>0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>[[117.0546, 100.0], [131.0703, 100.0], [173.08...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>BMP 2:0_4:0</td>\n",
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+ " <td>BMP 6:0</td>\n",
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+ " <td>BMP</td>\n",
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+ " <td>2:0_4:0</td>\n",
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+ " <td>2</td>\n",
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+ " <td>0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>4</td>\n",
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+ " <td>0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>...</td>\n",
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+ " <td>0.0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>MS-DIAL</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>4</td>\n",
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+ " <td>[[117.0546, 999], [145.0859, 999], [187.0976, ...</td>\n",
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+ " <td>[[117.0546, 100.0], [145.0859, 100.0], [187.09...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>BMP 2:0_5:0</td>\n",
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+ " <td>BMP 7:0</td>\n",
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+ " <td>BMP</td>\n",
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+ " <td>2:0_5:0</td>\n",
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+ " <td>2</td>\n",
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+ " <td>0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>5</td>\n",
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+ " <td>...</td>\n",
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+ " <td>0.0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>MS-DIAL</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>4</td>\n",
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+ " <td>[[117.0546, 999], [159.1016, 999], [201.1132, ...</td>\n",
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+ " <td>[[117.0546, 100.0], [159.1016, 100.0], [201.11...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
182
+ " <td>BMP 2:0_6:0</td>\n",
183
+ " <td>BMP 8:0</td>\n",
184
+ " <td>BMP</td>\n",
185
+ " <td>2:0_6:0</td>\n",
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+ " <td>2</td>\n",
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+ " <td>0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>6</td>\n",
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+ " <td>0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>...</td>\n",
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+ " <td>0.0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>MS-DIAL</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>4</td>\n",
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+ " <td>[[117.0546, 999], [173.1172, 999], [215.1289, ...</td>\n",
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+ " <td>[[117.0546, 100.0], [173.1172, 100.0], [215.12...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
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+ " <td>...</td>\n",
227
+ " </tr>\n",
228
+ " <tr>\n",
229
+ " <th>959026</th>\n",
230
+ " <td>PS 18:0_16:1</td>\n",
231
+ " <td>PS 34:1</td>\n",
232
+ " <td>PS</td>\n",
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+ " <td>18:0_16:1</td>\n",
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+ " <td>18</td>\n",
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+ " <td>0</td>\n",
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+ " <td>IOBA-NHC</td>\n",
244
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+ " <td>NaN</td>\n",
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+ " <td>[[152.993195, 59.0], [153.990814, 19.0], [252....</td>\n",
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+ " <td>[[152.993195, 14.192712533326324], [153.990814...</td>\n",
251
+ " </tr>\n",
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+ " <tr>\n",
253
+ " <th>959027</th>\n",
254
+ " <td>PS 18:0_22:5</td>\n",
255
+ " <td>PS 40:5</td>\n",
256
+ " <td>PS</td>\n",
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+ " <td>18:0_22:5</td>\n",
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+ " <td>18</td>\n",
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+ " <td>0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>22</td>\n",
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+ " <td>5</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>...</td>\n",
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+ " <td>3.0</td>\n",
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+ " <td>IOBA-NHC</td>\n",
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+ " <td>CCMSLIB00005724256</td>\n",
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+ " <td>2.0</td>\n",
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+ " <td>LC-ESI-qTof</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>53</td>\n",
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+ " <td>[[108.166229, 17.0], [152.996857, 103.912659],...</td>\n",
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+ " <td>[[108.166229, 4.017240816041031], [152.996857,...</td>\n",
275
+ " </tr>\n",
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+ " <tr>\n",
277
+ " <th>959028</th>\n",
278
+ " <td>PS 18:1_18:1</td>\n",
279
+ " <td>PS 36:2</td>\n",
280
+ " <td>PS</td>\n",
281
+ " <td>18:1_18:1</td>\n",
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+ " <td>18</td>\n",
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+ " <td>1</td>\n",
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+ " <td>3.0</td>\n",
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+ " <td>IOBA-NHC</td>\n",
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+ " <td>CCMSLIB00005724257</td>\n",
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+ " <td>2.0</td>\n",
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+ " <td>LC-ESI-qTof</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>29</td>\n",
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+ " <td>[[152.989365, 51.677917], [153.000549, 101.095...</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>959029</th>\n",
302
+ " <td>PS 18:1_22:0</td>\n",
303
+ " <td>PS 40:1</td>\n",
304
+ " <td>PS</td>\n",
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+ " <td>18:1_22:0</td>\n",
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+ " <td>18</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>...</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>3.0</td>\n",
315
+ " <td>IOBA-NHC</td>\n",
316
+ " <td>CCMSLIB00005724258</td>\n",
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+ " <td>2.0</td>\n",
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+ " <td>LC-ESI-qTof</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>59</td>\n",
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+ " <td>[[152.990509, 32.832367], [153.005264, 23.9671...</td>\n",
322
+ " <td>[[152.990509, 5.068854575905256], [153.005264,...</td>\n",
323
+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>959030</th>\n",
326
+ " <td>PS 20:3_18:0</td>\n",
327
+ " <td>PS 38:3</td>\n",
328
+ " <td>PS</td>\n",
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+ " <td>20:3_18:0</td>\n",
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+ " <td>20</td>\n",
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+ " <td>3</td>\n",
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+ " <td>...</td>\n",
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+ " <td>3.0</td>\n",
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+ " <td>IOBA-NHC</td>\n",
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+ " <td>CCMSLIB00005724259</td>\n",
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+ " <td>2.0</td>\n",
342
+ " <td>LC-ESI-qTof</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>41</td>\n",
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+ " <td>[[152.991302, 68.0], [247.26268, 16.0], [255.2...</td>\n",
346
+ " <td>[[152.991302, 21.451104100946374], [247.26268,...</td>\n",
347
+ " </tr>\n",
348
+ " </tbody>\n",
349
+ "</table>\n",
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+ "<p>462341 rows × 37 columns</p>\n",
351
+ "</div>"
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+ ],
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+ "\n",
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+ " num_c_2 num_db_2 extra_2 ... retention_time lib_quality source \\\n",
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+ "0 2 0 NaN ... 0.0 NaN MS-DIAL \n",
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+ "4 6 0 NaN ... 0.0 NaN MS-DIAL \n",
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+ "... ... ... ... ... ... ... ... \n",
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+ "959026 16 1 NaN ... NaN 3.0 IOBA-NHC \n",
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+ "959027 22 5 NaN ... NaN 3.0 IOBA-NHC \n",
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+ "959028 18 1 NaN ... NaN 3.0 IOBA-NHC \n",
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+ "959029 22 0 NaN ... NaN 3.0 IOBA-NHC \n",
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+ "959030 18 0 NaN ... NaN 3.0 IOBA-NHC \n",
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+ "\n",
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+ " spectrum_id ms_level instrument energy num_peaks \\\n",
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+ "0 NaN NaN NaN NaN 3 \n",
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+ "1 NaN NaN NaN NaN 4 \n",
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+ "2 NaN NaN NaN NaN 4 \n",
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+ "3 NaN NaN NaN NaN 4 \n",
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+ "4 NaN NaN NaN NaN 4 \n",
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+ "... ... ... ... ... ... \n",
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+ "959026 CCMSLIB00005724255 2.0 LC-ESI-qTof NaN 36 \n",
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+ "959027 CCMSLIB00005724256 2.0 LC-ESI-qTof NaN 53 \n",
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+ "959028 CCMSLIB00005724257 2.0 LC-ESI-qTof NaN 29 \n",
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+ "959029 CCMSLIB00005724258 2.0 LC-ESI-qTof NaN 59 \n",
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+ "959030 CCMSLIB00005724259 2.0 LC-ESI-qTof NaN 41 \n",
392
+ "\n",
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+ " MS2 \\\n",
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+ "0 [[117.0546, 1998], [159.0663, 50], [348.1054, ... \n",
395
+ "1 [[117.0546, 999], [131.0703, 999], [173.0819, ... \n",
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+ "2 [[117.0546, 999], [145.0859, 999], [187.0976, ... \n",
397
+ "3 [[117.0546, 999], [159.1016, 999], [201.1132, ... \n",
398
+ "4 [[117.0546, 999], [173.1172, 999], [215.1289, ... \n",
399
+ "... ... \n",
400
+ "959026 [[152.993195, 59.0], [153.990814, 19.0], [252.... \n",
401
+ "959027 [[108.166229, 17.0], [152.996857, 103.912659],... \n",
402
+ "959028 [[152.989365, 51.677917], [153.000549, 101.095... \n",
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+ "959029 [[152.990509, 32.832367], [153.005264, 23.9671... \n",
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+ "959030 [[152.991302, 68.0], [247.26268, 16.0], [255.2... \n",
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+ "959029 [[152.990509, 5.068854575905256], [153.005264,... \n",
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+ "959030 [[152.991302, 21.451104100946374], [247.26268,... \n",
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+ "\n",
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+ "[462341 rows x 37 columns]"
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+ ]
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+ "metadata": {},
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+ }
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+ "execution_count": 5,
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+ "id": "5b5a99dd",
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+ "execution_count": 6,
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+ "id": "18a39431",
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+ "metadata": {},
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+ "train_df = train_df.head(10000)\n",
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README.md CHANGED
@@ -1,6 +1,3 @@
1
- ## Download
2
- training_set, test_set, test_set2, can be downloaded from: https://huggingface.co/datasets/Bowen999/lipids_ms2/tree/main
3
-
4
  ## Data Source
5
  - **Training Set**:
6
  The training set contains 959,031 entries of lipid MS/MS spectra and corresponding structural information.
@@ -10,9 +7,6 @@ training_set, test_set, test_set2, can be downloaded from: https://huggingface.c
10
  The test set is sourced from **MassSpecGym**, containing 4,631 MS/MS spectra.
11
  Among them, 1,372 spectra are **novel** (not present in the training set).
12
 
13
- - **Test Set2**
14
- MS2 data of standards from Li Lab and Nova Medical Testing (non-public data)"
15
-
16
  *Preprocessing for **comprehensive MS2 databases** (like MassSpecGym) involved retaining only lipids by filtering the **InChI Key main block** using references from the MS DIAL Lipidome Atlas and Swiss Lipids*.
17
 
18
 
@@ -60,3 +54,4 @@ MS2 data of standards from Li Lab and Nova Medical Testing (non-public data)"
60
  | 38 | **novel** | Flag indicating novel lipid entry (y or n); **Only test set have this column**|
61
 
62
 
 
 
 
 
 
1
  ## Data Source
2
  - **Training Set**:
3
  The training set contains 959,031 entries of lipid MS/MS spectra and corresponding structural information.
 
7
  The test set is sourced from **MassSpecGym**, containing 4,631 MS/MS spectra.
8
  Among them, 1,372 spectra are **novel** (not present in the training set).
9
 
 
 
 
10
  *Preprocessing for **comprehensive MS2 databases** (like MassSpecGym) involved retaining only lipids by filtering the **InChI Key main block** using references from the MS DIAL Lipidome Atlas and Swiss Lipids*.
11
 
12
 
 
54
  | 38 | **novel** | Flag indicating novel lipid entry (y or n); **Only test set have this column**|
55
 
56
 
57
+ ## Download
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