data_descriptor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Descriptor\n",
    "This notebook is designed to describe the data used in out figures, how they are accessed in the API, and what the values mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the data?\n",
    "Data used in this repository is single cell proteomics data. Specifically, a sinlge data file comes from the analysis of a mass spectometry dataset where multiple undividual cells were simultaneously measured using the TMT labeling stategy. When a TMT run is quantified with MaxQuant, a file called proteinGroups.txt is generated. Among other things, this file contains the reporter intensities from each of the channels divided by each of the protein groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to get the data\n",
    "Data from another program may also be used if they are formatted in the same way and the labels are provided.\n",
    "\n",
    "In python, running <code>import load_df</code> will allow us to access the module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import load_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can load data from the file using <code>load_df.load_dataset()</code> <br>If no filename is provided, this will open a dialog box where you can select one. Here we know the filename and so provide the path like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = load_data.load_dataset(\"data/proteinGroups_PNNL_TMT_SC_Chip1.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file was generated by MaxQuant. Three TMT runs, Chip1 A, Chip 1 B, and Chip 1 C were analyzed together. We can use these experiment names to get the channels from just one run. We use the same call but add <code>experiment='Chip1 A'</code>. This is what we'll do here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_data.load_dataset(\"data/proteinGroups_PNNL_TMT_SC_Chip1.txt\",\n",
    "                           experiment='Chip1 A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns may also be chosen explicitly by passing a list to <code>usecols</code>. Run <code>print_columns(\"your_file.txt\")</code> to see a list of the columns in your file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at the data and what the values mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>load_dataset</code> returned a pandas dataframe. Now we have the intensities from Chip 1 row A. We can use the function <code>head</code> to see the first few entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reporter intensity 1 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 2 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 3 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 4 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 5 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 6 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 7 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 8 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 9 PNNL SC Chip1 A</th>\n",
       "      <th>Reporter intensity 10 PNNL SC Chip1 A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein IDs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>A6NHQ2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.43</td>\n",
       "      <td>510.82</td>\n",
       "      <td>328.78</td>\n",
       "      <td>616.95</td>\n",
       "      <td>412.62</td>\n",
       "      <td>876.18</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>P62308;A8MWD9</td>\n",
       "      <td>1554.2</td>\n",
       "      <td>1964.70</td>\n",
       "      <td>12875.00</td>\n",
       "      <td>2807.50</td>\n",
       "      <td>3438.80</td>\n",
       "      <td>2022.30</td>\n",
       "      <td>1532.90</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q99613;B5ME19</td>\n",
       "      <td>2469.8</td>\n",
       "      <td>3046.60</td>\n",
       "      <td>3027.20</td>\n",
       "      <td>2311.30</td>\n",
       "      <td>3852.60</td>\n",
       "      <td>3483.30</td>\n",
       "      <td>2929.10</td>\n",
       "      <td>5947.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132140.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Reporter intensity 1 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                          0.0   \n",
       "P62308;A8MWD9                                1554.2   \n",
       "Q99613;B5ME19                                2469.8   \n",
       "\n",
       "               Reporter intensity 2 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                       800.43   \n",
       "P62308;A8MWD9                               1964.70   \n",
       "Q99613;B5ME19                               3046.60   \n",
       "\n",
       "               Reporter intensity 3 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                       510.82   \n",
       "P62308;A8MWD9                              12875.00   \n",
       "Q99613;B5ME19                               3027.20   \n",
       "\n",
       "               Reporter intensity 4 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                       328.78   \n",
       "P62308;A8MWD9                               2807.50   \n",
       "Q99613;B5ME19                               2311.30   \n",
       "\n",
       "               Reporter intensity 5 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                       616.95   \n",
       "P62308;A8MWD9                               3438.80   \n",
       "Q99613;B5ME19                               3852.60   \n",
       "\n",
       "               Reporter intensity 6 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                       412.62   \n",
       "P62308;A8MWD9                               2022.30   \n",
       "Q99613;B5ME19                               3483.30   \n",
       "\n",
       "               Reporter intensity 7 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                       876.18   \n",
       "P62308;A8MWD9                               1532.90   \n",
       "Q99613;B5ME19                               2929.10   \n",
       "\n",
       "               Reporter intensity 8 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                       1745.0   \n",
       "P62308;A8MWD9                                1959.0   \n",
       "Q99613;B5ME19                                5947.4   \n",
       "\n",
       "               Reporter intensity 9 PNNL SC Chip1 A  \\\n",
       "Protein IDs                                           \n",
       "A6NHQ2                                          0.0   \n",
       "P62308;A8MWD9                                   0.0   \n",
       "Q99613;B5ME19                                   0.0   \n",
       "\n",
       "               Reporter intensity 10 PNNL SC Chip1 A  \n",
       "Protein IDs                                           \n",
       "A6NHQ2                                       56772.0  \n",
       "P62308;A8MWD9                                61560.0  \n",
       "Q99613;B5ME19                               132140.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value in each cell tells us the reporter intensity for each corresponding protein. The higher the reporter intensity is the more protein is in the sample. Zeros mean that we didn't see the protein (as in Reporter insensity 9). "
   ]
  }
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