MALDI-MSI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Description\n",
    "\n",
    "## Context\n",
    "This dataset is originally from Gastroenterology, Justus Liebig University Giessen, 35392, Giessen, Germany. The objective of the dataset is to better understand the different levels of host–pathogen interaction and pathology,  and the characterization of glycosphingolipids (GSLs)\n",
    "\n",
    "## Acknowledgements\n",
    " [David Luh, Sven Heiles, Martin Roderfeld, Christoph G. Grevelding, Elke Roeb and Bernhard Spengler* (2024). Hepatic Topology of Glycosphingolipids in Schistosoma mansoni-Infected Hamsters. In Anal. Chem. 2024, 96, 16, 6311–6320](https://pubs.acs.org/doi/10.1021/acs.analchem.3c05846)\n",
    "\n",
    "\n",
    "## About this dataset\n",
    "The particular data used in the project is downloaded from METASPACE from [here](https://metaspace2020.eu/dataset/2023-10-24_11h23m09s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis with Cardinal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if (!requireNamespace(\"BiocManager\", quietly = TRUE))\n",
    "    install.packages(\"BiocManager\")\n",
    "BiocManager::install(\"Cardinal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(Cardinal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: ProtGenerics\n",
      "\n",
      "\n",
      "Attaching package: 'ProtGenerics'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:stats':\n",
      "\n",
      "    smooth\n",
      "\n",
      "\n",
      "Loading required package: BiocGenerics\n",
      "\n",
      "\n",
      "Attaching package: 'BiocGenerics'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n",
      "    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n",
      "    get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n",
      "    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n",
      "    Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,\n",
      "    tapply, union, unique, unsplit, which.max, which.min\n",
      "\n",
      "\n",
      "Loading required package: BiocParallel\n",
      "\n",
      "Loading required package: S4Vectors\n",
      "\n",
      "Loading required package: stats4\n",
      "\n",
      "\n",
      "Attaching package: 'S4Vectors'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:utils':\n",
      "\n",
      "    findMatches\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    expand.grid, I, unname\n",
      "\n",
      "\n",
      "Cardinal v3.6 is a major update.\n",
      "Use vignette('Cardinal3-guide') to see what's new.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MSImagingArrays with 61400 spectra \n",
       "spectraData(2): mz, intensity\n",
       "pixelData(3): x, y, run\n",
       "coord(2): x = 1...307, y = 1...200\n",
       "runNames(1): F2_liver_bs-infected_hamster\n",
       "experimentData(7): spectrumType, spectrumRepresentation, contactName, ..., contactEmail, instrumentModel, pixelSize\n",
       "centroided: TRUE \n",
       "continuous: FALSE "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<61400 length> matter_list :: out-of-memory list\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=1 709.9389 714.3127 734.2728 736.0455 741.0827 752.1255 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=2 707.0722 708.0758 709.4698 709.9339 709.9438 710.4650 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=3 703.1005 705.5126 706.0646 707.0725 707.1751 708.0752 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=4 706.0634 707.0724 708.0777 709.0768 709.9393 712.0681 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=5 705.5124 706.5151 707.0726 708.0760 709.9362 709.9457 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=6 705.0485 705.5120 706.0638 707.0725 708.0754 708.2353 ...\n",
       "...\n",
       "(6.15 MB real | 293 MB virtual)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<61400 length> matter_list :: out-of-memory list\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=1 513.83661  70.44469  56.08697  58.55112  55.96539  61.28626 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=2 500.52844 132.70403  62.59430 190.40161 239.92535  58.76639 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=3 119.50774  71.53461  93.96964 345.98029  53.94270 164.54970 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=4 123.57072 431.74521  67.47224  64.08582 497.98672  60.79632 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=5 127.70343  72.11727 374.27628  93.63570 386.98755 113.13115 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=6  61.45592  77.15719  95.97279 412.46664 191.18527  57.92792 ...\n",
       "...\n",
       "(6.15 MB real | 146.5 MB virtual)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<61400 length> matter_list :: out-of-memory list\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=1 709.9389 714.3127 734.2728 736.0455 741.0827 752.1255 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=2 707.0722 708.0758 709.4698 709.9339 709.9438 710.4650 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=3 703.1005 705.5126 706.0646 707.0725 707.1751 708.0752 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=4 706.0634 707.0724 708.0777 709.0768 709.9393 712.0681 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=5 705.5124 706.5151 707.0726 708.0760 709.9362 709.9457 ...\n",
       "             [1]      [2]      [3]      [4]      [5]      [6] ...\n",
       "$Scan=6 705.0485 705.5120 706.0638 707.0725 708.0754 708.2353 ...\n",
       "...\n",
       "(6.15 MB real | 293 MB virtual)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<61400 length> matter_list :: out-of-memory list\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=1 513.83661  70.44469  56.08697  58.55112  55.96539  61.28626 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=2 500.52844 132.70403  62.59430 190.40161 239.92535  58.76639 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=3 119.50774  71.53461  93.96964 345.98029  53.94270 164.54970 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=4 123.57072 431.74521  67.47224  64.08582 497.98672  60.79632 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=5 127.70343  72.11727 374.27628  93.63570 386.98755 113.13115 ...\n",
       "              [1]       [2]       [3]       [4]       [5]       [6] ...\n",
       "$Scan=6  61.45592  77.15719  95.97279 412.46664 191.18527  57.92792 ...\n",
       "...\n",
       "(6.15 MB real | 146.5 MB virtual)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PositionDataFrame with 61400 rows and 3 columns\n",
       "                   x         y                          run\n",
       "           <numeric> <numeric>                     <factor>\n",
       "Scan=1             1         1 F2_liver_bs-infected_hamster\n",
       "Scan=2             2         1 F2_liver_bs-infected_hamster\n",
       "Scan=3             3         1 F2_liver_bs-infected_hamster\n",
       "Scan=4             4         1 F2_liver_bs-infected_hamster\n",
       "Scan=5             5         1 F2_liver_bs-infected_hamster\n",
       "...              ...       ...                          ...\n",
       "Scan=61396       303       200 F2_liver_bs-infected_hamster\n",
       "Scan=61397       304       200 F2_liver_bs-infected_hamster\n",
       "Scan=61398       305       200 F2_liver_bs-infected_hamster\n",
       "Scan=61399       306       200 F2_liver_bs-infected_hamster\n",
       "Scan=61400       307       200 F2_liver_bs-infected_hamster\n",
       "coord(2): x, y\n",
       "run(1): run"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PositionDataFrame with 61400 rows and 3 columns\n",
       "                   x         y                          run\n",
       "           <numeric> <numeric>                     <factor>\n",
       "Scan=1             1         1 F2_liver_bs-infected_hamster\n",
       "Scan=2             2         1 F2_liver_bs-infected_hamster\n",
       "Scan=3             3         1 F2_liver_bs-infected_hamster\n",
       "Scan=4             4         1 F2_liver_bs-infected_hamster\n",
       "Scan=5             5         1 F2_liver_bs-infected_hamster\n",
       "...              ...       ...                          ...\n",
       "Scan=61396       303       200 F2_liver_bs-infected_hamster\n",
       "Scan=61397       304       200 F2_liver_bs-infected_hamster\n",
       "Scan=61398       305       200 F2_liver_bs-infected_hamster\n",
       "Scan=61399       306       200 F2_liver_bs-infected_hamster\n",
       "Scan=61400       307       200 F2_liver_bs-infected_hamster\n",
       "coord(2): x, y\n",
       "run(1): run"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DataFrame with 61400 rows and 2 columns\n",
       "                   x         y\n",
       "           <numeric> <numeric>\n",
       "Scan=1             1         1\n",
       "Scan=2             2         1\n",
       "Scan=3             3         1\n",
       "Scan=4             4         1\n",
       "Scan=5             5         1\n",
       "...              ...       ...\n",
       "Scan=61396       303       200\n",
       "Scan=61397       304       200\n",
       "Scan=61398       305       200\n",
       "Scan=61399       306       200\n",
       "Scan=61400       307       200"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'F2_liver_bs-infected_hamster'"
      ],
      "text/latex": [
       "'F2\\_liver\\_bs-infected\\_hamster'"
      ],
      "text/markdown": [
       "'F2_liver_bs-infected_hamster'"
      ],
      "text/plain": [
       "[1] \"F2_liver_bs-infected_hamster\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>F2_liver_bs-infected_hamster</li><li>F2_liver_bs-infected_hamster</li><li>F2_liver_bs-infected_hamster</li><li>F2_liver_bs-infected_hamster</li><li>F2_liver_bs-infected_hamster</li><li>F2_liver_bs-infected_hamster</li></ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t'F2_liver_bs-infected_hamster'\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item F2\\_liver\\_bs-infected\\_hamster\n",
       "\\item F2\\_liver\\_bs-infected\\_hamster\n",
       "\\item F2\\_liver\\_bs-infected\\_hamster\n",
       "\\item F2\\_liver\\_bs-infected\\_hamster\n",
       "\\item F2\\_liver\\_bs-infected\\_hamster\n",
       "\\item F2\\_liver\\_bs-infected\\_hamster\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: 'F2\\_liver\\_bs-infected\\_hamster'"
      ],
      "text/markdown": [
       "1. F2_liver_bs-infected_hamster\n",
       "2. F2_liver_bs-infected_hamster\n",
       "3. F2_liver_bs-infected_hamster\n",
       "4. F2_liver_bs-infected_hamster\n",
       "5. F2_liver_bs-infected_hamster\n",
       "6. F2_liver_bs-infected_hamster\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 'F2_liver_bs-infected_hamster'"
      ],
      "text/plain": [
       "[1] F2_liver_bs-infected_hamster F2_liver_bs-infected_hamster\n",
       "[3] F2_liver_bs-infected_hamster F2_liver_bs-infected_hamster\n",
       "[5] F2_liver_bs-infected_hamster F2_liver_bs-infected_hamster\n",
       "Levels: F2_liver_bs-infected_hamster"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "MSImagingExperiment with 6 features and 61400 spectra \n",
       "spectraData(1): intensity\n",
       "featureData(1): mz\n",
       "pixelData(3): x, y, run\n",
       "coord(2): x = 1...307, y = 1...200\n",
       "runNames(1): F2_liver_bs-infected_hamster\n",
       "experimentData(7): spectrumType, spectrumRepresentation, contactName, ..., contactEmail, instrumentModel, pixelSize\n",
       "mass range: 700.048 to 700.262 \n",
       "centroided: TRUE "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Unzip the data\n",
    "unzip(\"data.zip\")\n",
    "\n",
    "#Loading data\n",
    "#To load MSI data, use the readMSIData function\n",
    "\n",
    "df <- readMSIData(\"data/F2_liver_bs-infected_hamster.imzML\")\n",
    "\n",
    "#Inspecting Data\n",
    "# inspect Spectra\n",
    "spectra(df, \"mz\")\n",
    "spectra(df, \"intensity\")\n",
    "\n",
    "#Mass-to-Charge Ratios (m/z):\n",
    "\n",
    "mz(df)\n",
    "\n",
    "# Intensity data\n",
    "intensity(df)\n",
    "\n",
    "#Pixel data\n",
    "pixelData(df)\n",
    "\n",
    "# Phenotypic data\n",
    "pData(df)\n",
    "\n",
    "#Coordinates\n",
    "coord(df)\n",
    "\n",
    "#Run names\n",
    "runNames(df)\n",
    "\n",
    "#Run data\n",
    "head(run(df))\n",
    "#df is msa, imp to convert to mse as matrix\n",
    "\n",
    "#Visualising data\n",
    "\n",
    "df_mse <-as(df, \"MSImagingExperiment\")\n",
    "\n",
    "# optional Subsetting features by m/z values:\n",
    "#df_mse <- subsetFeatures(df_mse, 1263 < mz, mz > 2460)\n",
    "#head(df_mse)\n",
    "\n",
    "#Visualising specific m/z values\n",
    "\n",
    "mzs <- c( 1578.9015, 1576.8850, 1552.8859, 1265.7135,2442.2211, 1468.7919)\n",
    "mycol1 <- colorRampPalette(c(\"black\", \"green\", \"red\"))(1000)\n",
    "mycol1 <- adjustcolor(mycol1, alpha.f = 0.6)\n",
    "image_a <- image(df_mse, mz=mzs, col=mycol1,  layout=c(3,3))\n",
    "image_a"
   ]
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}