- add normalisation to NMR and LC-MS examples

phenomecentre · Jun 7, 2021 · 909d58d · 909d58d
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diff --git a/Preprocessing and Quality Control of LC-MS Data with the nPYc-Toolbox.ipynb b/Preprocessing and Quality Control of LC-MS Data with the nPYc-Toolbox.ipynb
@@ -938,6 +938,56 @@
    "source": [
     "nPYc.reports.generateReport(msDatacorrected, 'final report', pcaModel=PCAmodel, destinationPath='.')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 8. OPTIONAL: Normalise Data and Export"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dilution effects on global sample intensity can be normalised by using one of the available [Normalisation classes](https://npyc-toolbox.readthedocs.io/en/latest/normalisation.html).\n",
+    "\n",
+    "This is especially critical in urine samples when samples can vary substantially in concentration. Here we apply Probabilistic Quotient Normalisation (PQN) [Deterle et al. 2006](https://pubs.acs.org/doi/10.1021/ac051632c).\n",
+    "\n",
+    "Setting the 'Normalisation' parameter in msDataCorrected automatically applies the selected normalisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "msDatacorrected.Normalisation = ProbabilisticQuotientNormaliser()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the normalised data exported (here we first change the name for clarity and so as not to overwrite existing data):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Append name\n",
+    "msDatacorrected.name = msDatacorrected.name + '_PQN_normalised'\n",
+    "\n",
+    "# Export dataset\n",
+    "msDatacorrected.exportDataset(destinationPath='.')\n",
+    "\n",
+    "# Export final summary report\n",
+    "nPYc.reports.generateReport(msDatacorrected, 'final report', destinationPath='.')"
+   ]
   }
  ],
  "metadata": {
@@ -957,7 +1007,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,

diff --git a/Preprocessing and Quality Control of NMR Data with the nPYc-Toolbox.ipynb b/Preprocessing and Quality Control of NMR Data with the nPYc-Toolbox.ipynb
@@ -753,6 +753,63 @@
    "source": [
     "nPYc.reports.generateReport(nmrData, 'final report', pcaModel=PCAmodel, destinationPath='.')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 8. OPTIONAL: Normalise Data and Export"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dilution effects on global sample intensity can be normalised by using one of the available [Normalisation classes](https://npyc-toolbox.readthedocs.io/en/latest/normalisation.html).\n",
+    "\n",
+    "This is especially critical in urine samples when samples can vary substantially in concentration. Here we apply Probabilistic Quotient Normalisation (PQN) [Deterle et al. 2006](https://pubs.acs.org/doi/10.1021/ac051632c).\n",
+    "\n",
+    "Setting the 'Normalisation' parameter in msDataCorrected automatically applies the selected normalisation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nmrData.Normalisation = ProbabilisticQuotientNormaliser()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the normalised data exported (here we first change the name for clarity and so as not to overwrite existing data):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Append name\n",
+    "nmrData.name = nmrData.name + '_PQN_normalised'\n",
+    "\n",
+    "# Export dataset\n",
+    "nmrData.exportDataset(destinationPath='.')\n",
+    "\n",
+    "# Export final summary report\n",
+    "nPYc.reports.generateReport(nmrData, 'final report', destinationPath='.')# Append name\n",
+    "nmrData.name = nmrData.name + '_PQN_normalised'\n",
+    "\n",
+    "# Export dataset\n",
+    "nmrData.exportDataset(destinationPath='.')\n",
+    "\n",
+    "# Export final summary report\n",
+    "nPYc.reports.generateReport(nmrData, 'final report', destinationPath='.')"
+   ]
   }
  ],
  "metadata": {
@@ -772,7 +829,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,

diff --git a/...sing and Quality Control of Targeted LC-MS Data (PeakPantheR) with the nPYc-Toolbox.ipynb b/...sing and Quality Control of Targeted LC-MS Data (PeakPantheR) with the nPYc-Toolbox.ipynb
@@ -878,7 +878,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,

diff --git a/...essing and Quality Control of Targeted NMR Data (Bruker IVDr) with the nPYc-toolbox.ipynb b/...essing and Quality Control of Targeted NMR Data (Bruker IVDr) with the nPYc-toolbox.ipynb
@@ -696,7 +696,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.5"
   },
   "pycharm": {
    "stem_cell": {