Fixes implemented after first presentation run through

templates/maldi-pipeline.ipynb

@@ -148,23 +148,12 @@
     "plt.rcParams[\"figure.constrained_layout.use\"] = False"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1.3. Intensity percentile\n",
-    "\n",
-    "Define the percentile to use for thresholding intensity values."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "intensity_percentile = 99"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -196,26 +185,13 @@
     "    imz_data=imz_data, intensity_percentile=intensity_percentile\n",
     ")\n",
     "\n",
-    "# display the format and the top few peaks extracted\n",
-    "display(total_mass_df.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* `global_intensity_threshold`: the intensity threshold defined by `intensity_percentile`, computed across all intensities extracted. This is used by the plotting functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "# define the global intensity threshold\n",
+    "intensity_percentile = 99\n",
     "global_intensity_threshold = np.percentile(total_mass_df[\"intensity\"].values, intensity_percentile)\n",
-    "print(f\"Global Intensity Threshold: {global_intensity_threshold}\")"
+    "print(f\"Global Intensity Threshold: {global_intensity_threshold}\")\n",
+    "\n",
+    "# display the format and the top few peaks extracted\n",
+    "display(total_mass_df.head())"
    ]
   },
   {
@@ -269,9 +245,7 @@
     "\n",
     "Although various peaks can be identified across a run's spectra, the vast majority of them will be too small to indicate any meaningful signal. To address this, a prominence-based peak filtering method is used (see https://www.mathworks.com/help/signal/ug/prominence.html for a good definition of prominence).\n",
     "\n",
-    "The first step is to use a rolling window-based approach to extract the prominence thresholds to use for peak filtering.\n",
-    "\n",
-    "* `window_size`: this parameter can be increased or decreased for more or less aggressive peak filtering respectively"
+    "The first step is to use a rolling window-based approach to extract the prominence thresholds to use for peak filtering."
    ]
   },
   {
@@ -427,9 +401,7 @@
    "source": [
     "## 4. Coordinate Integration\n",
     "\n",
-    "Once peaks have been identified, we need a way of mapping this information back to the slide. Across a coordinate's m/z spectrum, if it is also an identified peak from step 3, we store the corresponding intensity at that coordinate.\n",
-    "\n",
-    "**NOTE: recording the raw intensity is not a traditional coordinate integration technique, which normally uses AOC. The AOC technique is currently in development.**"
+    "Once peaks have been identified, we need a way of mapping this information back to the slide. Across a coordinate's m/z spectrum, if it is also an identified peak from step 3, we store the corresponding intensity at that coordinate."
    ]
   },
   {
@@ -471,7 +443,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Save the integrated intensity images per peak to the `\"extracted\"` folder. For every peak, a float32 and int32 image will be saved to the `float` and `int` subdirectories respectively."
+    "Save the integrated intensity images per peak to the `extracted` folder. For every peak, a float32 and int32 image will be saved to the `float` and `int` subdirectories respectively.\n",
+    "\n",
+    "* The `float` images should be used for quantitative downstresam analysis\n",
+    "* The `int` images are saved for visualization, as they're more compatible with most image viewers"
    ]
   },
   {
@@ -491,9 +466,7 @@
    "source": [
     "## 5. Glycan Library Matching\n",
     "\n",
-    "While the filtered peaks provide meaningful information, they're not particularly useful without knowing what glycan encompasses them. The master glycan library list (`library_peak_df`) defines the glycans of interest as well as the m/z value they're centered at. In this way, peak values can be mapped to their corresponding glycan within a tolerance range.\n",
-    "\n",
-    "* `ppm`: the tolerance range. Smaller values will lead to stricter matching and vice versa for larger values."
+    "While the filtered peaks provide meaningful information, they're not particularly useful without knowing what glycan encompasses them. The master glycan library list (`library_peak_df`) defines the glycans of interest as well as the m/z value they're centered at. In this way, peak values can be mapped to their corresponding glycan within a tolerance range."
    ]
   },
   {
@@ -504,10 +477,8 @@
    },
    "outputs": [],
    "source": [
-    "ppm = 100\n",
-    "\n",
     "matched_peaks_df = extraction.library_matching(\n",
-    "    image_xr=image_data, library_peak_df=library_peak_df, ppm=ppm, extraction_dir=extraction_dir\n",
+    "    image_xr=image_data, library_peak_df=library_peak_df, ppm=100, extraction_dir=extraction_dir\n",
     ")"
    ]
   },
@@ -678,7 +649,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   },
   "vscode": {
    "interpreter": {