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diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
-3083a05d
+7f8eb16e
diff --git a/results/index.html b/results/index.html
diff --git a/search.json b/search.json
@@ -4,14 +4,14 @@
     "href": "supplementary/index.html",
     "title": "Supplementary",
     "section": "",
-    "text": "Evaluation\nTask\nSubTask\nTransformation\nMetric\nRange\n\n\n\n\nData properties\nSpot-level\nFraction zero\nNo\nKernel Density\n[0, infinity) then map to [0, 1]\n\n\n\n\nLibrary size\nNo\n\n\n\n\n\n\nTMM\nNo\n\n\n\n\n\n\nEffective library size\nNo\n\n\n\n\n\n\nScaled variance\nYes, z-score standardization\n\n\n\n\n\n\nScaled mean\nYes, z-score standardization\n\n\n\n\n\n\nLibrary size vs Fraction zero\nNo\n\n\n\n\n\n\nSample Pearson correlation\nNo\n\n\n\n\n\nGene-level\nFraction zero gene\nNo\n\n\n\n\n\n\nScaled variance\n\nYes, z-score standardization\n\n\n\n\n\nScaled mean\nYes, z-score standardization\n\n\n\n\n\n\nMean vs variance\nNo\n\n\n\n\n\n\nMean vs variance (scale)\n\nYes, log-normal transformation\n\n\n\n\n\nMean vs fraction zero\nNo\n\n\n\n\n\n\nGene Pearson correlation\nNo\n\n\n\n\n\nSpatial-level\nTransition Matrix (TM)\nNo\n\n\n\n\n\n\nNeighborhood enrichment matrix (NEM)\nNo\n\n\n\n\n\n\nCentralized score matrix (CSM)\nNo\n\n\n\n\n\n\nCell type interaction\nNo\n\n\n\n\n\n\nMoran’s I\nNo\n\n\n\n\n\n\nL statistics\nNo\n\n\n\n\n\n\nNearest neighbor correlation\nNo\n\n\n\n\nSpatial downstream analysis\nSpatial clustering\n\nNo\nAdjusted rand index (ARI)\n[-1, 1]\n\n\n\n\n\nNo\nNormalized mutual information (NMI)\n[0, 1]\n\n\n\nCell type deconvolution\n\nNo\nRoot mean square deviation (RMSE)\n[0, infinity)\n\n\n\n\n\nNo\nJensen-Shannon divergence (JSD)\n[0, 1]\n\n\n\nSpatially variable genes (SVG) identification\n\nNo\nPrecision\n[0, 1]\n\n\n\n\n\nNo\nRecall\n[0, 1]\n\n\n\nSpatial cross-correlation\n\nNo\nCosine similarity\n[-1, 1]\n\n\n\n\n\nNo\nMental statistics\n[-1, 1]\n\n\nScalability\n200 x 200\n\nNo\ntime\n[0, infinity)\n\n\n\n3k x 500\n\nNo\nmemory\n[0, infinity)\n\n\n\n5k x 1k"
+    "text": "Evaluation\nTask\nSubTask\nTransformation\nMetric\nRange\n\n\n\n\nData properties\nSpot-level\nFraction zero\nNo\nKernel Density\n[0, infinity) then map to [0, 1]\n\n\n\n\nLibrary size\nNo\n\n\n\n\n\n\nTMM\nNo\n\n\n\n\n\n\nEffective library size\nNo\n\n\n\n\n\n\nScaled variance\nYes, z-score standardization\n\n\n\n\n\n\nScaled mean\nYes, z-score standardization\n\n\n\n\n\n\nLibrary size vs Fraction zero\nNo\n\n\n\n\n\n\nSample Pearson correlation\nNo\n\n\n\n\n\nGene-level\nFraction zero gene\nNo\n\n\n\n\n\n\nScaled variance\nYes, z-score standardization\n\n\n\n\n\n\nScaled mean\nYes, z-score standardization\n\n\n\n\n\n\nMean vs variance\nNo\n\n\n\n\n\n\nMean vs variance (scale)\nYes, log-normal transformation\n\n\n\n\n\n\nMean vs fraction zero\nNo\n\n\n\n\n\n\nGene Pearson correlation\nNo\n\n\n\n\n\nSpatial-level\nTransition Matrix (TM)\nNo\n\n\n\n\n\n\nNeighborhood enrichment matrix (NEM)\nNo\n\n\n\n\n\n\nCentralized score matrix (CSM)\nNo\n\n\n\n\n\n\nCell type interaction\nNo\n\n\n\n\n\n\nMoran’s I\nNo\n\n\n\n\n\n\nL statistics\nNo\n\n\n\n\n\n\nNearest neighbor correlation\nNo\n\n\n\n\nSpatial downstream analysis\nSpatial clustering\n\nNo\nAdjusted rand index (ARI)\n[-1, 1]\n\n\n\n\n\nNo\nNormalized mutual information (NMI)\n[0, 1]\n\n\n\nCell type deconvolution\n\nNo\nRoot mean square deviation (RMSE)\n[0, infinity)\n\n\n\n\n\nNo\nJensen-Shannon divergence (JSD)\n[0, 1]\n\n\n\nSpatially variable genes (SVG) identification\n\nNo\nPrecision\n[0, 1]\n\n\n\n\n\nNo\nRecall\n[0, 1]\n\n\n\nSpatial cross-correlation\n\nNo\nCosine similarity\n[-1, 1]\n\n\n\n\n\nNo\nMental statistics\n[-1, 1]\n\n\nScalability\n200 x 200\n\nNo\ntime\n[0, infinity)\n\n\n\n3k x 500\n\nNo\nmemory\n[0, infinity)\n\n\n\n5k x 1k"
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     "href": "supplementary/index.html#details-of-the-three-evaluations-categories-with-metric-and-range-in-this-study",
     "title": "Supplementary",
     "section": "",
-    "text": "Evaluation\nTask\nSubTask\nTransformation\nMetric\nRange\n\n\n\n\nData properties\nSpot-level\nFraction zero\nNo\nKernel Density\n[0, infinity) then map to [0, 1]\n\n\n\n\nLibrary size\nNo\n\n\n\n\n\n\nTMM\nNo\n\n\n\n\n\n\nEffective library size\nNo\n\n\n\n\n\n\nScaled variance\nYes, z-score standardization\n\n\n\n\n\n\nScaled mean\nYes, z-score standardization\n\n\n\n\n\n\nLibrary size vs Fraction zero\nNo\n\n\n\n\n\n\nSample Pearson correlation\nNo\n\n\n\n\n\nGene-level\nFraction zero gene\nNo\n\n\n\n\n\n\nScaled variance\n\nYes, z-score standardization\n\n\n\n\n\nScaled mean\nYes, z-score standardization\n\n\n\n\n\n\nMean vs variance\nNo\n\n\n\n\n\n\nMean vs variance (scale)\n\nYes, log-normal transformation\n\n\n\n\n\nMean vs fraction zero\nNo\n\n\n\n\n\n\nGene Pearson correlation\nNo\n\n\n\n\n\nSpatial-level\nTransition Matrix (TM)\nNo\n\n\n\n\n\n\nNeighborhood enrichment matrix (NEM)\nNo\n\n\n\n\n\n\nCentralized score matrix (CSM)\nNo\n\n\n\n\n\n\nCell type interaction\nNo\n\n\n\n\n\n\nMoran’s I\nNo\n\n\n\n\n\n\nL statistics\nNo\n\n\n\n\n\n\nNearest neighbor correlation\nNo\n\n\n\n\nSpatial downstream analysis\nSpatial clustering\n\nNo\nAdjusted rand index (ARI)\n[-1, 1]\n\n\n\n\n\nNo\nNormalized mutual information (NMI)\n[0, 1]\n\n\n\nCell type deconvolution\n\nNo\nRoot mean square deviation (RMSE)\n[0, infinity)\n\n\n\n\n\nNo\nJensen-Shannon divergence (JSD)\n[0, 1]\n\n\n\nSpatially variable genes (SVG) identification\n\nNo\nPrecision\n[0, 1]\n\n\n\n\n\nNo\nRecall\n[0, 1]\n\n\n\nSpatial cross-correlation\n\nNo\nCosine similarity\n[-1, 1]\n\n\n\n\n\nNo\nMental statistics\n[-1, 1]\n\n\nScalability\n200 x 200\n\nNo\ntime\n[0, infinity)\n\n\n\n3k x 500\n\nNo\nmemory\n[0, infinity)\n\n\n\n5k x 1k"
+    "text": "Evaluation\nTask\nSubTask\nTransformation\nMetric\nRange\n\n\n\n\nData properties\nSpot-level\nFraction zero\nNo\nKernel Density\n[0, infinity) then map to [0, 1]\n\n\n\n\nLibrary size\nNo\n\n\n\n\n\n\nTMM\nNo\n\n\n\n\n\n\nEffective library size\nNo\n\n\n\n\n\n\nScaled variance\nYes, z-score standardization\n\n\n\n\n\n\nScaled mean\nYes, z-score standardization\n\n\n\n\n\n\nLibrary size vs Fraction zero\nNo\n\n\n\n\n\n\nSample Pearson correlation\nNo\n\n\n\n\n\nGene-level\nFraction zero gene\nNo\n\n\n\n\n\n\nScaled variance\nYes, z-score standardization\n\n\n\n\n\n\nScaled mean\nYes, z-score standardization\n\n\n\n\n\n\nMean vs variance\nNo\n\n\n\n\n\n\nMean vs variance (scale)\nYes, log-normal transformation\n\n\n\n\n\n\nMean vs fraction zero\nNo\n\n\n\n\n\n\nGene Pearson correlation\nNo\n\n\n\n\n\nSpatial-level\nTransition Matrix (TM)\nNo\n\n\n\n\n\n\nNeighborhood enrichment matrix (NEM)\nNo\n\n\n\n\n\n\nCentralized score matrix (CSM)\nNo\n\n\n\n\n\n\nCell type interaction\nNo\n\n\n\n\n\n\nMoran’s I\nNo\n\n\n\n\n\n\nL statistics\nNo\n\n\n\n\n\n\nNearest neighbor correlation\nNo\n\n\n\n\nSpatial downstream analysis\nSpatial clustering\n\nNo\nAdjusted rand index (ARI)\n[-1, 1]\n\n\n\n\n\nNo\nNormalized mutual information (NMI)\n[0, 1]\n\n\n\nCell type deconvolution\n\nNo\nRoot mean square deviation (RMSE)\n[0, infinity)\n\n\n\n\n\nNo\nJensen-Shannon divergence (JSD)\n[0, 1]\n\n\n\nSpatially variable genes (SVG) identification\n\nNo\nPrecision\n[0, 1]\n\n\n\n\n\nNo\nRecall\n[0, 1]\n\n\n\nSpatial cross-correlation\n\nNo\nCosine similarity\n[-1, 1]\n\n\n\n\n\nNo\nMental statistics\n[-1, 1]\n\n\nScalability\n200 x 200\n\nNo\ntime\n[0, infinity)\n\n\n\n3k x 500\n\nNo\nmemory\n[0, infinity)\n\n\n\n5k x 1k"
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diff --git a/sitemap.xml b/sitemap.xml
@@ -2,7 +2,7 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
   <url>
     <loc>https://SydneyBioX.github.io/SpatialSimbench_website/supplementary/index.html</loc>
-    <lastmod>2024-11-06T06:50:33.320Z</lastmod>
+    <lastmod>2024-11-06T23:06:12.988Z</lastmod>
   </url>
   <url>
     <loc>https://SydneyBioX.github.io/SpatialSimbench_website/index.html</loc>

diff --git a/styles.css b/styles.css
@@ -29,6 +29,18 @@
   text-align: center;
 }
 
+thead{
+    background-color: #EDEDED;
+}
+
+thead, tbody, tfoot, tr, td, th  {
+    border-style: none;
+}
+
+.odd {
+    background-color: #f9f9f9;
+}
+
 svg {
     font-family: 'Arial Narrow';
     font-size: 7;

diff --git a/supplementary/index.html b/supplementary/index.html
@@ -134,6 +134,32 @@ <h1 class="title">Supplementary</h1>
 </header>
 
 
+<!-- 
+
+#| echo: false
+#| label: supplementary table 3
+#| tbl-cap: Details of the three evaluations categories with metric and range in this study
+
+from IPython.display import Markdown
+from tabulate import tabulate
+import pandas as pd
+
+df = pd.read_csv("data/supp3.csv", encoding='ISO-8859-1')
+
+df = df.fillna(' ')
+
+table = df.values.tolist()
+
+""" table = [["Sun","696,000",1.989e30],
+         ["Earth","6,371",5.972e24],
+         ["Moon","1,737",7.34e22],
+         ["Mars","3,390",6.39e23]] """
+
+Markdown(tabulate(
+  table, 
+  headers=["Evaluation", "Task", " ", "Transformation", "Metric", "Range"]
+))
+``` -->
 <section id="details-of-the-three-evaluations-categories-with-metric-and-range-in-this-study" class="level2">
 <h2 class="anchored" data-anchor-id="details-of-the-three-evaluations-categories-with-metric-and-range-in-this-study">Details of the three evaluations categories with metric and range in this study</h2>
 <table class="caption-top table">
@@ -232,9 +258,9 @@ <h2 class="anchored" data-anchor-id="details-of-the-three-evaluations-categories
 <td></td>
 <td></td>
 <td>Scaled variance</td>
-<td></td>
 <td>Yes, z-score standardization</td>
 <td></td>
+<td></td>
 </tr>
 <tr class="odd">
 <td></td>
@@ -256,9 +282,9 @@ <h2 class="anchored" data-anchor-id="details-of-the-three-evaluations-categories
 <td></td>
 <td></td>
 <td>Mean vs variance (scale)</td>
-<td></td>
 <td>Yes, log-normal transformation</td>
 <td></td>
+<td></td>
 </tr>
 <tr class="even">
 <td></td>