Built site for gh-pages

.nojekyll

@@ -1 +1 @@
-e2772c40
+dd13213d

4_integration_clustering.html

@@ -233,7 +233,7 @@ <h1 class="title">Integration and clustering</h1>
 </div>
 </div>
 <div class="callout-body-container callout-body">
-<p>How many variable features do we have? We did we select fewer genes than the default (check <code><a href="https://mojaveazure.github.io/seurat-object/reference/VariableFeatures.html">?VariableFeatures</a></code>)?</p>
+<p>How many variable features do we have? Why did we select fewer genes than the default (check <code><a href="https://mojaveazure.github.io/seurat-object/reference/VariableFeatures.html">?VariableFeatures</a></code>)?</p>
 </div>
 </div>
 <div class="callout callout-style-default callout-tip callout-titled">

precourse_preparations.html

@@ -189,7 +189,6 @@ <h1 class="title">Precourse preparations</h1>
 </header>
 
 
-<p>Audience and requirements</p>
 <p>The target audience are researchers that are interested to analyse spatial transcriptomics datasets. Attendees should have basic understanding of:</p>
 <ul>
 <li>The programming language R</li>

search.json

@@ -4,7 +4,7 @@
     "href": "precourse_preparations.html",
     "title": "Precourse preparations",
     "section": "",
-    "text": "Audience and requirements\nThe target audience are researchers that are interested to analyse spatial transcriptomics datasets. Attendees should have basic understanding of:\n\nThe programming language R\nDimensionality reduction techniques (PCA, UMAP)\nClustering techniques\n\nDimensionality reduction and clustering techniques are taught in the SIB course ‘Single-Cell Transcriptomics with R’ and prerequisites can be obtained through self-learning at https://sib-swiss.github.io/single-cell-training/."
+    "text": "The target audience are researchers that are interested to analyse spatial transcriptomics datasets. Attendees should have basic understanding of:\n\nThe programming language R\nDimensionality reduction techniques (PCA, UMAP)\nClustering techniques\n\nDimensionality reduction and clustering techniques are taught in the SIB course ‘Single-Cell Transcriptomics with R’ and prerequisites can be obtained through self-learning at https://sib-swiss.github.io/single-cell-training/."
   },
   {
     "objectID": "course_schedule.html",
@@ -25,7 +25,7 @@
     "href": "4_integration_clustering.html#dimensionality-reduction",
     "title": "Integration and clustering",
     "section": "Dimensionality reduction",
-    "text": "Dimensionality reduction\nIn order to perform dimensionality reduction, we first need to select variable features of both slices. To get a good representation of both slices, we take the intersect (i.e. genes that are variable in both slices):\n\nVariableFeatures(seu) <- intersect(VariableFeatures(seu_list$Anterior),\n                           VariableFeatures(seu_list$Posterior))\n\n\n\n\n\n\n\nExercise\n\n\n\nHow many variable features do we have? We did we select fewer genes than the default (check ?VariableFeatures)?\n\n\n\n\n\n\n\n\nAnswer\n\n\n\n\n\nJust by typing the object name we already see the number of variable features:\n\nseu\n\nAn object of class Seurat \n83447 features across 6039 samples within 3 assays \nActive assay: SCT (18877 features, 1938 variable features)\n 3 layers present: counts, data, scale.data\n 2 other assays present: Spatial, RNA\n 2 spatial fields of view present: Anterior Posterior\n\n\nSo, we have 1938 variable features. Because we selected the features that are variable in both slices, it is fewer genes than the originally selected default.\n\n\n\nNow that we have selected the most variable features, we can generate a PCA based on the normalized and scaled data of those:\n\nseu <- RunPCA(seu, assay = \"SCT\", npcs = 50, verbose = FALSE)\n\nDimPlot(seu, reduction = \"pca\", group.by = \"orig.ident\") + \n  scale_color_viridis_d(option = \"cividis\")\n\n\n\n\n\n\n\nBased on the PCA, we can create a UMAP to get a representation of all 50 dimensions in a two dimensional space:\n\nseu <- RunUMAP(seu, reduction = \"pca\", dims = 1:50)\n\nDimPlot(seu, reduction = \"umap\", group.by = \"orig.ident\") + \n  scale_color_viridis_d(option = \"cividis\")\n\n\n\n\n\n\n\n\n\n\n\n\n\nExercise\n\n\n\nThe two slices come from one brain, the posterior and anterior sides. Do you expect spots from similar cells/tissue in both slices? Is that represented in the UMAP?\n\n\n\n\n\n\n\n\nAnswer\n\n\n\n\n\nYes, it is likely that there are similar spots in both slides, but the spots of both slices hardly overlap in the UMAP. Therefore, it makes sense to do an integration."
+    "text": "Dimensionality reduction\nIn order to perform dimensionality reduction, we first need to select variable features of both slices. To get a good representation of both slices, we take the intersect (i.e. genes that are variable in both slices):\n\nVariableFeatures(seu) <- intersect(VariableFeatures(seu_list$Anterior),\n                           VariableFeatures(seu_list$Posterior))\n\n\n\n\n\n\n\nExercise\n\n\n\nHow many variable features do we have? Why did we select fewer genes than the default (check ?VariableFeatures)?\n\n\n\n\n\n\n\n\nAnswer\n\n\n\n\n\nJust by typing the object name we already see the number of variable features:\n\nseu\n\nAn object of class Seurat \n83447 features across 6039 samples within 3 assays \nActive assay: SCT (18877 features, 1938 variable features)\n 3 layers present: counts, data, scale.data\n 2 other assays present: Spatial, RNA\n 2 spatial fields of view present: Anterior Posterior\n\n\nSo, we have 1938 variable features. Because we selected the features that are variable in both slices, it is fewer genes than the originally selected default.\n\n\n\nNow that we have selected the most variable features, we can generate a PCA based on the normalized and scaled data of those:\n\nseu <- RunPCA(seu, assay = \"SCT\", npcs = 50, verbose = FALSE)\n\nDimPlot(seu, reduction = \"pca\", group.by = \"orig.ident\") + \n  scale_color_viridis_d(option = \"cividis\")\n\n\n\n\n\n\n\nBased on the PCA, we can create a UMAP to get a representation of all 50 dimensions in a two dimensional space:\n\nseu <- RunUMAP(seu, reduction = \"pca\", dims = 1:50)\n\nDimPlot(seu, reduction = \"umap\", group.by = \"orig.ident\") + \n  scale_color_viridis_d(option = \"cividis\")\n\n\n\n\n\n\n\n\n\n\n\n\n\nExercise\n\n\n\nThe two slices come from one brain, the posterior and anterior sides. Do you expect spots from similar cells/tissue in both slices? Is that represented in the UMAP?\n\n\n\n\n\n\n\n\nAnswer\n\n\n\n\n\nYes, it is likely that there are similar spots in both slides, but the spots of both slices hardly overlap in the UMAP. Therefore, it makes sense to do an integration."
   },
   {
     "objectID": "4_integration_clustering.html#integration",

sitemap.xml

@@ -2,34 +2,34 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
   <url>
     <loc>https://sib-swiss.github.io/spatial-transcriptomics-training/precourse_preparations.html</loc>
-    <lastmod>2024-06-14T11:10:58.745Z</lastmod>
+    <lastmod>2024-06-14T14:51:44.439Z</lastmod>
   </url>
   <url>
     <loc>https://sib-swiss.github.io/spatial-transcriptomics-training/course_schedule.html</loc>
-    <lastmod>2024-06-14T11:10:58.745Z</lastmod>
+    <lastmod>2024-06-14T14:51:44.439Z</lastmod>
   </url>
   <url>
     <loc>https://sib-swiss.github.io/spatial-transcriptomics-training/4_integration_clustering.html</loc>
-    <lastmod>2024-06-14T11:10:58.729Z</lastmod>
+    <lastmod>2024-06-14T14:51:44.419Z</lastmod>
   </url>
   <url>
     <loc>https://sib-swiss.github.io/spatial-transcriptomics-training/2_quality_control.html</loc>
-    <lastmod>2024-06-14T11:10:58.729Z</lastmod>
+    <lastmod>2024-06-14T14:51:44.419Z</lastmod>
   </url>
   <url>
     <loc>https://sib-swiss.github.io/spatial-transcriptomics-training/1_setup.html</loc>
-    <lastmod>2024-06-14T11:10:58.729Z</lastmod>
+    <lastmod>2024-06-14T14:51:44.419Z</lastmod>
   </url>
   <url>
     <loc>https://sib-swiss.github.io/spatial-transcriptomics-training/3_normalization_scaling.html</loc>
-    <lastmod>2024-06-14T11:10:58.729Z</lastmod>
+    <lastmod>2024-06-14T14:51:44.419Z</lastmod>
   </url>
   <url>
     <loc>https://sib-swiss.github.io/spatial-transcriptomics-training/5_marker_gene_identification.html</loc>
-    <lastmod>2024-06-14T11:10:58.729Z</lastmod>
+    <lastmod>2024-06-14T14:51:44.419Z</lastmod>
   </url>
   <url>
     <loc>https://sib-swiss.github.io/spatial-transcriptomics-training/index.html</loc>
-    <lastmod>2024-06-14T11:10:58.745Z</lastmod>
+    <lastmod>2024-06-14T14:51:44.439Z</lastmod>
   </url>
 </urlset>