Remove some TODOs

Signed-off-by: zethson <[email protected]>
theislab · Nov 14, 2023 · 43652ed · 43652ed
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Showing 4 changed files with 4 additions and 16 deletions.
diff --git a/jupyter-book/air_repertoire/ir_profiling.ipynb b/jupyter-book/air_repertoire/ir_profiling.ipynb
@@ -2634,14 +2634,6 @@
     "adata_bcr.obs.head()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "af90f8bf",
-   "metadata": {},
-   "source": [
-    "TODO downsampling for faster runs => depends on what to show in future chapters => not done yet"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5e729d42",

diff --git a/jupyter-book/air_repertoire/multimodal_integration.ipynb b/jupyter-book/air_repertoire/multimodal_integration.ipynb
@@ -91,15 +91,15 @@
    "id": "df125d78",
    "metadata": {},
    "source": [
-    "! wget -O $path_bcr_input -nc # todo file link https://figshare.com/ndownloader/files/35574338"
+    "! wget -O $path_bcr_input -nc https://figshare.com/ndownloader/files/35574338"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "d562d189",
    "metadata": {},
    "source": [
-    "! wget -O $path_bcr_input -nc # todo file link https://figshare.com/ndownloader/files/35574338"
+    "! wget -O $path_bcr_input -nc https://figshare.com/ndownloader/files/35574338"
    ]
   },
   {
@@ -2443,7 +2443,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.5"
   },
   "vscode": {
    "interpreter": {

diff --git a/jupyter-book/cellular_structure/clustering.ipynb b/jupyter-book/cellular_structure/clustering.ipynb
@@ -97,9 +97,7 @@
    "id": "504c02ab-3614-430e-a6a7-deea6d9872d8",
    "metadata": {},
    "source": [
-    "The Leiden algorithm leverages a KNN graph on the reduced expression space. We can calculate the KNN graph on a lower-dimensional gene expression representation with the scanpy function `sc.pp.neighbors`. We call this function on the top 30 principal-components as these capture most of the variance in the dataset (see Preprocessing chapter TODO LINK THIS LATER). \n",
-    "\n",
-    "Visualizing the clustering can help us to understand the results, we therefore embed our cells into a UMAP embedding. More information on UMAP visualizations can be found in the visualization section. TODO LINK THIS LATER"
+    "The Leiden algorithm leverages a KNN graph on the reduced expression space. We can calculate the KNN graph on a lower-dimensional gene expression representation with the scanpy function `sc.pp.neighbors`. We call this function on the top 30 principal-components as these capture most of the variance in the dataset. Visualizing the clustering can help us to understand the results, we therefore embed our cells into a UMAP embedding. More details can be found in the {ref}`pre-processing:dimensionality-reduction` chapter."
    ]
   },
   {

diff --git a/jupyter-book/surface_protein/quality_control.ipynb b/jupyter-book/surface_protein/quality_control.ipynb
@@ -101,7 +101,6 @@
    },
    "outputs": [],
    "source": [
-    "# ToDo adjust this\n",
     "rna = sc.read_10x_h5(\n",
     "    filename=\"rna_filtered_feature_bc_matrix.h5\",\n",
     "    backup_url=\"https://figshare.com/ndownloader/files/39546196\",\n",
@@ -196,7 +195,6 @@
     }
    ],
    "source": [
-    "# ToDo adjust this\n",
     "rna_raw = sc.read_10x_h5(\n",
     "    filename=\"rna_raw_feature_bc_matrix.h5\",\n",
     "    backup_url=\"https://figshare.com/ndownloader/files/39546217\",\n",