From b333b542a8ba7836d313a83263d2766cf72b0c23 Mon Sep 17 00:00:00 2001 From: Mehrtash Babadi Date: Mon, 22 Jul 2024 21:58:37 +0000 Subject: [PATCH] HOTFIX: removed a temporary cell from the tutorial notebook --- notebooks/quickstart_tutorial.ipynb | 20 +------------------- 1 file changed, 1 insertion(+), 19 deletions(-) diff --git a/notebooks/quickstart_tutorial.ipynb b/notebooks/quickstart_tutorial.ipynb index f774613..142ace0 100644 --- a/notebooks/quickstart_tutorial.ipynb +++ b/notebooks/quickstart_tutorial.ipynb @@ -222,7 +222,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The response will contain a list of annotation models and their brief descriptions. You need to choose the model that is suitable for your dataset. For this tutorial, we will choose **model_name**, which is suitable for annotating Human scRNA-seq datasets:" + "The response will contain a list of annotation models and their brief descriptions. You need to choose the model that is suitable for your dataset. For this tutorial, choose a model that is suitable for annotating human scRNA-seq datasets:" ] }, { @@ -318,24 +318,6 @@ ">**Note:** In a nutshell, the visualization shows various cell type ontology terms as colored circles. The size of the circle signifies the occurence of the term in the entire dataset (or over the chosen group of cells). The color of the circle signifies the relevance score of the term in cells over which the term was found to have any degree of relevance. You can highlight the ontology term relevance scores over the UMAP scatter plot by clicking on the circles. You can also show the terms relevant to individual cells and their scores by clicking on a cell over the UMAP scatter plot. " ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - } - }, - "outputs": [], - "source": [ - "# (This is temporary and will be removed)\n", - "import pickle\n", - "\n", - "with open(\"./resources/pbmc_10x_v3_4k__cas_ontology_aware_response.pkl\", \"rb\") as f:\n", - " loader = pickle.Unpickler(f)\n", - " cas_ontology_aware_response = loader.load()" - ] - }, { "cell_type": "code", "execution_count": null,