Merge pull request #111 from hubmapconsortium/development

v2.0.1

VERSION

@@ -1 +1 @@
-2.0.0
+2.0.1

src/user_templates_api/templates/jupyter_lab/templates/codex_clustering/template.txt

@@ -60,7 +60,7 @@
     "uuids = {{ uuids | safe }}\n",
     "\n",
     "# accepted datatypes \n",
-    "accepted_datatypes = ['codex_cytokit']\n",
+    "accepted_datatypes = ['CODEX [Cytokit + SPRM]']\n",
     "\n",
     "# required filetypes\n",
     "required_filetypes = ['sprm_outputs/reg001_expr.ome.tiff-cell_channel_total.csv', 'sprm_outputs/reg001_expr.ome.tiff-cell_channel_mean.csv', 'sprm_outputs/reg001_expr.ome.tiff-cell_centers.csv']\n",
@@ -114,19 +114,19 @@
     "            json={\n",
     "                'size': 10000,\n",
     "                'query': {'ids': {'values': uuids}},\n",
-    "                '_source': ['files', 'data_types']\n",
+    "                '_source': ['files', 'assay_display_name']\n",
     "            }, \n",
     "        ).text\n",
     "    )['hits']['hits']\n",
     "\n",
-    "    # create mapping for uuid to file_types and data_types\n",
+    "    # create mapping for uuid to file_types and assay_display_name\n",
     "    uuid_to_files = {}\n",
     "    uuid_to_datatypes = {}\n",
     "    for hit in hits:\n",
     "        file_paths = [file['rel_path'] for file in hit['_source']['files']]\n",
     "        uuid_to_files[hit['_id']] = file_paths\n",
     "\n",
-    "        hit_data_type = hit['_source']['data_types']\n",
+    "        hit_data_type = hit['_source']['assay_display_name']\n",
     "        uuid_to_datatypes[hit['_id']] = hit_data_type\n",
     "    \n",
     "    # save uuids without warnings\n",
@@ -150,9 +150,9 @@
     "                        accepted_uuids.remove(uuid)\n",
     "\n",
     "        if accepted_datatypes is not None: \n",
-    "            # check if data_types for each uuid are in accepted_datatypes\n",
-    "            data_types = uuid_to_datatypes[uuid]\n",
-    "            for data_type in data_types:\n",
+    "            # check if assay_display_name for each uuid are in accepted_datatypes\n",
+    "            assay_display_name = uuid_to_datatypes[uuid]\n",
+    "            for data_type in assay_display_name:\n",
     "                if data_type not in accepted_datatypes: \n",
     "                    warnings.warn('Dataset with UUID \"' + uuid + '\" has unaccepted data type: ' + data_type)\n",
     "                    if uuid in accepted_uuids:\n",

src/user_templates_api/templates/jupyter_lab/templates/scalable_cell_populations/metadata.json

@@ -0,0 +1,18 @@
+{
+  "title": "Scalable Cell Population Explorer",
+  "description": "This notebook shows how to load the scalable cell population viewer CellPop. It loads in datasets with annotated cell types and shows these in an interactive visualization.",
+  "tags": [
+    "visualization"
+  ], 
+  "is_multi_dataset_template": true,
+  "template_format": "jinja",
+  "examples": [
+    {
+      "title": "Explore snRNAseq Kidney datasets",
+      "description": "View the cell populations of 29 kidney datasets.",
+      "datasets": ["ad693f99fb9006e68a53e97598da1509","173de2e80adf6a73ac8cff5ccce20dfc","b95f34761c252ebbd1e482cd9afae73f","5a5ca03fa623602d9a859224aa40ace4","3c1b10bc912c60c9afc36b7423695236","1dc16eb0270ff73291dd45b6a96aa3c0","b05c21f9c94ce1a22a9694cd0fe0291e","8cdb42ed1194255c74c8462b99bbd7ef","fe0ded5fc0355c95239f9c040dd31e99","367fee3b40cba682063289505b922be1","b99fc30c4195958fbef217fa9ed9ec8f","898138b7f45a67c574e9955fb400e9be","f220c9e7bcaea3a87162cbe61287ea4d","e5f7a14d93659bd0b8dc2819ffa9bc4b","56cbda4789f04d79c0c3dffe21816d48","0b6f63f2bd61a8c091fc7afc0f318ad1","62efbe0a6abd0bcf53ab9ab29e7cd73f","4b62d9d2c248323ce029859f953fdc57","c81b0dc9d16eb825a7d6bce6e1b3678f","5ee240959c96b49d960702755478b9fc","7c9e07c96d144536525b1f889acee14d","dd7ccbc306692fc5ff5e61c22845da21","9a7e6be288b27ddbd3366c4ae41bbcd2","018a905cdbdff684760859f594d3fd77","af5741dad7aecf7960a129c3d2ae642a","6e1db473492095ccc2f1393d7259b9c0","fae9a1f2e7abefca2203765a3c7a5ba1","8d631eee88855ac59155edca2a3bc1ca","1ea6c0ac5ba60fe35bf63af8699b6fbe"]
+    }
+  ],
+  "is_hidden": false,
+  "last_modified_unix_timestamp": 1729805397
+}

src/user_templates_api/templates/jupyter_lab/templates/scalable_cell_populations/render.py

@@ -0,0 +1,6 @@
+from user_templates_api.templates.jupyter_lab.render import JupyterLabRender
+
+
+class JupyterLabExampleJinjaRender(JupyterLabRender):
+    def __init__(self):
+        pass

src/user_templates_api/templates/jupyter_lab/templates/scalable_cell_populations/template.txt

@@ -0,0 +1,60 @@
+[
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scalable Cell Population Viewer\n",
+    "This notebook shows how to load the scalable cell population viewer CellPop. It loads in datasets with annotated cell types and shows these in an interactive visualization.\n",
+    "\n",
+    "> This work is under active development and will be subject to change.\n",
+    "\n",
+    "The source code can be found [here](https://github.com/hms-dbmi/cellpop). Feel free to submit issues and feature requests [here](https://github.com/hms-dbmi/cellpop/issues). \n",
+    "\n",
+    "> For now, this tool has only been tested with snRNAseq [Salmon] datasets. Other assays may not yet work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install -i https://test.pypi.org/simple/ cellpop==0.0.2\n",
+    "# !pip install hubmap-template-helper\n",
+    "\n",
+    "from hubmap_template_helper import compatibility as hth_comp\n",
+    "from cellpop import Widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# linked datasets\n",
+    "uuids = {{ uuids | safe }}\n",
+    "\n",
+    "# accepted assay_display_names\n",
+    "accepted_assay_display_names=[\"snRNAseq [Salmon]\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uuids = hth_comp.check_template_compatibility(uuids, accepted_assay_display_names=accepted_assay_display_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Widget(uuids = uuids)"
+   ]
+  }
+ ]