Add disclaimer about scirpy data structure update (theislab#228)

jupyter-book/air_repertoire/clonotype.ipynb

@@ -47,7 +47,15 @@
    "id": "3adf7e04",
    "metadata": {},
    "source": [
-    "Here, as well as in the pre-processing step, we will use the utilities from the *Scirpy* library to perform the analysis and locate the results in the *AnnData* object."
+    "Here, as well as in the pre-processing step, we will use the utilities from the *Scirpy* library to perform the analysis and locate the results in the *AnnData* object.\n",
+    "\n",
+    ":::{warning}\n",
+    "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n",
+    "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n",
+    "\n",
+    "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n",
+    "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n",
+    ":::"
    ]
   },
   {

jupyter-book/air_repertoire/ir_profiling.ipynb

@@ -199,7 +199,15 @@
     "- **Scanpy**: general package for single cell analysis (https://github.com/theislab/scanpy, {cite}`wolf2018scanpy`)\n",
     "- **Scirpy**: scanpy extension for immune receptor analysis (https://github.com/scverse/scirpy, {cite}`sturm2020scirpy`)\n",
     "\n",
-    "Here, we only showcase IR analysis with Scirpy. However, there exist several tools with similar functionality such as immunarch(R, {cite}`immunomind2019`), scRepertoire (R, {cite}`borcherding2020screpertoire`), and dandelion (R, {cite}`stephenson2021single`), and Platypus (R, {cite}`yermanos2021platypus`) reviewed in {cite}`valkiers2022recent`. \n"
+    "Here, we only showcase IR analysis with Scirpy. However, there exist several tools with similar functionality such as immunarch(R, {cite}`immunomind2019`), scRepertoire (R, {cite}`borcherding2020screpertoire`), and dandelion (R, {cite}`stephenson2021single`), and Platypus (R, {cite}`yermanos2021platypus`) reviewed in {cite}`valkiers2022recent`. \n",
+    "\n",
+    ":::{warning}\n",
+    "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n",
+    "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n",
+    "\n",
+    "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n",
+    "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n",
+    ":::\n"
    ]
   },
   {

jupyter-book/air_repertoire/multimodal_integration.ipynb

@@ -25,7 +25,15 @@
    "source": [
     "## Data Preparation\n",
     "\n",
-    "First, we will combine the GEX with AIR data. Note, that we can do this also at earlier stages of single cell analysis: e.g. we can fuse both modalities already before preprocessing to filter GEX doublets. When fusing GEX and AIR data, a left join is often performed. I.e., only cells with GEX are kept for analysis. Therefore, the order of fusing and filtering is not of great importance. However, it can be convenient to visualize the AIR information in an GEX UMAP in early stages. E.g. missing AIR information in clusters can help during cell type assignment."
+    "First, we will combine the GEX with AIR data. Note, that we can do this also at earlier stages of single cell analysis: e.g. we can fuse both modalities already before preprocessing to filter GEX doublets. When fusing GEX and AIR data, a left join is often performed. I.e., only cells with GEX are kept for analysis. Therefore, the order of fusing and filtering is not of great importance. However, it can be convenient to visualize the AIR information in an GEX UMAP in early stages. E.g. missing AIR information in clusters can help during cell type assignment.\n",
+    "\n",
+    ":::{warning}\n",
+    "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n",
+    "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n",
+    "\n",
+    "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n",
+    "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n",
+    ":::"
    ]
   },
   {

jupyter-book/air_repertoire/specificity.ipynb

@@ -41,7 +41,15 @@
     "\n",
     "CDR3β > V- and J-gene > CDR3α > MHC > cell type\n",
     "\n",
-    "We can assume that this importance is similar not only for prediction, but also for querying, clustering, and distance calculation."
+    "We can assume that this importance is similar not only for prediction, but also for querying, clustering, and distance calculation.\n",
+    "\n",
+    ":::{warning}\n",
+    "Scirpy changed the format of [its datastructure](https://scirpy.scverse.org/en/latest/data-structure.html#storing-airr-rearrangement-data-in-anndata)\n",
+    "with v0.13. While the overall anlaysis workflow has not changed, some outputs shown in this chapter might not be accurate anymore. \n",
+    "\n",
+    "See [the scirpy release notes](https://scirpy.scverse.org/en/latest/changelog.html#v0-13-0-new-data-structure-based-on-awkward-arrays) for more details about this change. \n",
+    "Until we update this chapter, please also refer to the [official scirpy documentation](https://scirpy.scverse.org).\n",
+    ":::"
    ]
   },
   {