Update tutorial

docs/references.bib

@@ -136,7 +136,7 @@ @article{Schramm.2018
 
 
 
-@article{null.2022,
+@article{TabulaSapiens.2022,
 author = {{Robert C. Jones} and {Jim Karkanias} and {Mark A. Krasnow} and {Angela Oliveira Pisco} and {Stephen R. Quake} and {Julia Salzman} and {Nir Yosef} and {Bryan Bulthaup} and {Phillip Brown} and {William Harper} and {Marisa Hemenez} and {Ravikumar Ponnusamy} and {Ahmad Salehi} and {Bhavani A. Sanagavarapu} and {Eileen Spallino} and {Ksenia A. Aaron} and {Waldo Concepcion} and {James M. Gardner} and {Burnett Kelly} and {Nikole Neidlinger} and others},
  year = {2022},
  title = {The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans},

docs/tutorials/tutorial_5k_bcr.ipynb

@@ -54,10 +54,9 @@
    "id": "bfc24541",
    "metadata": {},
    "source": [
-    "This tutorial should serve as a guideline for any user to perform single cell B-cell receptor analysis with `Scirpy`.\n",
-    "However, while Scirpy has become a versatile and well-established tool for TCR {cite}`null.2022`, it was not applicable to do meaningful analysis of BCR.\n",
+    "In this tutorial, we guide the user through single cell {term}`BCR <B cell receptor (BCR)>` analysis with `Scirpy`. The functionality in this notebook requires Scirpy v0.19 or later. Additionally, we leverage the interoperability with Dandelion {cite}`Suo.2023` for certain preprocessing steps. \n",
     "\n",
-    "In this regard, we extended relevant `Scirpy` functionalities and also leveraged the interoperability with Dandelion {cite}`Suo.2023`. In this notebook, we showcase a minimal working example by re-analysing single-cell BCR/RNA data from Stephenson et al. {cite}`Stephenson2021`. The original dataset consists of 143 samples and > 60k B cells from patients with COVID-19 in different degrees of severity and three control groups. To simplify and speed up the computation, we included cells from the five most abundant COVID-19 positive samples per status category and randomly subsampled down to 5k."
+    "This tutorial uses a dataset from Stephenson et al. {cite}`Stephenson2021`. The original dataset consists of 143 samples and > 60k B cells from patients with COVID-19 in different degrees of severity and three control groups. To simplify and speed up the computation, we included cells from the five most abundant COVID-19 positive samples per status category and randomly subsampled down to 5k."
    ]
   },
   {
@@ -86,7 +85,9 @@
     "## Pre-processing/Re-annotation\n",
     "Unlike TCR data, it is not recommended to directly use the output of Cell Ranger while analysing scBCR-data. Best practice is to re-annotate Cell Ranger gene annotation with IgBlast or IMGT/HighV-QUEST, because Cell Ranger currently does not support the IMGT unique numbering scheme {cite}`Lefranc.2003`, which is heavily used by many downstream analysis tools e.g. phylogenetic analysis and mutation inference.\n",
     "\n",
-    "Our data used in this notebook is already re-annotated and pre-processed and cells have already passed transcriptome quality control as described [by the original authors](https://www.nature.com/articles/s41591-021-01329-2#Sec8). This tutorial does not present an exemplary workflow on how to re-annotate contigs based on Cell Ranger fasta and annotation files, as this is not part of the `scirpy` functionalities (yet). However, we highly recommend to use Dandelion {cite}`Suo.2023` for this crucial step as described in a great [tutorial on their website](https://sc-dandelion.readthedocs.io/en/latest/notebooks/1_dandelion_preprocessing-10x_data.html#) Note, that `Scirpy` offers two functions ({func}`scirpy.io.from_dandelion`and {func}`scirpy.io.to_dandelion`), that allow for convenient transformation between `Dandelion class` objects and `AnnData` objects. Pre-processing of Cell Ranger output with more customisation potential offers the [Immcantation suite](https://immcantation.readthedocs.io/en/stable/index.html) and a comprehensive tutorial is presented [on their website](https://immcantation.readthedocs.io/en/stable/getting_started/10x_tutorial.html). Re-annotation will finally result into a AIRR-C Format `.tsv` file, which can be loaded into Scirpy for further use."
+    "Our data used in this notebook is already re-annotated and pre-processed and cells have already passed transcriptome quality control as described [by the original authors](https://www.nature.com/articles/s41591-021-01329-2#Sec8). This tutorial does not present an exemplary workflow on how to re-annotate contigs based on Cell Ranger fasta and annotation files, as this functionality is beyond the scope of `Scirpy`. Instead, we recommend to use Dandelion {cite}`Suo.2023` for this step as described in their [tutorial on their website](https://sc-dandelion.readthedocs.io/en/latest/notebooks/1_dandelion_preprocessing-10x_data.html#). Note, that `Scirpy` offers two functions ({func}`scirpy.io.from_dandelion`and {func}`scirpy.io.to_dandelion`), that allow for convenient transformation between `Dandelion class` objects and `AnnData` objects.\n",
+    "\n",
+    "Alternatively, it is possible to pre-processing of Cell Ranger output with the [Immcantation suite](https://immcantation.readthedocs.io/en/stable/index.html) and a comprehensive tutorial is presented [on their website](https://immcantation.readthedocs.io/en/stable/getting_started/10x_tutorial.html). Re-annotation will finally result into a AIRR-C Format `.tsv` file, which can be loaded into Scirpy using {func}`scirpy.io.read_airr` for further use."
    ]
   },
   {
@@ -95,9 +96,7 @@
    "metadata": {},
    "source": [
     "### Germline reconstruction\n",
-    "After pre-processing, one might want to include information about the original (unmutated) germline sequence. While it is not possible to infer germline sequences with `Scirpy`at this point, users are encouraged to again utilize the interoperability with `Dandelion` to this extent, as explained in a [respective tutorial on their website](https://sc-dandelion.readthedocs.io/en/latest/notebooks/5_dandelion_diversity_and_mutation-10x_data.html).\n",
-    "\n",
-    "Inferring the germline sequences is currently only necessary for calculating mutations and is covered later in this tutorial. However, there are other interesting use-cases outside the (current) scope of `Scirpy` i.e. phylogenetic analysis."
+    "Germline sequences, primarily required for calculating mutations, can enrich your pre-processed data. Although `Scirpy` does not currently support germline sequence inference, it's recommended to leverage its interoperability with `Dandelion` for this purpose, as detailed in this [tutorial](https://sc-dandelion.readthedocs.io/en/latest/notebooks/5_dandelion_diversity_and_mutation-10x_data.html) on their website. This tutorial will later cover mutation calculation. Note that germline sequences can also be useful for other applications, like phylogenetic analysis, which are beyond the current scope of `Scirpy`."
    ]
   },
   {
@@ -106,7 +105,7 @@
    "metadata": {},
    "source": [
     "## Importing data\n",
-    "Data import into `Scirpy` is very easy and possible from various formats, as described in an own {ref}`dedicated tutorial on data loading <importing-data>`."
+    "Data import into `Scirpy` from various formats is straightforward, as described in the {ref}`dedicated tutorial on data loading <importing-data>`. In this case, we directly load the demo dataset: "
    ]
   },
   {
@@ -125,6 +124,7 @@
     }
    ],
    "source": [
+    "# TODO make demo dataset available\n",
     "mdata = mu.read_h5mu(\"stephenson_5K_2021.h5mu\")\n",
     "\n",
     "# minimal working example importing data from Cell Ranger:\n",