Fix typos in annotation

Signed-off-by: zethson <[email protected]>

jupyter-book/cellular_structure/annotation.ipynb

@@ -22,11 +22,7 @@
     "So what is a cell type? Biologists use the term cell type to denote a cellular phenotype that is robust across datasets, identifiable based on expression of specific markers (i.e. proteins or gene transcripts), and often linked to specific functions. For example, a plasma B cell is a type of white blood cell that secretes antibodies used to fight pathogens and it can be identified using specific markers. Knowing which cell types are in your sample is essential in understanding your data. For example, knowing that there are specific immune cell types in a tumor or unusual hematopoietic stem cells in your bone marrow sample can be a valuable insight into the disease you might be studying.<br>\n",
     "However, like with any categorization the size of categories and the borders drawn between them are partly subjective and can change over time, e.g. because new technologies allow for a higher resolution view of cells, or because specific \"sub-phenotypes\" that were not considered biologically meaningful are found to have important biological implications (see e.g. {cite}`anno:KadurLakshminarasimhaMurthy2022`). Cell types are therefore often further classified into \"subtypes\" or \"cell states\" (e.g. activated versus resting) and some researchers use the term \"cell identity\" to avoid this sometimes arbitrary distinction of cell types, cell subtypes and cell states. For a more detailed discussion of this topic, we recommend the review by Wagner et al. {cite}`anno:Wagner2016` and the recently published review by Zeng {cite}`anno:ZENG20222739`.<br>\n",
     "Similarly, multiple cell types can be part of a single continuum, where one cell type might transition or differentiate into another. For example, in hematopoiesis cells differentiate from a stem cell into a specific immune cell type. Although hard borders between early and late stages of this differentiation are often drawn, the state of these cells can more accurately be described by the differentiation coordinate between the less and more differentiated cellular phenotypes. We will discuss differentiation and cellular trajectories in subsequent chapters.<br>\n",
-    "So how do we go about annotating cells in single-cell data? There are multiple ways to do it and we will give an overview of different approaches below. As we are working with transcriptomic data, each of these methods is ultimately based on the expression of specific genes or gene sets, or general transcriptomic similarity between cells. \n",
-    "\n",
-    "```{admonition} Warning\n",
-    "The scArches-based label transfer section of this notebook shows a bug in some environments with older scvi-tools and scanpy. We haven't figured out yet what exactly causes this. If you observe random label assignment during label transfer, make sure to update your packages and re-run. The bug has not been observed in environments with scanpy>=1.9.3, scarches>=0.5.8 and scvi-tools>=0.20.3.\n",
-    "```"
+    "So how do we go about annotating cells in single-cell data? There are multiple ways to do it and we will give an overview of different approaches below. As we are working with transcriptomic data, each of these methods is ultimately based on the expression of specific genes or gene sets, or general transcriptomic similarity between cells. "
    ]
   },
   {
@@ -738,7 +734,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Scrolling back up, you will see that cluster 4 and 5 are the clusters consistently expressing Naive CD20+ B cell markers. We can also visualize this using a dotplot:"
+    "Scrolling back up, you will see that cluster 4 and 6 are the clusters consistently expressing Naive CD20+ B cell markers. We can also visualize this using a dotplot:"
    ]
   },
   {
@@ -997,7 +993,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's take a look at cluster 12, which seems to have a set of relatively unique markers including CDK6, ETV6, NKAIN2, and GNAQ. Some googling tells us that NKAIN2 and ETV6 are hematopoietic stem cell markers {cite}`anno:SHI20222234` {cite}`anno:Wang1998-rx` (NKAIN2 was also present in our list above). In the UMAP we can see that these clusters are expressed throughout cluster 12: "
+    "Let's take a look at cluster 12, which seems to have a set of relatively unique markers including CDK6, ETV6, NKAIN2, and GNAQ. Some googling tells us that NKAIN2 and ETV6 are hematopoietic stem cell markers {cite}`anno:SHI20222234` {cite}`anno:Wang1998-rx` (NKAIN2 was also present in our list above). In the UMAP we can see that these genes are expressed throughout cluster 12: "
    ]
   },
   {