fix typos

jupyter-book/cellular_structure/annotation.ipynb

@@ -406,7 +406,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now show expression of the markers using the calculated UMAP. We'll limit ourselves to B/plasma cell subtypes for this example. Note from the marker dictionary above that there are three negative markers in our list: IGHD and IGHM for B1 B, and PAX5 for plasmablasts, or meaning that this cell type is expected not to or to lowly express those markers."
+    "Now show expression of the markers using the calculated UMAP. We'll limit ourselves to B/plasma cell subtypes for this example. Note from the marker dictionary above that there are three negative markers in our list: IGHD and IGHM for B1 B, and PAX5 for plasmablasts, meaning that this cell type is expected not to or to lowly express those markers."
    ]
   },
   {
@@ -1146,7 +1146,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The aforementioned points highlight possible disadvantages of using classifiers, depending on the training data and model type. Nonetheless, there are several important advantages of using pre-trained classifiers to annotate your data. First, it is a fast and and easy way to annotate your data. The annotation does not require the downloading nor preprocessing of the training data and sometimes merely involves the upload of your data to an online webpage. Second, these methods don't rely on a partitioning of your data into clusters, as the manual annotation does. Third, pre-trained classifiers enable you to directly leverage the knowledge and information from previous studies, such as a high quality annotation. And finally, using such classifiers can help with harmonizing cell-type definitions across a field, thereby clearing the path towards a field-wide consensus on these definitions. "
+    "The aforementioned points highlight possible disadvantages of using classifiers, depending on the training data and model type. Nonetheless, there are several important advantages of using pre-trained classifiers to annotate your data. First, it is a fast and easy way to annotate your data. The annotation does not require the downloading nor preprocessing of the training data and sometimes merely involves the upload of your data to an online webpage. Second, these methods don't rely on a partitioning of your data into clusters, as the manual annotation does. Third, pre-trained classifiers enable you to directly leverage the knowledge and information from previous studies, such as a high quality annotation. And finally, using such classifiers can help with harmonizing cell-type definitions across a field, thereby clearing the path towards a field-wide consensus on these definitions. "
    ]
   },
   {