New tutorial. Fixed docs

docs/notebooks/tutorial.ipynb

@@ -921,7 +921,7 @@
    "id": "9d57a683",
    "metadata": {},
    "source": [
-    "# Niche analysis\n",
+    "## Niche analysis\n",
     "\n",
     "We now demonstrate how to perform an analysis of cellular niches. Specifically, for each cell, we count the number of neighbors that are of each cell type thus forming a \"neighborhood profile\" vector of length C, where C is the number of cell types. We then cluster all neighborhood profiles and call each cluster a \"niche\".\n",
     "\n",
@@ -1274,11 +1274,11 @@
    "id": "0f2ad69e",
    "metadata": {},
    "source": [
-    "# Comparing gene expression between niches\n",
+    "## Comparing gene expression between niches\n",
     "\n",
     "Next we'll show how the niche annotation can be further explored by examining changes in gene expression between these niches\n",
     "\n",
-    "## Visualizing gene expression differences\n",
+    "### Visualizing gene expression differences\n",
     "\n",
     "Below we zoom on a specific region of the specimen and color each cell by their expression of CXCL9 and CXCL10, two genes that are up-regulated in response to interferon."
    ]
@@ -1394,7 +1394,7 @@
    "id": "3d0c4eb8",
    "metadata": {},
    "source": [
-    "## Differential expression analysis between cells in different niches\n",
+    "### Differential expression analysis between cells in different niches\n",
     "\n",
     "Here we demonstrate how to use Scanpy to perform differential expression analysis between cells in different niches. Specifically, we will perform a DE analysis between T cells in Niches 4 and 2. We begin by isolating the T cells in these niches."
    ]
@@ -1492,9 +1492,9 @@
    "id": "23afca4e",
    "metadata": {},
    "source": [
-    "# Exploration  of co-localization between cell types\n",
+    "## Exploration  of co-localization between cell types\n",
     "\n",
-    "## Visualization of neighboring cells\n",
+    "### Visualization of neighboring cells\n",
     "\n",
     "Here we compute a mapping of each cell of a given cell type to all neighboring cells within a specified radius. Here we identify all CD4 T cell neighbors of each dendritic cell (mregDC). Notice, each argument is a list denoting a union of cell types. Here we identify all neighbors of mature regulatory DCs (mregDCs), conventional DCs, and pDCs that are CD4 T cells."
    ]
@@ -1887,7 +1887,7 @@
    "id": "4bc79fcb",
    "metadata": {},
    "source": [
-    "# Ligand-receptor analysis"
+    "## Ligand-receptor analysis"
    ]
   },
   {
@@ -2158,7 +2158,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "193d5937",
+   "id": "f55f230d",
    "metadata": {},
    "source": [
     "To summarize the results, we can plot the p-value vs. the score itself. To do so, we call `ligand_receptor_scatter` and pass the observed scores, `lr_pair_to_score`, and the results from the permutation test stored in `res`. "
@@ -2167,7 +2167,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "2721c9d9",
+   "id": "cfa21182",
    "metadata": {},
    "outputs": [
     {
@@ -2441,7 +2441,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "347c6136",
+   "id": "922c87c7",
    "metadata": {},
    "source": [
     "Lastly, we can plot the ligand-receptor co-expression score for multiple ligand receptors using a heatmap. By setting `z_score=True`, we plot the z-score normalized scores, where the z-score is computed for each ligand/receptor pair over the niches. The parameter `clip_z=2` clips the maximum and minimum z-scores to 2 and -2 (i.e., 2 and -2 standard deviations from the mean)."

src/monkeybread/calc/_ligand_receptor.py

@@ -90,13 +90,10 @@ def ligand_receptor_score_per_niche(
     """
      Calculates an average co-expression score of a
     ligand-receptor pair between neighboring cells within each cellular niche
-    calculated by :func:`monkeybread.calc.cellular_niches`.
-
-    Statistical test is as described in :cite:p:`He2021.11.03.467020` (See
-    Figure 4).
-
-    This function is a wrapper around :func:`monkeybread.calc.ligand_receptor_score`
-    and calls this function separately for each niche.
+    calculated by :func:`monkeybread.calc.cellular_niches`. Statistical test is as described 
+    in :cite:p:`He2021.11.03.467020` (See Figure 4). This function is a wrapper around 
+    :func:`monkeybread.calc.ligand_receptor_score` and calls this function separately for 
+    each niche.
 
     Parameters
     ----------
@@ -115,6 +112,7 @@ def ligand_receptor_score_per_niche(
     A dictionary mapping each niche to a sub-dictionary mapping each ligand-receptor 
     pair to its co-expression score.
     """
+
     niche_to_lr_pair_to_score = {}
     for niche, cell_to_neighbors in niche_to_cell_to_neighbors.items():