New tutorial

docs/notebooks/tutorial.ipynb

@@ -34,9 +34,7 @@
     "import seaborn as sns\n",
     "import os\n",
     "\n",
-    "sys.path.append('../../monkeybread/src')\n",
-    "\n",
-    "import monkeybread as mb \n"
+    "import monkeybread as mb "
    ]
   },
   {
@@ -2160,7 +2158,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67e995fb",
+   "id": "193d5937",
    "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`. "
@@ -2169,7 +2167,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "fbd46d68",
+   "id": "2721c9d9",
    "metadata": {},
    "outputs": [
     {
@@ -2443,7 +2441,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4341976c",
+   "id": "347c6136",
    "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)."