Add suggested intro text.

AllenInstitute · Aug 5, 2024 · 65db4b8 · 65db4b8
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diff --git a/descriptions/MERFISH-C57BL6J-638850-imputed.md b/descriptions/MERFISH-C57BL6J-638850-imputed.md
@@ -1,9 +1,13 @@
-# Imputed gene MERFISH spatial transcriptomics  dataset of a single adult mouse brain
+# Imputed MERFISH spatial transcriptomics of a single adult mouse brain
 
-TO FILL IN
-
-The expression matrices and associated metadata is hosted on AWS S3 bucket as a
-AWS Public Dataset:
+The MERFISH spatial transcriptomics dataset (MERFISH-C57BL6J-638850) was mapped
+to the whole mouse brain taxonomy (WMB-taxonomy) using a hierarchical
+correlation method. To further integrate the transcriptomics and spatial
+profiles of each cell type, 10Xv3 expression was projected or imputed into the
+MERFISH space. The basic idea is to compute the k-nearest neighbors (KNNs)
+among the 10Xv3 cells for each MERFISH cell and use the average expression of
+these neighbors for each gene as the impute values. Further details can be
+found in the methods section of Yao et. al.
 
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diff --git a/notebooks/merfish_imputed_genes_example.ipynb b/notebooks/merfish_imputed_genes_example.ipynb
@@ -22,32 +22,15 @@
    "source": [
     "# MERFISH whole brain spatial transcriptomics with imputed genes\n",
     "\n",
-    "Based of the MERFISH 2b tutotiral found [here](https://alleninstitute.github.io/abc_atlas_access/notebooks/merfish_tutorial_part_2b.html).\n",
+    "MERFISH whole brain spatial transcriptomics cells with imputed genes\n",
     "\n",
-    "We repoduce and slightly modify the above tutorial this time using the a set of imputed genes on the same spatial data. These imputed genes were infered from the 10X data by finding the cells in that share expression similar to the MERFISH data over the 500 genes measured there. The user should note that this notebook requires a large download to process the imputed merfish genes. NOTE: Currently you'll have grab the files yourself as they are not currently part of the abc_atlas_access package (but will be as part fothe next release).\n",
+    "In the MERFISH tutorial (part 1), we discussed how each set form the MERFISH data was mapped to the whole mouse brain taxonomy using a hierarchical correlation method. To further integrate the transcriptomics and spatial profiles of each cell type, 10Xv3 expression was projected or imputed into the MERFISH space. The basic idea is to compute the k-nearest neighbors (KNNs) among the 10Xv3 cells for each MERFISH cell and use the average expression of these neighbors for each gene as the impute values. Further details can be found in the methods section of Yao et. al.\n",
+    "\n",
+    "In this notebook, we will show the basics on how to access the imputed gene data and perform some illustrative comparison between expression 10X, MERFISH measured and impute expression similar to Extended Data Fig 8 in the manuscript.\n",
     "\n",
     "You need to be connected to the internet to run this notebook and that you have downloaded the example data via the [getting started notebook](https://alleninstitute.github.io/abc_atlas_access/notebooks/getting_started.html)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: pyarrow in /Users/chris.morrison/src/miniconda3/envs/abc_atlas_access/lib/python3.11/site-packages (17.0.0)\n",
-      "Requirement already satisfied: numpy>=1.16.6 in /Users/chris.morrison/src/miniconda3/envs/abc_atlas_access/lib/python3.11/site-packages (from pyarrow) (2.0.0)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pip install pyarrow"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,