add missing ME feature projections

Author: tmchartrand

python_scripts/README.md

@@ -13,6 +13,8 @@ All high-level analysis and figure code is presented in jupyter notebooks as fol
 
 `morpho_electro_feature_analysis.ipynb`: All remaining depth, electrophysiology, and morphology analysis from main paper, Figures 4-6.
 
+`morpho_electro_feature_projections.ipynb`: Electrophysiology and morphology feature visualization by SPCA and UMAP projections, Figure 4.
+
 `classifier_and_species_analysis.ipynb`: Additional Extended Data results including random forest classifier for t-types and cross-species comparison of electrophysiology.
 
 Additionally, code to calculate gene-depth correlations (very compute-intensive) is in `gene_depth_corr_calculation.py`. For convenience, results are included in data so there is no need to re-run this analysis.

python_scripts/morpho_electro_feature_analysis.ipynb

@@ -40,18 +40,8 @@
    "source": [
     "from hivscc_data import load_data, join_gene_data\n",
     "import hivscc_analysis as utils\n",
-    "human_df, mouse_df, ephys_df, morph_df = load_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# maybe do this in raw data?? check effects on Anatoly?\n",
+    "human_df, mouse_df, ephys_df, morph_df = load_data()\n",
+    "\n",
     "drop_neg = [\n",
     "    'input_resistance',\n",
     "    'fi_fit_slope',\n",

python_scripts/morpho_electro_feature_projections.ipynb

@@ -5,4 +5,6 @@ scikit-learn==0.22
 seaborn==0.9
 statsmodels==0.11
 umap-learn==0.3.8
+numba==0.43.1
+llvm-lite==0.28
 pyarrow