batchMetrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0b7f2c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scib\n",
    "import scanpy\n",
    "import anndata as ad\n",
    "import os\n",
    "import pickle\n",
    "import pickle5\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "15a7d293",
   "metadata": {},
   "outputs": [],
   "source": [
    "name='allk20XA_02_dca_over'\n",
    "modelsavepath='/mnt/external_ssd/xinyi/models/train_gae_starmap/'+name\n",
    "plotsavepath='/mnt/external_ssd/xinyi/plots/train_gae_starmap/'+name\n",
    "datadir='/home/xinyiz/2021-01-13-mAD-test-dataset'\n",
    "plotepoch=9990\n",
    "inverseAct='leakyRelu'\n",
    "n_neighbors=10\n",
    "min_dist=0.25\n",
    "n_pcs=40 #for clustering\n",
    "resolution=0.1\n",
    "savenameAdd=''\n",
    "plot_sample_X=['logminmax']\n",
    "plot_samples={'disease13':'AD_mouse9494','control13':'AD_mouse9498','disease8':'AD_mouse9723','control8':'AD_mouse9735'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a81f9d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/h5ad.py:238: OldFormatWarning: Element '/X' was written without encoding metadata.\n",
      "  d[k] = read_elem(f[k])\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/orig_index' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/sample' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/sample' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/area' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/tau' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/x' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/y' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/batch' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/batch' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/time' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/time' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/group' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/group' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/n_genes_by_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/log1p_n_genes_by_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/total_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/log1p_total_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/n_genes' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/n_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/leiden' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/__is_in_cluster__' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/top_level' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/top_level' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/cell_type' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/cell_type' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/region' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/region' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/cell_type_label' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/cell_type_label' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/_index' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/h5ad.py:238: OldFormatWarning: Element '/uns' was written without encoding metadata.\n",
      "  d[k] = read_elem(f[k])\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9494_morph' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9494_morph/label_img' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9494_morph/plaque' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9494_morph/region' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9494_morph/tau' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9498_morph' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9498_morph/label_img' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9498_morph/plaque' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9498_morph/region' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9498_morph/tau' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9723_morph' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9723_morph/label_img' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9723_morph/plaque' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9723_morph/region' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9723_morph/tau' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9735_morph' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9735_morph/label_img' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9735_morph/plaque' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9735_morph/region' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/AD_mouse9735_morph/tau' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/cell_type_label' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/cell_type_order' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/A1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/A2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/A3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/CA1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/CA2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/CA3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/CTX-Ex1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/CTX-Ex2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/CTX-Ex3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/CTX-Ex4' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/DG' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/E1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/E2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/E3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/In1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/In2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/In3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/In4' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/LHb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/M1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/M2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/M3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/O1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/O2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/OL1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/OL2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/OL3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/S1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/S2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/S3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/S4' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_hex_dict/S5' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/A1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/A2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/A3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/CA1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/CA2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/CA3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/CTX-Ex1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/CTX-Ex2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/CTX-Ex3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/CTX-Ex4' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/DG' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/E1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/E2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/E3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/In1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/In2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/In3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/In4' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/LHb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/M1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/M2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/M3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/O1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/O2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/OL1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/OL2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/OL3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/S1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/S2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/S3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/S4' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/label_rgb_dict/S5' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Astro' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Astro_Cst3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Astro_Gfap' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Astro_Gfap/Vim' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/CA1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/CA2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/CA3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/DG' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Endo' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Endo_Cldn5+' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Endo_Esam+' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Inhi_Cnr1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Inhi_Lamp5' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Inhi_Pvalb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Inhi_Sst' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/L2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/L2/3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/L4' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/L4/5_RSC_A' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/L4/5_RSC_B' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/L6' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/LHb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Micro' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Micro_Cst7' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Micro_Cst7/Ctsb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Micro_Gpr34' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/OPC' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/OPC_Gpr17' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Oligo' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Oligo_Cldn11' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Oligo_Klk6' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Peri_A' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/Peri_B' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/SMC' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/VLMC_A' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_hex_dict/VLMC_B' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Astro' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Astro_Cst3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Astro_Gfap' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Astro_Gfap/Vim' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/CA1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/CA2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/CA3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/DG' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Endo' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Endo_Cldn5+' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Endo_Esam+' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Inhi_Cnr1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Inhi_Lamp5' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Inhi_Pvalb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Inhi_Sst' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/L2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/L2/3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/L4' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/L4/5_RSC_A' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/L4/5_RSC_B' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/L6' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/LHb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Micro' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Micro_Cst7' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Micro_Cst7/Ctsb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Micro_Gpr34' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/OPC' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/OPC_Gpr17' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Oligo' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Oligo_Cldn11' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Oligo_Klk6' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Peri_A' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/Peri_B' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/SMC' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/VLMC_A' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/sub_rgb_dict/VLMC_B' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/Astro' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/CA1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/CA2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/CA3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/DG' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/Endo' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/Ex' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/Inhi' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/LHb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/Micro' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/OPC' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/Oligo' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_hex_dict/SMC' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_level_order' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/Astro' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/CA1' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/CA2' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/CA3' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/DG' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/Endo' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/Ex' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/Inhi' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/LHb' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/Micro' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/OPC' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/Oligo' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/uns/top_rgb_dict/SMC' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/n_cells_by_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/mean_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/log1p_mean_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/pct_dropout_by_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/total_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/log1p_total_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/max_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/n_cells' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/mean' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/std' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/_index' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n"
     ]
    }
   ],
   "source": [
    "#loading data for overparam 6000\n",
    "#load latent\n",
    "with open(os.path.join(modelsavepath,str(plotepoch)), 'rb') as output:\n",
    "    mulist=pickle5.load(output)\n",
    "#load data\n",
    "scaleddata=scanpy.read_h5ad(datadir+'/2020-12-27-starmap-mAD-raw.h5ad')\n",
    "\n",
    "xcorr = plot_sample_X[0]\n",
    "#load leiden clusters--change\n",
    "sampledir=os.path.join(plotsavepath,'combined'+xcorr)\n",
    "if inverseAct:\n",
    "    sampledir+='_beforeAct'\n",
    "clustersavedir=os.path.join(sampledir,'cluster')\n",
    "savenamecluster='leiden_nn'+str(n_neighbors)+'mdist0'+str(int(min_dist*100))+'n_pcs'+str(n_pcs)+'res'+str(resolution)+'epoch'+str(plotepoch)\n",
    "with open(os.path.join(clustersavedir,savenamecluster), 'rb') as output:\n",
    "    clusterRes=pickle5.load(output)\n",
    "\n",
    "latents=None\n",
    "celltype_broad=None\n",
    "celltype_sub=None\n",
    "region=None\n",
    "samplenameList=None\n",
    "sobj_coord_np=None\n",
    "\n",
    "for s in plot_samples.keys():\n",
    "    sampleidx=plot_samples[s]        \n",
    "    samplename=s+'X_'+xcorr\n",
    "    muplot=np.copy(mulist[samplename])\n",
    "\n",
    "    if latents is None:\n",
    "        latents=muplot\n",
    "        celltype_broad=scaleddata.obs.loc[scaleddata.obs['sample']==sampleidx,'top_level']\n",
    "        celltype_sub=scaleddata.obs.loc[scaleddata.obs['sample']==sampleidx,'cell_type_label']\n",
    "        region=scaleddata.obs.loc[scaleddata.obs['sample']==sampleidx,'region']\n",
    "        sobj_coord_np=scaleddata.obs.loc[scaleddata.obs['sample']==sampleidx,['x','y']].to_numpy()\n",
    "        samplenameList=np.repeat(s,muplot.shape[0])\n",
    "    else:\n",
    "        latents=np.vstack((latents,muplot))\n",
    "        celltype_broad=np.concatenate((celltype_broad,scaleddata.obs.loc[scaleddata.obs['sample']==sampleidx,'top_level']),axis=None)\n",
    "        celltype_sub=np.concatenate((celltype_sub,scaleddata.obs.loc[scaleddata.obs['sample']==sampleidx,'cell_type_label']),axis=None)\n",
    "        region=np.concatenate((region,scaleddata.obs.loc[scaleddata.obs['sample']==sampleidx,'region']),axis=None)\n",
    "        sobj_coord_np=np.concatenate((sobj_coord_np,scaleddata.obs.loc[scaleddata.obs['sample']==sampleidx,['x','y']].to_numpy()),axis=0)\n",
    "        samplenameList=np.concatenate((samplenameList,np.repeat(s,muplot.shape[0])),axis=None)\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d18b62a",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_over=ad.AnnData(latents)\n",
    "adata_over.obs['celltype_broad']=celltype_broad\n",
    "adata_over.obs['leiden']=clusterRes\n",
    "adata_over.obs['samples']=samplenameList\n",
    "scanpy.tl.pca(adata_over, svd_solver='arpack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4658fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scib.metrics.kBET(adata_over,'samples','leiden',verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6610859f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean silhouette per group:        silhouette_score\n",
      "group                  \n",
      "0              0.948467\n",
      "1              0.921189\n",
      "2              0.893706\n",
      "3              0.920813\n",
      "4              0.914021\n",
      "5              0.910687\n",
      "6              0.875364\n",
      "7              0.901802\n"
     ]
    }
   ],
   "source": [
    "over01=scib.metrics.silhouette_batch(adata_over,'samples','leiden','X_pca',verbose=True,return_all=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "033603f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean silhouette per group:        silhouette_score\n",
      "group                  \n",
      "Astro          0.960838\n",
      "CA1            0.868374\n",
      "CA2            0.919746\n",
      "CA3            0.881051\n",
      "DG             0.904354\n",
      "Endo           0.960684\n",
      "Ex             0.948401\n",
      "Inhi           0.944683\n",
      "LHb            0.843512\n",
      "Micro          0.925539\n",
      "OPC            0.954621\n",
      "Oligo          0.939662\n",
      "SMC            0.938514\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9223061525063011"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scib.metrics.silhouette_batch(adata_over,'samples','celltype_broad','X_pca',verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b2cb996",
   "metadata": {},
   "outputs": [],
   "source": [
    "savenamecluster='leiden_nn'+str(n_neighbors)+'mdist0'+str(int(min_dist*100))+'n_pcs'+str(n_pcs)+'res'+str(0.2)+'epoch'+str(plotepoch)\n",
    "with open(os.path.join(clustersavedir,savenamecluster), 'rb') as output:\n",
    "    clusterRes015=pickle5.load(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e372e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_over.obs['leiden02']=clusterRes015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab876662",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean silhouette per group:        silhouette_score\n",
      "group                  \n",
      "0              0.885147\n",
      "1              0.914869\n",
      "10             0.836897\n",
      "2              0.900399\n",
      "3              0.908247\n",
      "4              0.938657\n",
      "5              0.898260\n",
      "6              0.859923\n",
      "7              0.931271\n",
      "8              0.899674\n",
      "9              0.918819\n"
     ]
    }
   ],
   "source": [
    "over02=scib.metrics.silhouette_batch(adata_over,'samples','leiden02','X_pca',verbose=True,return_all=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "14eff58c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading data for underparam 1024\n",
    "#load latent\n",
    "with open('/mnt/external_ssd/xinyi/models/server1/allk20XA_01_dca_9990', 'rb') as output:\n",
    "    mulist=pickle.load(output)\n",
    "\n",
    "xcorr = plot_sample_X[0]\n",
    "#load leiden clusters--change\n",
    "savenamecluster='leiden_nn'+str(n_neighbors)+'mdist0'+str(int(min_dist*100))+'n_pcs'+str(n_pcs)+'res'+str(resolution)+'epoch'+str(plotepoch)\n",
    "with open('/mnt/external_ssd/xinyi/models/server1/allk20XA_01_dca_'+savenamecluster, 'rb') as output:\n",
    "    clusterRes_under=pickle.load(output)\n",
    "\n",
    "latents_under=None\n",
    "\n",
    "for s in plot_samples.keys():\n",
    "    sampleidx=plot_samples[s]        \n",
    "    samplename=s+'X_'+xcorr\n",
    "    muplot=np.copy(mulist[samplename])\n",
    "\n",
    "    if latents_under is None:\n",
    "        latents_under=muplot\n",
    "    else:\n",
    "        latents_under=np.vstack((latents_under,muplot))\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "04fd0102",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_under=ad.AnnData(latents_under)\n",
    "adata_under.obs['celltype_broad']=celltype_broad\n",
    "adata_under.obs['leiden']=clusterRes_under\n",
    "adata_under.obs['samples']=samplenameList\n",
    "scanpy.tl.pca(adata_under, svd_solver='arpack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d536988c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean silhouette per group:        silhouette_score\n",
      "group                  \n",
      "0              0.906686\n",
      "1              0.850256\n",
      "10             0.728849\n",
      "11             0.905255\n",
      "12             0.815378\n",
      "13             0.917969\n",
      "14             0.822960\n",
      "15             0.866289\n",
      "2              0.865573\n",
      "3              0.947725\n",
      "5              0.817211\n",
      "6              0.787213\n",
      "7              0.882489\n",
      "9              0.924031\n"
     ]
    }
   ],
   "source": [
    "under01=scib.metrics.silhouette_batch(adata_under,'samples','leiden','X_pca',verbose=True,return_all=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ab2656d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean silhouette per group:        silhouette_score\n",
      "group                  \n",
      "Astro          0.972214\n",
      "CA1            0.928912\n",
      "CA2            0.916906\n",
      "CA3            0.946624\n",
      "DG             0.955760\n",
      "Endo           0.978988\n",
      "Ex             0.959318\n",
      "Inhi           0.970845\n",
      "LHb            0.887076\n",
      "Micro          0.967517\n",
      "OPC            0.967947\n",
      "Oligo          0.967494\n",
      "SMC            0.952674\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9517132874131841"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scib.metrics.silhouette_batch(adata_under,'samples','celltype_broad','X_pca',verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1fec5728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.90668641],\n",
       "       [0.85025597],\n",
       "       [0.72884874],\n",
       "       [0.90525519],\n",
       "       [0.8153777 ],\n",
       "       [0.917969  ],\n",
       "       [0.82296014],\n",
       "       [0.86628862],\n",
       "       [0.86557279],\n",
       "       [0.94772499],\n",
       "       [0.81721088],\n",
       "       [0.78721343],\n",
       "       [0.8824886 ],\n",
       "       [0.92403086]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "under01[1].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7e780fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [10, 5]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "plt.boxplot([under01[1].to_numpy().flatten(),over01[1].to_numpy().flatten(),over02[1].to_numpy().flatten()],labels=[\"underparameterization_0.1\", \"overparameterization_0.1\",\"overparameterization_0.2\"])\n",
    "plt.xticks(fontsize=15)\n",
    "plt.ylabel('silhouette')\n",
    "\n",
    "plt.scatter(np.random.normal( 1, 0.04, under01[1].size),under01[1].to_numpy().flatten())\n",
    "plt.scatter(np.random.normal( 2, 0.04, over01[1].size),over01[1].to_numpy().flatten())\n",
    "plt.scatter(np.random.normal( 3, 0.04, over02[1].size),over02[1].to_numpy().flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "53906d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def entropyBatch(dataset, batch, nsample=100, ncell=100, k=50):\n",
    "    if dataset.shape[0]<k/2:\n",
    "        return -1\n",
    "    entropy=0\n",
    "    np.random.seed(3)\n",
    "    batchID=np.unique(batch)\n",
    "    nbrs=NearestNeighbors(n_neighbors=k+1).fit(dataset)\n",
    "    for b in range(nsample):\n",
    "        randomIdx=np.random.choice(dataset.shape[0],min(ncell,dataset.shape[0]),replace=False)\n",
    "        distances, indices = nbrs.kneighbors(dataset[randomIdx])\n",
    "        for i in range(ncell):\n",
    "            _,counts=np.unique(batch[indices[i,1:]],return_counts=True)\n",
    "            entropy+=scipy.stats.entropy(counts/np.sum(counts),base=2)\n",
    "    return entropy/(nsample*ncell)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6e0d103e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7931948431022469"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "under01E=entropyBatch(latents_under,adata_under.obs['samples'])\n",
    "under01E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3514197b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1189530815713182"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overE=entropyBatch(latents,adata_over.obs['samples'])\n",
    "overE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a5516c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "under01E_byClusters=np.zeros(np.unique(adata_under.obs['leiden']).size)\n",
    "for i in range(under01E_byClusters.size):\n",
    "    c=np.unique(adata_under.obs['leiden'])[i]\n",
    "    under01E_byClusters[i]=entropyBatch(latents_under[adata_under.obs['leiden'].to_numpy()==c],adata_under.obs['samples'].to_numpy()[adata_under.obs['leiden'].to_numpy()==c])\n",
    "    \n",
    "under01E_byClusters=under01E_byClusters[under01E_byClusters!=-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "41c23605",
   "metadata": {},
   "outputs": [],
   "source": [
    "over01E_byClusters=np.zeros(np.unique(adata_over.obs['leiden']).size)\n",
    "for i in range(over01E_byClusters.size):\n",
    "    c=np.unique(adata_over.obs['leiden'])[i]\n",
    "    over01E_byClusters[i]=entropyBatch(latents[adata_over.obs['leiden'].to_numpy()==c],adata_over.obs['samples'].to_numpy()[adata_over.obs['leiden'].to_numpy()==c])\n",
    "    \n",
    "over01E_byClusters=over01E_byClusters[over01E_byClusters!=-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f5205f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "over02E_byClusters=np.zeros(np.unique(adata_over.obs['leiden02']).size)\n",
    "for i in range(over02E_byClusters.size):\n",
    "    c=np.unique(adata_over.obs['leiden02'])[i]\n",
    "    over02E_byClusters[i]=entropyBatch(latents[adata_over.obs['leiden02'].to_numpy()==c],adata_over.obs['samples'].to_numpy()[adata_over.obs['leiden02'].to_numpy()==c])\n",
    "    \n",
    "over02E_byClusters=over02E_byClusters[over02E_byClusters!=-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0c9bd52f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [10, 5]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "plt.boxplot([under01E_byClusters,over01E_byClusters,over02E_byClusters],labels=[\"underparameterization_0.1\", \"overparameterization_0.1\",\"overparameterization_0.2\"])\n",
    "plt.xticks(fontsize=15)\n",
    "plt.ylabel('entropy')\n",
    "\n",
    "plt.scatter(np.random.normal( 1, 0.04, under01E_byClusters.size),under01E_byClusters)\n",
    "plt.scatter(np.random.normal( 2, 0.04, over01E_byClusters.size),over01E_byClusters)\n",
    "plt.scatter(np.random.normal( 3, 0.04, over02E_byClusters.size),over02E_byClusters)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81c0fbf",
   "metadata": {},
   "source": [
    "## scRNAseq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "35669d2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('/mnt/external_ssd/xinyi/models/train_gae_scrnaseq/allk20XA_02_dca_over_FCXonly_scrnaseq_Bone_Marrow_latent', 'rb') as output:\n",
    "    latents_scrnaseq=pickle5.load(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2e6a9e31",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/h5ad.py:238: OldFormatWarning: Element '/layers' was written without encoding metadata.\n",
      "  d[k] = read_elem(f[k])\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/layers/counts' was written without encoding metadata.\n",
      "  return {k: read_elem(v) for k, v in elem.items()}\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/batch' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/batch' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/chemistry' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/chemistry' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/data_type' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/data_type' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/dpt_pseudotime' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/final_annotation' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/final_annotation' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/mt_frac' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/n_counts' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/n_genes' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/sample_ID' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/sample_ID' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/size_factors' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/species' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/species' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/study' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/study' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/tissue' was written without encoding metadata.\n",
      "  categories = read_elem(categories_dset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/tissue' was written without encoding metadata.\n",
      "  read_elem(dataset), categories, ordered=ordered\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/index' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n",
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/index' was written without encoding metadata.\n",
      "  return read_elem(dataset)\n"
     ]
    }
   ],
   "source": [
    "scrnaseqdata=scanpy.read_h5ad('/mnt/external_ssd/xinyi/staci_validation/scrnaseq/Immune_ALL_human.h5ad')\n",
    "scrnaseqdata=scrnaseqdata[:,np.array(np.sum(scrnaseqdata.layers['counts'],axis=0)>3).flatten()]\n",
    "data=scrnaseqdata[scrnaseqdata.obs['tissue']=='Bone_Marrow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6b6fe815",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "adata_scrnaseq=ad.AnnData(latents_scrnaseq)\n",
    "adata_scrnaseq.obs['samples']=data.obs['batch'].to_numpy()\n",
    "scanpy.tl.pca(adata_scrnaseq, svd_solver='arpack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e2301f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "scanpy.tl.pca(scrnaseqdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b650b075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8242635629003753"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropyBatch(latents_scrnaseq,adata_scrnaseq.obs['samples'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c6082049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7029334002169173"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropyBatch(data.layers['counts'],adata_scrnaseq.obs['samples'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5439f224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5777697197537487"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropyBatch(latents_scrnaseq,data.obs['final_annotation'].to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "951477a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7678285083982911"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropyBatch(data.layers['counts'],data.obs['final_annotation'].to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de89ea2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scanpy.tl.pca(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8b38c923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [20, 5]\n",
    "scanpy.pl.pca(data,color='final_annotation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e3e43c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [10, 5]\n",
    "scanpy.pl.pca(data,color='batch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "af286faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scanpy.pl.pca_variance_ratio(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d803b629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 30 artists>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFgCAYAAACmDI9oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAd9klEQVR4nO3df7RdZ13n8fdnKE3SSZMUYSCljp3KIHRRVjq2ddF2aeXHdCE/BqllHEsAFRiZhb/4IWDB6sB0tSy1BQXtahUMisSiRLBTkbRWJxacVUqAVhFEQ9GkA1STkDZpqP3OH3tfeXpzbu65955z7mnu+7XWXfveZz9772+zu28+ec6z905VIUmSJKnzb5a7AEmSJGmaGJAlSZKkhgFZkiRJahiQJUmSpMZxy13AfJI8QBfk9y93LZIkSTpmrAMerKoj8nCm/SkWSR4Esn79+uUuRZIkSceIffv2AVRVHTGjYupHkIH969evX793797lrkOSJEnHiA0bNrBv376BMxScgyxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1jlvuAqbZqW+8YUnb77riOSOqRJIkSZPiCLIkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEmNoQJykvcmqaN8Pb7pe26SHUnuS3J3knckOWF8/wmSJEnS6Bw3ZL9rgO2z2gL8OrCrqv4RIMkm4CbgTuA1wCnA64DTgOeNoF5JkiRprIYKyFX1ceDjbVuS84ETgN9pmi8H7gEuqKoDfb9dwLVJnl5VN4+iaEmSJGlcljIH+YeAAt4PkGQd8Cxgy0w47m0BDgAvWsKxJEmSpIkYdorFQyR5JF3gvbWqdvXNZ/T7u63tW1WHk+wEzpxjX3vnOdz6xdQoSZIkLcZiR5AvBL6Fh06v2Ngv9wzovwc4eZHHkiRJkiZmUSPIdNMrvgH8XtO2pl/eP6D/oWb9Q1TVhqMdqB9hdhRZkiRJE7HgEeQka4H/Any0qu5pVh3sl6sGbLa6WS9JkiRNrcVMsXgBRz69Ar45tWIjR9oI7F7EsSRJkqSJWkxAvoTuqRQfntV+B/AAcFbbmOR4YBOwcxHHkiRJkiZqQQE5yWOAZwIfqqr72nVVtY/uZSKb+2kYMzYDa4Hrl1irJEmSNHYLvUnvv/bbzJ5eMeNS4FbgliTX0b1J77XAjVU1+018kiRJ0tRZ6BSLS4CvcORrpwGoqtvpRpjvB64CXgFcC1y8hBolSZKkiVnQCHJVPW2IPjuA8xZdkSRJkrSMlvKqaUmSJOmYY0CWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpMXRATnJ2khuS/HOSA0k+neRls/o8P8ntSQ4luSvJZUmOG3nVkiRJ0pgMFV6TPBv4Q+AW4C3AN4AnAt86q8824Gbgx4EzgJ8DHt3/LEmSJE29eQNykvXAe4Ffq6qfPErXXwQ+BVxYVf/Sb7sfeFOSd1bVF0ZQryRJkjRWw0yx+CFgA91oMElOTJK2Q5LTgdOBa2bCce/d/TEuGkm1kiRJ0pgNE5CfCXwO+L4kXwb2A/+U5Iokj+j7nNkvb2s3rKrdwD8064+QZO/RvoD1C/xvkiRJkhZtmDnIT6Cba/xe4O100yieC7wBWA38FLCx77tnwPZ7gJOXWKckSZI0EcME5LXAScAbq+rKvu0PkqwF/keStwFr+vb7B2x/CDhhrp1X1YajHdxRZEmSJE3SMFMsDvbL353V/jvAI4Fzmj6rBmy/ulkvSZIkTbVhAvLMtIn/N6t95ueTmj4bOdJGYPfCS5MkSZImb5iA/Ml++fhZ7af0y68CO/vvz2o7JDm577cTSZIk6WFgmIB8fb/80ZmG/jFvLwfuBT5RVXfSPenilc2TLQBeBTwI/P5oypUkSZLGa96b9Krqk0m20L3w498BtwPPAS4Efqaq9vddXw98GPhokq3AU4BX0z0b+fNjqV6SJEkasaFeNQ28ArgLeGn/9XfAj1XVNTMdquqPkrwQuAz4FbqpF28D3jrSiiVJkqQxGiogV9Vh4C3919H6bQO2LbkqSZIkaZkMMwdZkiRJWjEMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDXmDchJLkhSc3w9aVbfc5PsSHJfkruTvCPJCeMrX5IkSRqt4xbQ92rgk7Pads98k2QTcBNwJ/Aa4BTgdcBpwPOWUqQkSZI0KQsJyH9WVduOsv5y4B7ggqo6AJBkF3BtkqdX1c2LrlKSJEmakAXNQU5yYpIjQnWSdcCzgC0z4bi3BTgAvGhJVUqSJEkTspCA/D5gP3AwyZ8kOaNZdwbdaPRt7QZVdRjYCZy5xDolSZKkiRhmisVh4IPAjcDXgKfSzS3ekeTsqvo8sLHvu2fA9nuAp8218yR75zn++iFqlCRJkkZi3oBcVbcCtzZNH07yEbrR4suAS4A1/br7B+ziULNekiRJmmoLuUnvX1XVp5NsB57RNx3sl6sGdF/drB+0rw1HO1Y/wuwosiRJkiZiKS8K+TLwqP77makVGwf020jzODhJkiRpmi0lIJ8GfLX//g7gAeCstkOS44FNdDfqSZIkSVNvmDfpPWZA2/nA9wIfBaiqfcB2YHOStU3XzcBa4PqRVCtJkiSN2TBzkLcmuY/uRr2vAU8BXtl///NNv0v7PrckuY7uTXqvBW6squ2jLFqSJEkal2GmWGwDHkMXdt8FXAS8Hzi7qu6a6VRVtwPPpHuSxVXAK4BrgYtHW7IkSZI0PsM85u2dwDuH2VlV7QDOW2pRkiRJ0nJZyk16kiRJ0jHHgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUuO45S5gJTn1jTcsaftdVzxnRJVIkiRpLo4gS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktRYVEBO8jNJKsnOAevOTbIjyX1J7k7yjiQnLLlSSZIkaQIWHJCTPA54M3DvgHWbgJuA1cBrgOuA/w5sXVKVkiRJ0oQs5lXTVwC30YXrDbPWXQ7cA1xQVQcAkuwCrk3y9Kq6efGlSpIkSeO3oBHkJOcAL6YbHZ69bh3wLGDLTDjubQEOAC9aQp2SJEnSRAw9gpwkwK8Av1VVO7sfH+KMfn+3tY1Vdbifq3zmHPvdO8+h1w9boyRJkrRUC5li8RLgdOAFc6zf2C/3DFi3B3jaAo4lSZIkLYuhAnKSE+nmHl9RVYMCMMCafnn/gHWHmvUPUVUb5jn2XhxFliRJ0oQMOwf5zcBh4JeP0udgv1w1YN3qZr0kSZI0teYdQU6yEfgp4C3AY5u5x6uB45OcCuzjm1MrNnKkjcDuJdYqSZIkjd0wI8iPBY4HrgT+vvn6LuDJ/fdvAO4AHgDOajdOcjywCdg5opolSZKksRlmDvLfA98/oP1twL8Ffhr4fFXtS7Id2Jzk8uZRb5uBtcD1oyhYkiRJGqd5A3JV7QO2zW5P8lPAA1XVrrsUuBW4Jcl1wCnAa4Ebq2r7COqVJEmSxmrBr5o+mqq6HXgm3ZMsrgJeAVwLXDzK40iSJEnjsphXTQNQVRfM0b4DOG+x+5UkSZKW00hHkCVJkqSHOwOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJjXkDcpKzknwoyZeSHExyd5I/TnLugL7nJtmR5L6+3zuSnDCe0iVJkqTRO26IPt/e97sW2ANsAC4B/jzJs6vqYwBJNgE3AXcCrwFOAV4HnAY8b9SFS5IkSeMwb0Cuqq3A1rYtya8Bfwf8JPCxvvly4B7ggqo60PfbBVyb5OlVdfMI65YkSZLGYlFzkKvqPuCrdKPJJFkHPAvYMhOOe1uAA8CLllamJEmSNBnDTLEAIMmJwCrgW4CXAk8B/me/+ox+X7e121TV4SQ7gTOPst+98xx6/bA1SpIkSUs1dEAG3gNc1H9/GPh1umkVABv75Z4B2+0Bnrao6iRJkqQJW0hA/gXgGrqb7zbTjSY/ErgfWNP3uX/Adoea9Ueoqg1HO2g/wuwosiRJkiZi6IBcVZ8FPguQ5LfpplO8F/gB4GDfbdWATVc36yVJkqSpttib9L4B/CHwwiRr+ObUio0Dum8Edi+uPEmSJGmyFjLFYrY1QIATgTuAB4CzgD+Y6ZDkeGAT8P4lHEdzOPWNNyx5H7uueM4IKpEkSTp2DPMmvccMaFsHXAx8uaq+UlX7gO3A5iRrm66bgbXA9SOqV5IkSRqrYUaQtyY5BNwK3A18K/DDdDfr/WDT79K+zy1JruvXvxa4saq2j7RqSZIkaUyGCci/DbwE+AngJGAv8Algc1X92Uynqro9yTOBK4GrgP10r6d+04hrliRJksZmmFdN/ybwm8PsrKp2AOcttShJkiRpuSzqKRaSJEnSscqALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUmPegJzk7CTvSvJXSe5NcleSDyR5woC+5ybZkeS+JHcneUeSE8ZTuiRJkjR6xw3R5w3AecD1wGeAxwGvBj6V5Jyq+muAJJuAm4A7gdcApwCvA04DnjfyyiVJkqQxGCYg/zLwQ1V1eKYhyVbgs3Th+WV98+XAPcAFVXWg77cLuDbJ06vq5hHWLUmSJI3FvFMsqurWNhz3bV+gGyl+MkCSdcCzgC0z4bi3BTgAvGhkFUuSJEljtKib9JIEeCzwtb7pDLrR6Nvafn2w3gmcufgSJUmSpMkZZorFIJcAjwcu7X/e2C/3DOi7B3jaXDtKsneeY61faHGSJEnSYi14BDnJk4B3ATuA9/XNa/rl/QM2OdSslyRJkqbagkaQkzwOuAH4Z+DiqnqwX3WwX64asNnqZv0RqmrDPMfci6PIkiRJmpChA3KS9cCNdGH1vKq6u1k9M7Vi4xEbdm27F12hJEmSNEFDTbFIshr4CPBE4LlV9TezutwBPACcNWu744FNdDfqSZIkSVNvmDfpPQLYSnej3cVV9YnZfapqH7Ad2JxkbbNqM7CW7iUjkiRJ0tQbZorFLwHPpxtBflSSFzfrDlTVtv77S4FbgVuSXEf3Jr3XAjdW1fbRlSxJkiSNzzABeVO/fB5HvjL6S8A2gKq6PckzgSuBq4D9wLXAm0ZRqCRJkjQJ8wbkqrpg2J1V1Q7gvKUUJEmSJC2nRb1JT5IkSTpWGZAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqHLfcBWi6nPrGG5a0/a4rnjOiSiRJkpaHI8iSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1hgrISTYmuSLJnyb5epJKcsEcfZ+f5PYkh5LcleSyJD5OTpIkSQ8Lw44gfwfwBuAU4DNzdUrybGAb8E/Aj/ff/xxw1VKKlCRJkiZl2JHdTwKPrqp7krwA+NAc/X4R+BRwYVX9C0CS/cCbkryzqr6w1IIlSZKkcRpqBLmqvl5V9xytT5LTgdOBa2bCce/d/XEuWnSVkiRJ0oSMcm7wmf3ytraxqnYn+Ydm/UMk2TvPftcvvTRJkiRpOKN8isXGfrlnwLo9wMkjPJYkSZI0FqMcQV7TL+8fsO4QcMKgjapqw9F22o8wO4r8MHXqG29Y0va7rnjOiCqRJEkazihHkA/2y1UD1q1u1kuSJElTa5QBeWZqxcYB6zYCu0d4LEmSJGksRhmQd/bLs9rGJCfTPT95J5IkSdKUG1lArqo7gc8Br0zyiGbVq4AHgd8f1bEkSZKkcRn6Jr0kb+6/fXK/3JzkfGBvVf1q3/Z64MPAR5NsBZ4CvJru2cifH1HNkiRJ0tgs5CkWb53184/0yy8BvwpQVX+U5IXAZcCvAF8F3jZgW0mSJGkqDR2QqypD9tsGbFtkPZIkSdKyGuVNepIkSdLDngFZkiRJahiQJUmSpMYoXzUtjd1SX10Nvr5akiQdnSPIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw6dYaMVb6pMxfCqGJEnHFkeQJUmSpIYBWZIkSWo4xUIaMadsSJL08OYIsiRJktRwBFmaco5IS5I0WY4gS5IkSQ1HkKUVZqkj0uCotCTp2GZAlrRkTgORJB1LDMiSpo6BW5K0nJyDLEmSJDUcQZZ0zHPetSRpIQzIkrQIo54G4rQSSZoeBmRJOgYZuCVp8ZyDLEmSJDUcQZYkzWsc87idpiJpWo08ICdZBfxPYDNwEvBp4NKqumnUx5IkaVz8R4G0co1jBPm9wEXA1cDfAi8DbkzyPVX18TEcT5IkjcC0Bfhx7HPa9zeOffoPoYUbaUBOcg7wg8BPV9XVfdsW4A7gSuC7R3k8SZIkLcy0/aNgGgP8qG/S+wHgG8B1Mw1VdQj4DeD8JBtHfDxJkiRppFJVo9tZ8jHgsVX11FntzwC2A99XVTfOWrd3nt2uB1i/fv3I6hzW/kMPLGn7dasfOkA/bfsbxz6nfX/j2OdK29849jnt+xvHPlfa/saxz2nf3zj2udL2N459Tvv+xrHPh9v+JmXfvn0AVVVHDBiPOiDfAfxjVV04q/104E7g5VX1G7PW7Z1nt+uBAvaPrNDRmEns+5a1CrU8J9PJ8zJ9PCfTyfMyfTwn02lU52Ud8GBVHZHQRx3Z1wD3D2g/1Kx/iKraMOIaJmIm2D9c6z8WeU6mk+dl+nhOppPnZfp4TqbTJM7LqOcgHwRWDWhf3ayXJEmSptaoA/IeYNCNeDNtu0d8PEmSJGmkRh2QdwJPSrJ2Vvt39ctPj/h4kiRJ0kiNOiB/EHgk8PKZhv7Nej8M/EVVOYIsSZKkqTbSm/Sq6i+TXA+8vX/m8ReBlwLfRvdGPUmSJGmqjePBcy8B3tovTwI+Q/f8478Yw7EkSZKkkRp5QO7fnPf6/kuSJEl6WBnpi0IkSZKkh7tR36QnSZIkPawZkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQFyjJqiRXJtmd5GCSTyR5xnLXtVIluSBJzfH1pOWubyVIsjHJFUn+NMnX+z/7C+bo+/wktyc5lOSuJJclGcfz2Fe8Yc9Lkl1zXD9XTL7qY1eSs5O8K8lfJbm3////A0meMKDvuUl2JLkvyd1J3pHkhOWo+1g37HlJcssc18kHlqv2Y1WSs5J8KMmX+px1d5I/TnLugL5ju1b8i2nh3gtcBFwN/C3dGwJvTPI9VfXx5Strxbsa+OSsNl9tPhnfAbyB7nr4DHDELzGAJM8GtgE3Az8OnAH8HPDo/meN1lDnpfdJumuodcd4ylqx3gCcB1xPdz4eB7wa+FSSc6rqrwGSbAJuAu4EXgOcArwOOA143uTLPuYNdV56dwGXztp+1ySKXGG+nS6fXgvsATYAlwB/nuTZVfUxGP+14nOQFyDJOcBfAj9dVVf3bavp/iLZXVXfvYzlrUj9iNifAt9fVduWtZgVKsmJwPFVdU+SFwAfAr63qm6Z1e9O4BBwTlX9S9/2NuBNwJOq6gsTLfwYt4DzsgvYWVUvmHSNK0k/+nVbVR1u2v4j8FngA1X1sr7tfwNPpbsmDvRtL6cLC8+oqpsnXfuxbAHn5RZgQ1VtWoYyV7x+VPjv6M7Vc/u2sV4rTrFYmB8AvgFcN9PQvznwN4Dzk2xcrsLUBQI/rp+8qvp6Vd1ztD5JTgdOB66ZCce9d9P9HrpojCWuSMOcl1Y/fcyP8cekqm5tQ1jf9gW60a8nAyRZBzwL2DLzF35vC3AAeNGEyl0xhjkvrSTHJVk7qfrUqar7gK/SjSZP5FoxIC/MmcDnZp0MgP8LBNg08Yo0433AfuBgkj9JcsZyF6SHOLNf3tY2VtVu4B+a9Voe/xm4F7g3yReTvHK5C1oJkgR4LPC1vukMuo+WZ18nh4GdeJ1MxIDzMuPJdNfJ1/v7kH42iTlqTPpBr0cn+Y4klwNPoZtSARO4VhxtW5iNwD8OaN/TL0+eYC3qHAY+CNxI98vsqXRzkHYkObuqPr+cxelfzXy6smfAuj147SynzwD/B/g88BjgFcA1SR5VVd6oN16XAI/nm/Na57tOnjaJonTEeQH4It39E58F1gH/DfhfwL8HfmzSBa4Q7+Gbny4eBn4duLz/eezXigF5YdYA9w9oP9Ss1wRV1a3ArU3Th5N8hO5flZfR/aLT8pu5Nua6fvxof5lU1fPbn5O8B9gBvCXJr1XVvuWp7NjWP2XnXXR/1u/rm+e7Tvw7ZszmOC9U1Y/O6vpbSX4PeGWSq6rqbyZY5krxC8A1dDffbQZWAY+kuz7Gfq340cDCHKQ7QbOtbtZrmVXVp4HtgI/fmx4z18Zc14/XzpTo54hfTfePFkcsxyDJ44AbgH8GLq6qB/tVXifL6CjnZS6/RDe98nvHXdtKVFWfraqPVdV7gAuB76R7khhM4FoxIC/MHr45rN+aafOxYtPjy8CjlrsI/auZj8Hmun68dqbLl/ul19CIJVlPNyVsPXBhVd3drPY6WSbznJe5eJ1MSFV9A/hD4IVJ1jCBa8WAvDA7gScNuIP1u/rlpydbjo7iNLo7XjUddvbLs9rGJCfTfXy2E02T0/ql19AI9Y8F/QjwROC5Az6WvwN4gCOvk+PpbgLfOf4qV54hzstcvE4maw3diP2JTOBaMSAvzAfp5r+8fKYhySrgh4G/6O/I1wQlecyAtvPpPvL66OQr0iBVdSfwObr5eo9oVr0KeBD4/WUpbIVL8qjZd+H3YeH1wNcBX340Iv3/91vppq1cXFWfmN2nn++9Hdg8ayBmM7CW7mUWGqFhzkuSdf3f9bO3+1m631/bJ1HrSjHH3+vrgIuBL1fVVyZxrXiT3gJU1V8muR54e//M4y8CLwW+je6Nepq8rUnuo7tR72t0j4F5Zf/9zy9jXStKkjf33848N3Rz/w+VvVX1q33b64EPAx9NspXuXL2a7tnIPm1kDIY4L88HLk3yQbo3gn0L3e+0JwKvGvBISy3eL9H9eX8EeFSSFzfrDjQvOrqU7vfZLUmuo/uE5bXAjVVlEBu9Yc7LfwLen+R36d5MuZbuObtnAVdW1d9PtuRj3tYkh+iug7uBb6UbiDwF+MGm31ivFd+kt0D96MpbgRcDJ9E9Iuln/cW1PJL8BN2TKp5A9+idr9CNHP98Vd21nLWtJEnm+kXypao6ten3ArqnizyZ7mPJ3wTeWlUPjLvGlWi+85LkO+n+IXkm3SPe7gduB36xqv5oMlWuDP2b2L5njtWzr5PzgSvpgtl+uhHON1XVvWMuc8UZ5rwk+Q/A24Gz6Z6P/CDdR/zvrqrfmkihK0iSHwFeQvdyqZOAvcAn6H4v/dmsvmO7VgzIkiRJUsM5yJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElS4/8DAeqHDkl12coAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(30),data.uns['pca']['variance'][:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6cc4dd2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xinyiz/anaconda3/envs/torch_r/lib/python3.7/site-packages/IPython/core/pylabtools.py:151: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAFsCAYAAACev46GAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAA3FklEQVR4nO3deZgdZZX48e9JIkkw0o3AQBBBjcimI8GRJSSELBMWQQISATOyKCKrECHCTxGFgAouoOM2CrLoiAJu4yigiIEQNYwgIiogSUBMwhbpTiALJDm/P+p26DQ36Xs7N+mu7u/neeqp7reqTp1bFDen33qrKjITSZIk9Wz9ujsBSZIkdc6iTZIkqQQs2iRJkkrAok2SJKkELNokSZJKwKJNkiSpBCzaJJVCRGREXNPdeZSVx08qP4s2SeoFIqI5Ij4ZEft3dy6SNowB3Z2AJNVoMLCyu5PowZqBT1R+nl5lucdPKjl72iT1WBExOCIGAGTmssx8sbtz2hgi4lWNjtmXjp/UW1m0SapZRBxUGRv1obUs/21EPB0Rr6j8vmdEXBMRD0fEkohYHBEzI+LwKtteU4m9VUR8KyKeBJ4Htqssf9mYrIg4KiL+JyL+HhHLI+KZiPhxRPxrlfiPRsT0iNg5In5WyaU1Im6KiG2qrL9ZRFwSEX+NiGURsTAi7oqIozusNzQivlbJ4YWImB8R34iIf6nxmE6v5PaGSi7/BBZVlvWLiI9FxJ0R8UQl/t8r+9uiXYz9gbmVXz9ROVYZEY+2W6fqmLaIODEi7o2IpZXj8YuIGFlL7pI2Li+PSqrHL4AngGOBL7VfEBE7AnsDX2rXo3M4sDNwA/AYsAVwHPDDiJicmd+tso9fVvYxDXgl8Nw68jkdWAh8o7LNMOAkYGZE7JGZf+uw/msoLh3+CJgKvBX4ILAZMKHdZ2kG7gJ2A24Cvgb0B4YDhwDfq6y3PfBbYBPgKmA28EbgFGBMRPxbZrauI/82Q4A7gJnAx4C2gm+TSp4/AH5CUcS+HXg/MDIi3paZLwB/BaYAl1c+2w8r26/r2BERlwIfAe4GPgq8iuL4/ToiDsvMn9eQu6SNJTOdnJycap6AzwIJ7NqhfVqlfY92ba+ssv2mwEPAXzq0X1PZ/jtr2W8C13RoqxZ/F2A58NUO7Y9WYry7Q/tXKu07tWv7aqXtpCrx+7X7+SfAU8B2Hdb5N2AF8Mkajuf0yr4urrIsgMFV2t/f8bMAr6u0Vd1nx+MH7ASsoihON2nXvi3QUjle/bv7fHNycnpp8vKopHpdW5kf29YQEQH8B/BAZt7b1p6Zz7dbZ9PKJb1NgduBXSJisyrxP1drIm3xo7BZRGwJPE1RFO5VZZP5mXlDh7bbK/MdK7H6AUcDf83Mb1TZ56rKek0UvW7/AyyLiC3bJoqC5xHa9d7V4GWfOwtLK/vrX7lDdMt2OVf7jLU6jKIovCyL3rq2fc4HrgZ2oOhZlNRDWLRJqktmPgDcC0yuFDgA+1H09FzXft2I+JfK+K628WnPUBRVJ1dWaa6yi4drzSUihkfE/wKLgdZK7KeBtwCbV9lkTpW2hZV52xixLSvb3tfJ7nei+A59f7v9tp92Arau8aM8nZkt1RZExLsjYhawFHi2Ervtc1T7jLV6fWX+5yrL2tresB7xJTWYY9okdcV1wBXAWOA2il63lcB32lao9L79guJy5ReB31MUViuBE4D3UOUPx8xcUksClfFkd1IM2p9G0bv2PMVlwCsoxol1tK5HXkQt+62y/nd4qfexo6U1xqr6mSPiCOD7FGPOzgQeB5ZRjK+7Bf/wlvoUizZJXfFdirFtx0bETOBI4JeZuaDdOv9KMdD/osz8RPuNI+LEBuRwOEVh9s7M/HWH+FtQjGvrimcoerTe2sl6j1AUiJtk5m1d3Fdn3ktRpI1pX8xGxM5V1s06Y7f11u1GcQNFe7t2WEdSD+BfaZLqlplPAzcDRwCTKe6+7Njb1NartUYPVkS8maLgWl9ri/8B4GWP8KhVZcza9cCuEfH+jssrPYhk5kLg58AREbF3tfUiYquu5lGxkqIYW/1dXdn/+VXWbbtT9NU1xv6fSuypUXlESyX+UIqe0MeAP3QhZ0kbiD1tkrrqWuCdwOcpLnv+uMPyv1KMjfpIRLTdMfomikds/Al423ru/2aKy4rfjogvU/SO7QscTNFztD7fb+dTXPq9MiImUNxhGRQD8wdQ9IBB8WiPu4A7I+I6iiKnH8VYsMMoLiN/cj3yuAl4F3B7Jf4rgIkUN3OsITMXRsQjwNERMRt4Eng+M39aLXBmPhQRn6V45MedEfF9XnrkxxBgcmb6BgWpB7Fok9RV/wv8k6Jn58rMXNZ+YWaujIh3UNwVeRzFM9ceqPz8VtazaMvM2RFxEPApimeMraR4ztlo4MsUN0Z0NfazEbFPJe4RFD2Di4G/AP/Zbr3HI+JtwLkURdp/UFzOfBz4KcXz6bosM78XxdsRplAcx2crcc/jpRso2ptM8ay2T1EUdo9V1l9b/HMrhd6pwGeAF4BZwHsyc8b65C6p8SKz3mEQkiRJ2tgc0yZJklQCFm2SJEklYNEmSZJUAhZtkiRJJVDKu0cjYgVFwbmou3ORJElqoM2AVZn5shqtlHePRsQqIJqamtYrzrJlyxg0aFBjkpIkSVpPra2tAJmZL7saWsqeNmBRU1NTU0tLS00rf+tb33pZW2Zy+eWX88ADDzQ4NUmSpK5pbm6mtbW16pXEPjGm7bzzzmPAgAH0799/9TRgwAAqb6ORJEnq8cra01aXSZMmccABB7D11luv0T5v3rxuykiSJKk+NY1pi4iBwEUU79vbHPgj8LHM/FUn2z0K7LCWxY9k5o51ZftS3JZ6Lo9KkiSVQeXyaGtmNndcVuvl0Wso3n33HeBMYBVwc+XdfOtyFkWh1346v7LsFzXue71NmTKFGTPWfI3ejBkzmDJlysZKQZIkab102tMWEXtSvEB4SmZeUWkbRPHi5/mZuV9dO4w4H5gG7JuZv+lS0nX2tO23337ceeedNbdLkiR1h/XtaTsSeBG4sq0hM5cBVwEjI2Jonfm8B5jb1YKtK9Z2w4E3IkiSpLKopWgbDjyYmc91aL8bCGD3WncWEcOBXYDv1rpNI0ycOJFTTjmFuXPnsnTpUmbPns3JJ5/MxIkTN2YakiRJXVbL3aNDgWq3WS6ozLetY3+TK/P/XtdKEdHSSZy6nqp72mmnccMNNzBs2DA22WQTJk+ezKRJkzjwwAPrCSNJktRtainaBgPLq7Qva7e8UxHRDzga+ENm/rW29BrjqKOOYsSIEdxyyy3ccccdLFq0yIJNkiSVSi1F21JgYJX2Qe2W12I08Brg8s5WrDb4rr1KT1zNvW2tra1MnToVgAkTJjBu3LhaN5UkSeoRainaFlBcIu2orW1+jfuaTPGokOtrXL9h5syZwwUXXAAUr6+aPXv26t8vuuiijZ2OJElS3Wop2u4DzoyIIR1uRtirMv9jZwEqD+d9FzA9M2st8hrm2muvXeP38ePHb+wUJEmS1kstRdtNwDnAicAVsLoIOwGY2VaERcT2wKaZ+WCVGAcDzXRyA8KGMnr06O7YrSRJUsN0WrRl5qyIuBG4rPJMttnAcRSvpzq+3arXUYxbq/bws8kUNzP8YH0TliRJ6otqfWH8sRRvMTiW4t2j9wMHZ+bMzjaMiM2AdwA/y8zWriYqSZLUl9X0wviexhfGS5Kk3qgRL4yXJElSN7JokyRJKgGLNkmSpBKwaJMkSSoBizZJkqQSsGiTJEkqAYs2SZKkErBokyRJKgGLNkmSpBKwaJMkSSoBizZJkqQSsGiTJEkqAYs2SZKkErBokyRJKgGLNkmSpBKwaJMkSSoBizZJkqQSsGiTJEkqAYs2SZKkErBokyRJKgGLNkmSpBKwaJMkSSoBizZJkqQSsGiTJEkqAYs2SZKkEqipaIuIgRFxaUTMj4ilEfG7iBhX604i4j0RcXdEPB8R/4yIOyJiz66nLUmS1LfU2tN2DTAF+A5wJrAKuDki9ulsw4i4GLgWeKCy7YXAbGCbLuQrSZLUJ0VmrnuFokdsFjAlM6+otA2iKMLmZ+Z+69h2BHAX8K7M/FHDko5oaWpqamppaWlUSEmSpG7X3NxMa2tra2Y2d1xWS0/bkcCLwJVtDZm5DLgKGBkRQ9ex7ZnA/2XmjyKiX0QMqS91SZIkQW1F23Dgwcx8rkP73UAAu69j23HA/0XEp4BWYHFEPBoRk9e1w4hoWdcENNWQtyRJUq8xoIZ1hgLzqrQvqMy3rbZRRGwObAEcDawEzgX+CZwGfCciljTykqkkSVJvVkvRNhhYXqV9Wbvl1bRdCt0C2DszZwFExI+AR4ALgKpFW7XruO3Z2yZJkvqaWi6PLgUGVmkf1G752rYDmNtWsAFk5nLgJuCtjnGTJEmqTS1F2wKKS6QdtbXNX8t2/6TooXuyyrInKcbD2VsmSZJUg1qKtvuAnav0iu1Vmf+x2kaZuaqy7WuqLN6OYpzbP2vKUpIkqY+rpWi7CXgFcGJbQ0QMBE4AZmbm/Erb9hGxc4dtbwReGxH/3m7bzYB3A7/JzLVdWpUkSVI7nd6IkJmzIuJG4LLKM9lmA8cBOwDHt1v1OmA0xWXPNl+jKPZ+EBGXA88C7weagf/XgPwlSZL6hFruHgU4FphWmW8O3A8cnJkz17VRZi6JiDHAZ4EzKO40vQcY39m2kiRJekmnr7HqiXyNlSRJ6o3W9zVWkiRJ6mYWbZIkSSVg0SZJklQCFm2SJEklYNEmSZJUAhZtkiRJJWDRJkmSVAIWbZIkSSVg0SZJklQCFm2SJEklYNEmSZJUAhZtkiRJJWDRJkmSVAIWbZIkSSVg0SZJklQCFm2SJEklYNEmSZJUAhZtkiRJJWDRJkmSVAIWbZIkSSVg0SZJklQCFm2SJEklYNEmSZJUAhZtkiRJJVBT0RYRAyPi0oiYHxFLI+J3ETGuhu0+GRFZZXpi/VOXJEnqOwbUuN41wLuAK4BHgOOBmyNidGb+tobtPwgsaff70tpTlCRJUqdFW0TsCRwNTMnMKypt1wEPAJcC+9Wwnxsys6XraUqSJPVttVwePRJ4EbiyrSEzlwFXASMjYmgNMSIiNouI6FqakiRJfVstRdtw4MHMfK5D+91AALvXEOPvQCvQGhHfiohX15WlJElSH1fLmLahwLwq7Qsq823Xse2zwH8CvwNeAMZSjG/bIyL2yszl1TaKiJZOcmrqZLkkSVKvUkvRNhioVlwta7e8qsz8YoemmyLiAeArwLHAN2tJUpIkqa+r5fLoUmBglfZB7ZbX4+sUd5Ku9ZEhmdm8roniUqskSVKfUUvRtoDiEmlHbW3z69lhZq6iuNzquDZJkqQa1VK03QfsHBFDOrTvVZn/sZ4dRsQrgNcCT9eznSRJUl9WS9F2E/AK4MS2hogYCJwAzMzM+ZW27SNi5/YbRsRWVeJNpbi0emtXk5YkSeprOr0RITNnRcSNwGWVZ7LNBo4DdqB4M0Kb64DRFI8BafNYRHyP4kG8y4ExFG9WuAv4biM+gCRJUl9Q62usjgWmVeabA/cDB2fmzE62+29gX2ASsAnwaCXOpzNzRVcSliRJ6osiM7s7h7pFREtTU1NTS0tLd6ciSZLUMM3NzbS2trZWnpaxhlrGtEmSJKmbWbRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJWARZskSVIJWLRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJWARZskSVIJWLRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJWARZskSVIJWLRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJVATUVbRAyMiEsjYn5ELI2I30XEuHp3FhE/j4iMiCvqzlSSJKkPq7Wn7RpgCvAd4ExgFXBzROxT644i4h3AfvUmKEmSpBqKtojYEzga+EhmfiQzvwGMBf4OXFrLTiJiE+By4LL1yFWSJKnPqqWn7UjgReDKtobMXAZcBYyMiKE1xDgTGAx8ritJSpIk9XW1FG3DgQcz87kO7XcDAey+ro0jYhvg48BHM3NJV5KUJEnq6wbUsM5QYF6V9gWV+badbP9p4CGK8XA1iYiWTlZpqjWWJElSb1BL0TYYWF6lfVm75VVVxsMdC4zOzKw/PUmSJEFtRdtSYGCV9kHtlr9MRATwReAHmXlXPUllZvO6lld64uxtkyRJfUYtRdsCikukHbW1zV/LdocDewIfjYjXdVi2WaXtycysWvRJkiTpJbXciHAfsHNEDOnQvldl/se1bLd9Jf7twNx2E8AJlZ9H15OsJElSX1VLT9tNwDnAicAVULwhgaLwmpmZ8ytt2wObZuaDle1+CjxaJd6PgP+leGTIveuRuyRJUp/RadGWmbMi4kbgssoz2WYDxwE7AMe3W/U6ip6zqGw3u7LuGoqhbszOzB+vZ+6SJEl9Ri09bVDcATqtMt8cuB84ODNnbqjEJEmS9JIo45M4IqKlqampqaWlpbtTkSRJapjm5mZaW1tbqz1Jo9YXxkuSJKkbWbRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJWARZskSVIJWLRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJWARZskSVIJWLRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJWARZskSVIJWLRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJVATUVbRAyMiEsjYn5ELI2I30XEuBq2mxwRt0fEExGxPCIejYirI2KH9U9dkiSp7xhQ43rXAO8CrgAeAY4Hbo6I0Zn523Vs91ZgHvBz4J/ADsBJwDsi4l8z84mupS1JktS3RGaue4WIPYFZwJTMvKLSNgh4AJifmfvVtcOIPYB7gKmZ+bkuJR3R0tTU1NTS0tKVzSVJknqk5uZmWltbWzOzueOyWi6PHgm8CFzZ1pCZy4CrgJERMbTOfB5ry6vO7SRJkvqsWoq24cCDmflch/a7gQB27yxARLw6Iv4lIv4NuLrS/Kt6EpUkSerLahnTNpRiXFpHCyrzbWuI8TCwReXnhcDpmfnrta0cES2dxGuqYZ+SJEm9Ri1F22BgeZX2Ze2Wd+YI4JXAzsB/AK+qKTtJkiQBtRVtS4GBVdoHtVu+Tpl5Z+XHmyPix8ADEfFcZn55Les3rytepSfO3jZJktRn1DKmbQHFJdKO2trm17PDzJxLcffo5Hq2kyRJ6stqKdruA3aOiCEd2veqzP/Yhf0Oxp4ySZKkmtVStN0EvAI4sa0hIgYCJwAzM3N+pW37iNi5/YYRsVXHYBHxNoo7Tu/petqSJEl9S6dj2jJzVkTcCFxWeSbbbOA4ircbHN9u1euA0RSPAWnzWETcAPwJeA7YDXgfsBiY1ogPIEmS1BfU+hqrYymKrGOBzYH7gYMzc2Yn230FGA9MBDalGB93AzCtMrZNkiRJNej0NVY9ka+xkiRJvdH6vsZKkiRJ3cyiTZIkqQQs2iRJkkrAok2SJKkELNokSZJKwKJNkiSpBCzaJEmSSsCiTZIkqQQs2iRJkkrAok2SJKkELNokSZJKwKJNkiSpBCzaJEmSSsCiTZIkqQQs2iRJkkrAok2SJKkELNokSZJKwKJNkiSpBCzaJEmSSsCiTZIkqQQs2iRJkkrAok2SJKkELNokSZJKwKJNkiSpBGoq2iJiYERcGhHzI2JpRPwuIsbVsN0REfH9iJgbEUsi4sGI+GxENK1/6pIkSX1HrT1t1wBTgO8AZwKrgJsjYp9OtvsGsAvwbeBDwK2V+cyIGNSVhCVJkvqiAZ2tEBF7AkcDUzLzikrbdcADwKXAfuvY/MjMnN4h3j3AtZWY13QlaUmSpL6mlp62I4EXgSvbGjJzGXAVMDIihq5tw44FW8WPKvNdak9TkiSpb6ulaBsOPJiZz3VovxsIYPc697lNZf5MndtJkiT1WZ1eHgWGAvOqtC+ozLetc5/nAiuBH65thYho6SSGNzJIkqQ+pZaetsHA8irty9otr0lEvAd4P3BZZs6udTtJkqS+rpaetqXAwCrtg9ot71REjKIYB/cz4OPrWjczmzuJ1YK9bZIkqQ+ppadtAcUl0o7a2uZ3FiAi3gr8D3A/cFRmrqw5Q0mSJNVUtN0H7BwRQzq071WZ/3FdG0fEMOAW4CngHZn5fL1JSpIk9XW1FG03Aa8ATmxriIiBwAnAzMycX2nbPiJ2br9hRGwD/ILiYbwHZKZ3jEqSJHVBp2PaMnNWRNwIXFZ5Jtts4DhgB+D4dqteB4ymeAxIm1uANwCXUTzTbWS7ZbMz87frl74kSVLfUMuNCADHAtMq880pxqYdnJkzO9nurZX5R6osuxawaJMkSapBZGZ351C3iGhpampqamlp6e5UJEmSGqa5uZnW1tbWak/SqPWF8ZIkSepGFm2SJEklYNEmSZJUAhZtkiRJJWDRJkmSVAIWbZIkSSVg0SZJklQCFm2SJEklYNEmSZJUAhZtkiRJJWDRJkmSVAIWbZIkSSVg0SZJklQCFm2SJEklYNEmSZJUAhZtkiRJJWDRJkmSVAIWbZIkSSVg0SZJklQCFm2SJEklYNEmSZJUAhZtkiRJJWDRVof58+dzzjnncN555zF37tzV7RdeeGE3ZiVJkvoCi7Y6HHfccRx00EEcdNBBnHTSSfz3f/83AHfccUc3ZyZJknq7AbWsFBEDgYuA9wKbA38EPpaZv+pkuz2B44G9gLcAr8jMWJ+Eu9Py5csZN24cAKNGjeLss8/m3nvvZeXKld2cmSRJ6u1q7Wm7BpgCfAc4E1gF3BwR+3Sy3cHAByo/z+5Kgj1Jc3Mzjz32GAD9+vXj8ssvZ9ddd2XWrFndnJkkSertIjPXvULRWzYLmJKZV1TaBgEPAPMzc791bLs1sCgzl0bEFcCZjehpi4iWpqamppaWlvUNJUmS1GM0NzfT2trampnNHZfV0tN2JPAicGVbQ2YuA64CRkbE0LVtmJlPZubS+lPumaZMmcKMGTPWaJsxYwZTpkzppowkSVJfUUvRNhx4MDOf69B+NxDA7o1Oqqe65557GDVq1Bpto0aN4p577ummjCRJUl9Ry40IQ4F5VdoXVObbNi6dQkS0dLJKU6P3WYuI6ld219YuSZLUKLX0tA0GlldpX9ZueZ8wceJETjnlFObOncvSpUuZPXs2J598MhMnTuzu1CRJUi9XS0/bUmBglfZB7ZY3VLXBd+1VeuI2em/baaedxg033MCwYcPYZJNNmDx5MpMmTeLAAw/c2KlIkqQ+ppaibQHFJdKO2trmNy6dnu2oo45ixIgR3HLLLdxxxx0sWrTIgk2SJG0UtRRt9wFnRsSQDjcj7FWZ/7HhWfVQra2tTJ06FYAJEyasftCuJEnShlZL0XYTcA5wInAFrH5DwgnAzMycX2nbHtg0Mx/cMKl2vzlz5nDBBRcAkJnMnj179e8XXXRRd6YmSZJ6uU6LtsycFRE3ApdVnsk2GzgO2IHiFVVtrgNGUzwGBICI2IHi1VcAe1bazq/8/sfM/On6foCN6dprr13j9/Hjx3dTJpIkqa+p6d2jwLHAtMp8c+B+4ODMnNnJdq+vbNde2+/XAqUq2kaPHt3dKUiSpD6q09dY9US+xkqSJPVG6/saK0mSJHUzizZJkqQSsGiTJEkqAYs2SZKkErBokyRJKgGLNkmSpBKwaOsGf/vb3zjjjDP45je/yZ/+9CcmTpzIe97zHh566KHuTk2SJPVQtT5cVw30/ve/n0suuYTFixdz+OGHc+ONNzJkyBBOOeUUbrvttu5OT5Ik9UAWbd2gX79+jBo1CoBPf/rTDB8+HCjeZ1qPOXPmrPF7ZnLKKafw9a9/nTe84Q2NSVaSJPUIFm3dYOutt2blypX079+f22+/HYAVK1awatWquuK8+c1vZu+99+Z1r3sdUBRtf/nLX7j44ov51re+1ei0JUlSN/I1Vj3I8uXLGThwYM3rL1iwgC9+8YssWrSIM844g1122YWDDjqIm2++eQNmKUmSNhRfY9XDTJkyhRkzZqzRNmPGDM4777y64gwdOpTPfOYzTJs2je9+97scf/zxLFq0qJGpSpLWU2tra3enoF7Coq0b3HPPPavHtLUZNWoU99xzT5fibbHFFkybNo0vfvGLXH755Y1IUQ02f/58Tj/9dMaMGcPo0aMZO3Ysp59+Ov/4xz8aEv/iiy+uO59zzjmH8847j7lz565uv/DCC+uK06g7oefMmbPGNHv2bCZMmPCycZudmT59OgAtLS2cffbZHHjggZx22mksWLCgrjhXX301UHy+ww8/nNGjR3PooYdy77331hzjiCOO4Fvf+hYLFy6sa98dzZo1iyOOOIKTTjqJBx54gHe84x2MGTOGn//853XFeeqpp/jwhz/MPvvswy677MJhhx3G5z73OZYvX15XnAceeICJEycyZswY9t9/f8aMGcPEiRO5//7764qzNmeccUbd+Rx55JEcffTR/OY3v1ndfsopp9QV57e//S2HHHII559/PtOnT2efffbh3//939eIWYvbb799jelXv/oVEyZMWD0Uplbf+973APj73//OpEmTGDVqFO985zv5y1/+UnOMT3/60wD87ne/Y9SoUey///6MHDmSW265pa5c9thjDy644ALuu+++urbr6Kc//Sn7778/hx12GLfddhujRo1i7733rns4z8MPP8zRRx/N29/+dl71qldx4IEHMmXKFJ599tm64vS07+WaZGbpJqClqakpy2q//farq31tzjrrrLzzzjvXaLvzzjvzrLPOqivOr3/968zMfPbZZ/PDH/5wHnDAAXnqqafm/Pnz64qjtRs7dmzefffda7TNmjUrx44dW1ecq6666mXTlVdembvttltdccaPH5+33XZbTp8+PcePH5/f+c53MjNzzJgxdcUZNWpU3nnnnfmzn/0shw0blvfee28+/PDDOW7cuLriDB48OMeMGZMnnHBCnnDCCXn88cfna17zmjzhhBPqitOW/zHHHJPXX399Ll26NH/5y1/mhAkT6orT9t/lwAMPzD//+c+ZmfmPf/wjR4wYUXOM4cOH5+WXX55jxozJcePG5Ze+9KV8/PHH68ojM3PEiBE5b968fOCBB3K77bbLefPm5aJFi3KvvfaqK85BBx2U9957b65YsSJ/8Ytf5JQpU3L69On5vve9r644I0eOfNl3w7x583LkyJF1xfn4xz/+sun888/PN7zhDXXFGTVqVD7yyCP56KOP5jHHHJMXX3xxZmbuv//+dcXZe++98+9///sax/nZZ5+t+3t5m222yQkTJuSFF16YF154YX7yk5/MN77xjXnhhRfWFaftXH7nO9+Zd911V2ZmPvjgg3Xl0/b/4dixY/PJJ5/MzMznnnsu995777py2WefffJHP/pRHnvssbnHHnvkhz/84ZwxY0ZdMTKLY7xs2bJ86qmncrvttsvFixfnihUr6s5n7NixuWDBgszM/Otf/5onnHBC/u1vf8tJkybVHacnfS+3aWpqSqAlq9Q/9rR1g4kTJ3LKKacwd+5cli5dyuzZszn55JOZOHFiXXEa1WN30UUXAXDqqafy9re/nR//+MccfvjhHH/88XXF0dotXbqU3XbbbY223XbbjaVLl9YV57zzzmPAgAH0799/9TRgwAAioq44y5cvZ9y4cYwePZpbb72V3//+95x99tmsXLmyrjhtd0IffPDBDB06lOHDh7PjjjvWfSf07Nmz2XPPPRk0aBBTp07l6quv5i1veUvdf4FnJqtWrWLBggUcffTRDBo0iPHjx7Ns2bK64my66aYsXLiQTTfdlG222QaAV7/61fTv37/mGM3NzZx11lncfvvtXH/99QwePJiTTz75Zf/PdmblypVsu+22DBs2jP79+7P11lvzqle9in796vv6bmlpYfjw4fTv3599992XP/zhD4wePZpHH320rjjw8jvd86U/qGt21VVXMW7cuDWm8ePHs/nmm9cVZ9WqVQwbNowddtiB7373uwwZMoSjjjqKJUuW1BVn4MCBvPa1r2W33Xbj7W9/O9tuuy3Nzc11H+e5c+dy6KGH8vDDD7PXXnvxiU98gh133JELLrigrjhLly5lzpw5PPPMM+y7774A7LTTTnXdsLbFFlvw8MMPs91229E2Bnzx4sUMGjSorlwGDRrExIkTufbaa5k1axYHHXQQ119/PW9729vqitOWe9v3VUQQEXWfO0uWLKG5uRko/r987LHHeOMb38gzzzxTV5ye9r1cC+8e7QannXYaN9xwA8OGDWOTTTZh8uTJTJo0iQMPPLCuOGs7Ieo9UTr+Qwcwfvx4pk2bVlecq6++mhNOOIG//e1vfOQjH+Gf//wnm222GRdeeCF77LFHXbF6m0suuYRDDjmETTfdlM0224zW1laWLVtW9zGeNGkSBxxwAFtvvfUa7fPmzasrTnNzM4899hg77LAD/fr14/LLL+eqq67iK1/5Sl1xGnUndNv4zIULF3LFFVfw+OOPd3l85rhx4+jXrx8tLS00NzezePHiuv8B/8pXvsJZZ53F008/zbBhw9h1110ZMmRIXZc72v9DtNVWW3HiiSdy4oknsnjx4rpyec973sMee+zB61//es4++2xGjBjBK1/5Sg499NC64px66qmMGDGC1772tcydO3d1EdHxH63OfP3rX+f000/n2WefZdWqVUQEW2yxBV/72tfqinPaaaex6667stVWW70sz3q8+c1vXn0uA5x55pnssssufOhDH6orzu677776XP7hD38IwAsvvLC6OKjVoEGDOP300/ngBz/INddcw5FHHtmlS+Q777wz06ZNY6eddlrjXK6nqP3617/OhRdeyLx589h9993ZaaedeNOb3sRXv/rVunJp/30zYMAAxo8fz/jx4+uKATB16lTGjh3LG9/4Rr70pS8xcuRIBg0aVPel7GnTpjFhwgSg+PfusssuA6g7p572vVwL7x7tBocffjgjRozgrW99K3fccQeLFi3iP//zP+uOc/nll/Pwww/zkY98hG222Yb58+fz2c9+lp122okpU6bUHGfMmDFA0Wvygx/8YPWXw9ixY/m///u/muOMGzeOX/3qVxx00EF8/vOfZ9ddd2XevHm8+93vZubMmTXHOeKIIzjkkEM47LDD2GKLLWrerqNZs2Zx6aWXsuWWW/KhD32Ic889lyVLljB16lQOPvjgmmI89dRTfOYzn+G3v/0tLS0tvOlNb2LUqFGcccYZdd3p22bp0qW0tLTQ1NTEpptuWvf2ZVHvndAdtba28tBDD7Hnnns2MKv6rVixgmeeeYampiYGDx7crbm0Wbx4Mf369eOVr3xl3duuXLmShQsXsuWWW9bdg6SuWblyJU8++STbbrttd6eitehp38vrunu028endWWi5GPaOo4bqvf6eZvly5fnt7/97YyIHDhwYL7vfe/Lm2++uREpdskhhxySzzzzTB5xxBG5cOHCzMxcsmRJjho1qq44PWksUKPGAa3NtGnTjNPH4vSkXLoSZ968eXnOOefkueeem3Pnzl3d/slPfrLuOGeffXaee+65OWfOHONUidN2nLsapyf+t1rfz9Qxzvp8rocffjhPP/30/MY3vpH3339/HnbYYXnMMcfkgw8+2C1x2qxrTJuXR7vBnDlzVl+ayExmz569+ve28WW1OOqooxgxYgS33HLL6h67ei+xQvEIkiOOOGKNsTYzZszghz/8YV13ozbikhK8NBaoLdZPfvITTj75ZFpbW1/2qJR1aRsL1DYWaeutt6Z///519TC0jQMC2HffffnUpz7FF77whbr+OwFVx2ZlJt/73vc4//zzjdML4/SkXBoZ57jjjls9hucDH/gAxx9/PJMnT+aOO+6oOUbHOCeddJJxNkCcvvDfan0+V6NeKbkxX01p0dYNrr322jV+78rYACguIU2dOhWACRMmMG7cuC7Fueeee15WnI0aNYqPfexjdcXZfvvt+fa3v73el5SyQWOBjjnmmPUeC3TKKac0ZBzQeeedx+c+97mXDbitd/yhccoTpyfl0sg4bTexQPE9cfbZZ3PvvffWfROLcTZ8nJ6US0+M06hXSjYqTk2qdb/19ImSXx5tlB122GGNW+Xb/16PnvYIkg1l0aJF+dxzz9W93YoVK/LJJ5/MlStXdnnfp556aj7xxBMva7/kkkuM00vj9KRcGhnn0EMPzUcffXSNtiuvvDIHDhxonB4Wpyfl0hPjvPvd784VK1ZkZuYLL7yQmZkvvvhi3Y+LaVScNuu6POqNCCW2rq7g0aNH1xynUTc07Lffftx55501t69Noy7XNiJOo3KRJJXD+t5Itb5xvBFB69SoGxoa1WO3thsX6r2hoRFxGpVLo3ohjVOeOD0pF+P0zTg9KRfj1G5dPW3dXoB1ZbJoa6yJEyfmZZddlrfeemt+9KMfzdNPP71Lcb7whS/kySefnHPmzMklS5bkI488kh/84AfzC1/4Ql1xGlX8NSJObyxEjbNx4vSkXIzTN+P0pFyMUzvfiKB1aruhYcKECVxyySV1vduuvdNOO419992XYcOGsfnmm/OpT32KiRMn1nWJFRr3xohGxGlULo16ELJxyhOnJ+VinL4ZpyflYpzGsGjT6keQXHDBBXz84x9f/QiSel+7ctRRR7FgwQJuueUWzj77bDbddNMuPYKkUcVfI+L0xkLUOBsnTk/KxTh9M05PysU4DVKt+62nT3h5tKGmT5++1qkejXpocKMu1zYiTqNyadS4QeOUJ05PysU4fTNOT8rFOLXz4bpap3ruNF2XRj00uFHPn2tEnEbl0qgHIRunPHF6Ui7G6ZtxelIuxmmQapVcT5+wp61HalSPXaOeP9eIOI3KpVG9kMYpT5yelItx+macnpSLcWrXG5/TtgqIpqam7k5FG8CKFSvWumzAgNo7hxsRp1G5LFq0iE022WT17y+88MLq3wcNGmScXhinJ+VinL4ZpyflYpzatba2AmRmvuy+g7IWbSsobqJY1MUQbdVea2MyUhUe4zX1X8eyet690jHOkMr8ufWM06h8emOc9se4njg9+TP1xDiey42P4zHunjhdPc5tNgNWZebLegZKWbStr4hoAchqTxtWQ3iMNw6P84bnMd44PM4bnsd449iQx9lHfkiSJJWARZskSVIJWLRJkiSVgEWbJElSCVi0SZIklYBFmyRJUglYtEmSJJVAn3xOmyRJUtnY0yZJklQCFm2SJEklYNEmSZJUAhZtkiRJJdCniraIGBgRl0bE/IhYGhG/i4hx3Z1XbxER+0dErmXaubvzK6OIGBoRn4mIX0fE4sqx3H8t674zIu6NiGUR8feI+EREDNi4GZdTrcc5Ih5dy/n9mY2fdblExNsj4isR8ZeIeL5yjn4vIt5YZd0REXFXRCyJiCci4osRsWl35F0mtR7jiJi+lvP4e92Ve1lExL9FxI8i4rFKHfFERNwSESOqrNvw87ivfaFfA7wLuAJ4BDgeuDkiRmfmb7svrV7nCuCeDm3zuyGP3mAn4FyK8/V+4GVfDAARcRDwY+B24AzgLcAFwJaV37VuNR3ninsozvH2HtgwafUq5wL7AjdSHONtgNOBP0TEnpn5V4CI2B34FfBn4MPAdsA5wBuAQzd+2qVS0zGu+DvwsQ7bP7oxkiy5YRS10zeBBUAzMBm4MyIOysxfwgY8jzOzT0zAnkACZ7VrG0TxJX1nd+fXGyZg/8oxntjdufSWCXgVsEXl54mV47t/lfX+TFFM9G/XdjGwEtixuz9HT5/qOM6PAj/u7nzLOFEUwpt0aNsRWAZc067t58A/gCHt2k6s/DcZ292foydPdRzj6cB93Z1vb5mATYEngP9t17ZBzuO+dHn0SOBF4Mq2hsxcBlwFjIyIod2VWG8UEa/y0tz6y8zFmblwXetExK7ArsB/ZebKdou+SjEE4l0bMMVeoZbj3F5lqIWX6+qQmb/JzBc6tP2N4g+OXQAiYjPg34HrMvO5dqteBzwHvHsjpVtKtRzj9iJiQEQM2Vj59VaZuQR4mqLXbYOex32paBsOPNjhAALcDQSw+0bPqPf6NrAIWBoRv4iIt3R3Qr3c8Mr89+0bM3M+xV96w1+2hdbHBOB54PmImB0RJ3V3QmUVEQFsDTxTaXoLxaWnjufyC8B9eC7XrcoxbrMLxXm8uDLO+6MR0ZdqgvVS6ZjYMiJ2iohPAW+muBwKG/A87ks9IUOBeVXaF1Tm227EXHqrF4CbgJspviD+leIa/l0R8fbMfLg7k+vF2nqJF1RZtgDP7Ua6H5gBPAxsBXwA+K+IeHVmejNC/SYDr+GlsVWdncv7bIykepmOxxhgNsX41z8BmwHHAJcA2wMnb+wES+pqXrqK8QLwdeBTld832Hncl4q2wcDyKu3L2i3XesjM3wC/adf0PxHxU4q/Nj5B8eWhxms7d9d2fnsZr0Ey853tf4+Iq4G7gI9HxNcys7V7Miufyh3lX6E4ft+uNHd2Lvs9XYe1HGMy8/0dVr02Im4AToqIyzPzoY2YZlldCPwXxQ0G7wUGAq+gOHc32Hncl7pCl1Ic1I4GtVuuBsvMPwK3AT5aZcNpO3fXdn57bm8glTGEV1AUxvYC1SgitgF+BjwLTMrMVZVFnssNso5jvDafpxgqNGZD59YbZOafMvOXmXk1cADwNoonVMAGPI/7UtG2gJe6LNtra/ORFBvO48CruzuJXqytC35t57fn9ob1eGXuOV6DiGiiGELRBByQmU+0W+y53ACdHOO18Tzuosx8EfgJcEREDGYDnsd9qWi7D9i5yp0ye1Xmf9y46fQpb6C4s0Ybxn2V+b+1b4yIbSm67u9DG9IbKnPP8U5ExCDgp8CbgEOqXIZ7AFjBy8/lTShuFrtvw2dZbjUc47XxPF4/gyl6Kl/FBjyP+1LRdhPF9eYT2xoiYiBwAjCzcqed1kNEbFWlbSRFd/utGz+jviEz/ww8SDEepX+7RacAq4AfdEtivUxEvLrj3XWVfyCnAosBH9C9DpVz8/sUl5EnZebvOq5TGRN4G/DeDn9gvxcYQvHQWK1FLcc4Ijar/NvXcbuPUnxf3LYxci2rtfw7txkwCXg8M5/akOdxn7kRITNnRcSNwGWVZ7LNBo4DdqB4M4LW3/cjYgnFzQjPUNwCfVLl5092Y16lFhHnV35se87SeyvFcEtmfrnSNhX4H+DWiPg+xbE/neLZbd61W4MajvM7gY9FxE0UD9ndguI75E3AKVUeJ6Q1fZ7iGP4UeHVE/Ee7Zc9l5o8rP3+M4jtkekRcSdFbfDZwc2ZaUKxbLcd4D+C7EXE9xcPlh1A8N+zfgEszc+7GTbl0vh8RyyjO0SeA11J0/mwHHN1uvQ1zHnf3k4Q35kQxAPCzFNebl1E8o218d+fVWybgQ8AsYCHFg4znAd8Ctu/u3Mo8UTxBu9r0aIf1JgJ/qJzbj1Pc3TSgu/Mvy9TZcaYYaPxTimffLad4FuF0iktQ3Z5/T58qx6rWc3kkMJNiwPaTwJeAV3b3Z+jpUy3HGHg9RU/Po5Xj+3zle/u47s6/DBPwvspxfqry79zTle+F0VXWbfh5HJXAkiRJ6sH60pg2SZKk0rJokyRJKgGLNkmSpBKwaJMkSSoBizZJkqQSsGiTJEkqAYs2SZKkErBok6QqIuL4iMiI2L+GdR+NiOkbPClJfZpFmyRJUgn0mXePStIGtBPFq4IkaYOxaJNUShHRHxiYmUu6O5fMXN7dOUjq/bw8KqnHaze+bHxEfDwiZgPLgHdHxISI+H5EzImIpRHREhG/iIjRVeJMr4w/2zYiro+IZyNiSUTcGhFvqjGXj1Vy+c+I6Fdpe9mYtra2iNg5In4WEYsjojUiboqIbarE/ddK3s9HxMKIuDYitqzs65quHDdJvYs9bZLK5HPAK4BvAouAh4AzgFcD1wH/AF4DnAj8KiLGZOaMDjFeCdwJ/A74KPB64EzgJxHx5sxcWW3HlZ69LwMnA/8vMz9TQ76vAaYDPwKmAm8FPghsBkxoF3tHYAbFH9JfAuYBBwO31LAPSX2ERZukMhkMDG9/STQi7s/M59uvFBFfB/4M/D+KYqi9LYHPZuZl7dZ/GrgMGA/c2nGnETEY+C7wDuC4zLyuxnzfCByVmTe0i7UKODUidsrMhyrNl1AUciMzc2al7csR8X3gbTXuS1Iv5+VRSWXytY5j2NoXbBExJCK2AFYCs4C9qsRYRdGb1d7tlfmOVdZ/NfBLioLu0DoKNoD57Qu2avuq9OAdDNzdrmBr8/k69iWpl7OnTVKZPNyxISKGUfRUHQA0d1hc7Y7O+Zm5rEPbwsp8iyrrXwMMAfbLzLvqSRaYU6Wt4762orhk+1CVdau1Seqj7GmTVCZr9LJFxBCK8WkHAl8EjqQo3v6dokcrqsSoOmatLWSVtu9T9M59vHKZtB717kuS1sqeNkllNg7YFnhfZl7dfkFEXNygffw38Cvg28D/RsShDX7MyNPA8xTPeuuoWpukPsqeNkll1taTtUavVURMoPp4ti7JzO8BxwCjgJsrPXyNir0SuBnYMyL27bD47EbtR1L52dMmqczuAp4APh8Rr6N45MfuwHuBPwFvadSOMvOmiHgRuAG4NSIOysxFDQp/PsVl3Vsi4ssUn+MdFOPdwLctSMKeNkkllpktFMXOLIrntX0e2JXibsx7N8D+fgIcQfEYjl9ERFOD4j4E7EfxOc4EpgHPAO+urLK0EfuRVG6R6R9wktQTRcTbgN9T+8N8JfVi9rRJUg/Q8c7UiAjgI5Vff7nxM5LU0zimTZJ6hvsi4naKsXivBA6luPHh+5l5T7dmJqlH8PKoJPUAEXEZRaH2Woo/qOdSPG7k0sx8sTtzk9QzWLRJkiSVgGPaJEmSSsCiTZIkqQQs2iRJkkrAok2SJKkELNokSZJKwKJNkiSpBP4/fCDcRInzeycAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scanpy.pl.pca_variance_ratio(adata_scrnaseq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "978a843e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 30 artists>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(30),adata_scrnaseq.uns['pca']['variance'][:30])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}