{

 "cells": [

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "# Uncertainy quantification\n",

    "\n",

    "BABEL's embedding space also provides a basis for measuring confidence in downstream classifications. Such confidence measures are useful for quantifying how \"trustworthy\" BABEL's predictions might be on new data. \n",

    "\n",

    "Intuitively, within the embedding space, in-distribution examples should share a subspace, and exmaples outside this subspace are likely to be out of distribution and therefore low confidence. Here, we explore this idea by building a Gaussian Process classifier that predicts in-vs-out of distribution, which we interpret informally as an estimate of BABEL's confidence. Although this measure is applied to the embedding, its estimates are valid for output RNA/ATAC modalities as well, since this embedding is a predecessor to those outputs."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "/home/wukevin/projects/babel/data\n"

     ]

    }

   ],

   "source": [

    "import os, sys\n",

    "import collections\n",

    "import functools\n",

    "import json\n",

    "import importlib\n",

    "import logging\n",

    "\n",

    "import numpy as np\n",

    "import pandas as pd\n",

    "from scipy import stats, sparse, spatial\n",

    "from sklearn import metrics\n",

    "from sklearn.gaussian_process import GaussianProcessClassifier\n",

    "from matplotlib import pyplot as plt\n",

    "import seaborn as sns\n",

    "\n",

    "import anndata as ad\n",

    "import scanpy as sc\n",

    "\n",

    "import gdown\n",

    "import tqdm.notebook\n",

    "\n",

    "SRC_DIR = os.path.join(os.path.dirname(os.getcwd()), 'babel')\n",

    "assert os.path.isdir(SRC_DIR)\n",

    "sys.path.append(SRC_DIR)\n",

    "import utils\n",

    "\n",

    "BIN_DIR = os.path.join(os.path.dirname(SRC_DIR), \"bin\")\n",

    "assert os.path.isfile(os.path.join(BIN_DIR, \"predict_model.py\"))\n",

    "\n",

    "import perturb\n",

    "\n",

    "DATA_DIR = os.path.join(os.path.dirname(os.getcwd()), \"data\")\n",

    "assert os.path.isdir(DATA_DIR)\n",

    "print(DATA_DIR)\n",

    "\n",

    "logging.basicConfig(level=logging.INFO)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Data setup\n",

    "\n",

    "First, we download and ensure data is in the expected locations."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [],

   "source": [

    "# PBMC ATAC data\n",

    "pbmc_h5_fname = os.path.join(DATA_DIR, \"10x\", \"atac_v1_pbmc_10k_filtered_peak_bc_matrix.h5\")\n",

    "assert os.path.isfile(pbmc_h5_fname)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Computing MD5: /home/wukevin/projects/babel/data/bcc/GSE129785_scATAC-TME-All.h5ad\n",

      "MD5 matches: /home/wukevin/projects/babel/data/bcc/GSE129785_scATAC-TME-All.h5ad\n"

     ]

    }

   ],

   "source": [

    "# Download BCC ATAC data\n",

    "# https://drive.google.com/file/d/1dv1l-dgrWiHey-RS0SwS_ASn4gVQ_qZu/view?usp=sharing\n",

    "bcc_adata_fname = gdown.cached_download(\n",

    "    url=\"https://drive.google.com/uc?id=1dv1l-dgrWiHey-RS0SwS_ASn4gVQ_qZu\",\n",

    "    path=os.path.join(DATA_DIR, \"bcc/GSE129785_scATAC-TME-All.h5ad\"),\n",

    "    md5=\"09cc204cabd59fdf5aa9c07fa29de961\",\n",

    "    quiet=False,\n",

    ")"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## PBMC perturbation\n",

    "\n",

    "We take PBMC scATAC-seq data and perturb it. Both the original, unperturbed data and the perturbed data are then fed through BABEL to generate corresponding (16-dimensional) embeddings. We then use the original, unperturbed BABEL embeddings as examples of \"in-distribution\" data, and the perturbed BABEL embeddings as examples of \"out-of-distribution\" data to train a Gaussian Process classifier to distinguish between the two. "

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "<function perturb.swap_adata(adata: anndata.core.anndata.AnnData, p: float = 0.1, mode: str = 'zero_nonzero', copy: bool = True, seed: int = 6489) -> anndata.core.anndata.AnnData>"

      ]

     },

     "execution_count": 4,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# Define how we will perform perturbations\n",

    "drop_method = \"swap\"\n",

    "drop_p = 0.5\n",

    "swapper = perturb.swap_adata if drop_method == \"swap\" else perturb.dropout_adata\n",

    "swapper"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "AnnData object with n_obs × n_vars = 8633 × 80234 \n",

       "    var: 'gene_ids', 'feature_types', 'genome'"

      ]

     },

     "execution_count": 5,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "pbmc_atac_vanilla_adata = sc.read_10x_h5(pbmc_h5_fname, gex_only=False)\n",

    "pbmc_atac_vanilla_adata"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 6,

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "INFO:root:Swapping 0.5 of values in each observation\n",

      "... storing 'feature_types' as categorical\n",

      "... storing 'genome' as categorical\n"

     ]

    }

   ],

   "source": [

    "pbmc_atac_dropped_adata = swapper(pbmc_atac_vanilla_adata, p=drop_p)\n",

    "pbmc_atac_dropped_adata_fname = os.path.join(DATA_DIR, \"10x\", \"atac_v1_pbmc_10k_filtered_peak_bc_matrix_dropped.h5ad\")\n",

    "pbmc_atac_dropped_adata.write_h5ad(pbmc_atac_dropped_adata_fname)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "INFO:root:Evaluating: /home/wukevin/projects/babel/data/10x/atac_v1_pbmc_10k_filtered_peak_bc_matrix.h5\n",

      "INFO:root:Model tarball at: /home/wukevin/.cache/babel_atac_rna/babel_human_v1.1.tar.gz\n",

      "INFO:root:Inferred RNA input dimension: 34861\n",

      "INFO:root:Inferred ATAC input dimension: [19602, 9956, 10323, 12262, 8982, 7845, 6207, 7099, 8775, 4851, 5230, 21793, 5815, 2587, 3516, 16973, 14702, 12519, 13318, 11534, 9999, 10009] (sum=223897)\n",

      "INFO:root:Inferred model with basename cv_logsplit_01_model_only be normal (non-naive)\n",

      "INFO:root:ChromDecoder with 1 output activations\n",

      "INFO:root:Loaded model with hidden size 16\n",

      "INFO:root:Building RNA dataset with parameters: {'reader': functools.partial(<function sc_read_multi_files at 0x7fd275298710>, reader=<function load_rna_files_for_eval.<locals>.<lambda> at 0x7fd205bea8c0>), 'transpose': False, 'gtf_file': '/home/wukevin/projects/babel/data/Homo_sapiens.GRCh38.100.gtf.gz', 'autosomes_only': True, 'sort_by_pos': True, 'split_by_chrom': True, 'concat_outputs': True, 'selfsupervise': True, 'binarize': False, 'filt_cell_min_genes': 200, 'filt_cell_max_genes': 7000, 'normalize': True, 'log_trans': True, 'clip': 0.5, 'y_mode': 'size_norm', 'calc_size_factors': True, 'return_sf': False, 'cluster_res': 1.5, 'fname': ['/home/wukevin/projects/babel/data/10x/atac_v1_pbmc_10k_filtered_peak_bc_matrix.h5']}\n",

      "WARNING:root:Error when reading RNA gene expression data from ['/home/wukevin/projects/babel/data/10x/atac_v1_pbmc_10k_filtered_peak_bc_matrix.h5']: No shared genes\n",

      "WARNING:root:Ignoring RNA data\n",

      "INFO:root:Auto-set atac bins fname to /home/wukevin/.cache/babel_atac_rna/cv_logsplit_01_model_only/atac_bins.txt\n",

      "Reading liftover chains\n",

      "Mapping coordinates\n",

      "WARNING:root:Found unmapped regions: 48\n",

      "INFO:root:Read in /home/wukevin/projects/babel/data/10x/atac_v1_pbmc_10k_filtered_peak_bc_matrix.h5 for (8633, 223897) (obs x var)\n",

      "INFO:root:Got data split skip, skipping data split\n",

      "INFO:root:Writing truth ATAC binary counts\n",

      "... storing 'source_file' as categorical\n",

      "... storing 'chrom' as categorical\n",

      "INFO:root:Inferred model to be normal (non-naive)\n",

      "INFO:root:Inferred RNA input dimension: 34861\n",

      "INFO:root:Inferred ATAC input dimension: [19602, 9956, 10323, 12262, 8982, 7845, 6207, 7099, 8775, 4851, 5230, 21793, 5815, 2587, 3516, 16973, 14702, 12519, 13318, 11534, 9999, 10009] (sum=223897)\n",

      "INFO:root:Inferred model with basename cv_logsplit_01_model_only be normal (non-naive)\n",

      "INFO:root:ChromDecoder with 1 output activations\n",

      "INFO:root:Loaded model with hidden size 16\n",

      "INFO:root:Inferring latent representations\n",

      "INFO:root:Inferring RNA from ATAC\n",

      "INFO:root:Writing RNA from ATAC\n"

     ]

    }

   ],

   "source": [

    "%%bash -s  \"$DATA_DIR\" \"$pbmc_h5_fname\"\n",

    "\n",

    "python /home/wukevin/projects/babel/bin/predict_model.py --data ${2} --outdir ${1}/10x/babel_atac_to_rna_pbmc_vanilla --noplot --liftHg19toHg38 --transonly --device 0"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 8,

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "INFO:root:Evaluating: /home/wukevin/projects/babel/data/10x/atac_v1_pbmc_10k_filtered_peak_bc_matrix_dropped.h5ad\n",

      "INFO:root:Model tarball at: /home/wukevin/.cache/babel_atac_rna/babel_human_v1.1.tar.gz\n",

      "INFO:root:Inferred RNA input dimension: 34861\n",

      "INFO:root:Inferred ATAC input dimension: [19602, 9956, 10323, 12262, 8982, 7845, 6207, 7099, 8775, 4851, 5230, 21793, 5815, 2587, 3516, 16973, 14702, 12519, 13318, 11534, 9999, 10009] (sum=223897)\n",

      "INFO:root:Inferred model with basename cv_logsplit_01_model_only be normal (non-naive)\n",

      "INFO:root:ChromDecoder with 1 output activations\n",

      "INFO:root:Loaded model with hidden size 16\n",

      "INFO:root:Building RNA dataset with parameters: {'reader': functools.partial(<function sc_read_multi_files at 0x7fef84e17710>, reader=<function load_rna_files_for_eval.<locals>.<lambda> at 0x7fef0dd068c0>), 'transpose': False, 'gtf_file': '/home/wukevin/projects/babel/data/Homo_sapiens.GRCh38.100.gtf.gz', 'autosomes_only': True, 'sort_by_pos': True, 'split_by_chrom': True, 'concat_outputs': True, 'selfsupervise': True, 'binarize': False, 'filt_cell_min_genes': 200, 'filt_cell_max_genes': 7000, 'normalize': True, 'log_trans': True, 'clip': 0.5, 'y_mode': 'size_norm', 'calc_size_factors': True, 'return_sf': False, 'cluster_res': 1.5, 'fname': ['/home/wukevin/projects/babel/data/10x/atac_v1_pbmc_10k_filtered_peak_bc_matrix_dropped.h5ad']}\n",

      "WARNING:root:Error when reading RNA gene expression data from ['/home/wukevin/projects/babel/data/10x/atac_v1_pbmc_10k_filtered_peak_bc_matrix_dropped.h5ad']: No shared genes\n",

      "WARNING:root:Ignoring RNA data\n",

      "INFO:root:Auto-set atac bins fname to /home/wukevin/.cache/babel_atac_rna/cv_logsplit_01_model_only/atac_bins.txt\n",

      "Reading liftover chains\n",

      "Mapping coordinates\n",

      "WARNING:root:Found unmapped regions: 48\n",

      "INFO:root:Read in /home/wukevin/projects/babel/data/10x/atac_v1_pbmc_10k_filtered_peak_bc_matrix_dropped.h5ad for (8633, 223897) (obs x var)\n",

      "INFO:root:Got data split skip, skipping data split\n",

      "INFO:root:Writing truth ATAC binary counts\n",

      "... storing 'source_file' as categorical\n",

      "... storing 'chrom' as categorical\n",

      "INFO:root:Inferred model to be normal (non-naive)\n",

      "INFO:root:Inferred RNA input dimension: 34861\n",

      "INFO:root:Inferred ATAC input dimension: [19602, 9956, 10323, 12262, 8982, 7845, 6207, 7099, 8775, 4851, 5230, 21793, 5815, 2587, 3516, 16973, 14702, 12519, 13318, 11534, 9999, 10009] (sum=223897)\n",

      "INFO:root:Inferred model with basename cv_logsplit_01_model_only be normal (non-naive)\n",

      "INFO:root:ChromDecoder with 1 output activations\n",

      "INFO:root:Loaded model with hidden size 16\n",

      "INFO:root:Inferring latent representations\n",

      "INFO:root:Inferring RNA from ATAC\n",

      "INFO:root:Writing RNA from ATAC\n"

     ]

    }

   ],

   "source": [

    "%%bash -s  \"$DATA_DIR\" \"$pbmc_atac_dropped_adata_fname\"\n",

    "\n",

    "python /home/wukevin/projects/babel/bin/predict_model.py --data ${2} --outdir ${1}/10x/babel_atac_to_rna_pbmc_dropped --noplot --liftHg19toHg38 --transonly --device 0"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 15,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "AnnData object with n_obs × n_vars = 8633 × 16 \n",

       "    obs: 'source_file', 'n_counts', 'log1p_counts', 'n_genes', 'size_factors'"

      ]

     },

     "execution_count": 15,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "pbmc_vanilla_embed = ad.read_h5ad(os.path.join(\n",

    "    DATA_DIR,\n",

    "    \"10x/babel_atac_to_rna_pbmc_vanilla/atac_encoded_adata.h5ad\",\n",

    "))\n",

    "pbmc_vanilla_embed"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 16,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "AnnData object with n_obs × n_vars = 8633 × 16 \n",

       "    obs: 'source_file', 'n_counts', 'log1p_counts', 'n_genes', 'size_factors'"

      ]

     },

     "execution_count": 16,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "pbmc_dropped_embed = ad.read_h5ad(os.path.join(\n",

    "    DATA_DIR,\n",

    "    \"10x/babel_atac_to_rna_pbmc_dropped/atac_encoded_adata.h5ad\",\n",

    "))\n",

    "pbmc_dropped_embed"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 17,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "GaussianProcessClassifier(copy_X_train=True, kernel=None, max_iter_predict=100,\n",

       "                          multi_class='one_vs_rest', n_jobs=None,\n",

       "                          n_restarts_optimizer=0, optimizer='fmin_l_bfgs_b',\n",

       "                          random_state=1234, warm_start=False)"

      ]

     },

     "execution_count": 17,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "pbmc_gp = GaussianProcessClassifier(random_state=1234)\n",

    "# label of 1 = in distribution, 0 = out of distribution\n",

    "pbmc_gp.fit(\n",

    "    np.vstack([pbmc_vanilla_embed.X, pbmc_dropped_embed.X]),\n",

    "    [1] * pbmc_vanilla_embed.n_obs + [0] * pbmc_dropped_embed.n_obs,\n",

    ")\n",

    "pbmc_gp"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## BCC data\n",

    "\n",

    "We turn to the basal cell carcinoma (BCC) dataset (Yost et al., https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7299161/) discussed in our manuscript. \n",

    "\n",

    "We know that, biologically, the BCC dataset contains celltypes (particularly, endothelial skin cells and tumor cells) that are out-of-distribution with respect to BABEL's training data; therefore, we expect that the GP trained above will produce low prediction scores (interpreted as low confidence) for these celltypes. We verify this below."

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 13,

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "INFO:root:Evaluating: /home/wukevin/projects/babel/data/bcc/GSE129785_scATAC-TME-All.h5ad\n",

      "INFO:root:Model tarball at: /home/wukevin/.cache/babel_atac_rna/babel_human_v1.1.tar.gz\n",

      "INFO:root:Inferred RNA input dimension: 34861\n",

      "INFO:root:Inferred ATAC input dimension: [19602, 9956, 10323, 12262, 8982, 7845, 6207, 7099, 8775, 4851, 5230, 21793, 5815, 2587, 3516, 16973, 14702, 12519, 13318, 11534, 9999, 10009] (sum=223897)\n",

      "INFO:root:Inferred model with basename cv_logsplit_01_model_only be normal (non-naive)\n",

      "INFO:root:ChromDecoder with 1 output activations\n",

      "INFO:root:Loaded model with hidden size 16\n",

      "INFO:root:Building RNA dataset with parameters: {'reader': functools.partial(<function sc_read_multi_files at 0x7f157d883680>, reader=<function load_rna_files_for_eval.<locals>.<lambda> at 0x7f15075c1830>), 'transpose': False, 'gtf_file': '/home/wukevin/projects/babel/data/Homo_sapiens.GRCh38.100.gtf.gz', 'autosomes_only': True, 'sort_by_pos': True, 'split_by_chrom': True, 'concat_outputs': True, 'selfsupervise': True, 'binarize': False, 'filt_cell_min_genes': 200, 'filt_cell_max_genes': 7000, 'normalize': True, 'log_trans': True, 'clip': 0.5, 'y_mode': 'size_norm', 'calc_size_factors': True, 'return_sf': False, 'cluster_res': 1.5, 'fname': ['/home/wukevin/projects/babel/data/bcc/GSE129785_scATAC-TME-All.h5ad']}\n",

      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",

      "WARNING:root:Error when reading RNA gene expression data from ['/home/wukevin/projects/babel/data/bcc/GSE129785_scATAC-TME-All.h5ad']: No shared genes\n",

      "WARNING:root:Ignoring RNA data\n",

      "INFO:root:Auto-set atac bins fname to /home/wukevin/.cache/babel_atac_rna/cv_logsplit_01_model_only/atac_bins.txt\n",

      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",

      "Reading liftover chains\n",

      "Mapping coordinates\n",

      "WARNING:root:Found unmapped regions: 182\n",

      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",

      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",

      "INFO:root:Read in /home/wukevin/projects/babel/data/bcc/GSE129785_scATAC-TME-All.h5ad for (37818, 223897) (obs x var)\n",

      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",

      "INFO:root:Got data split skip, skipping data split\n",

      "INFO:root:Writing truth ATAC binary counts\n",

      "... storing 'source_file' as categorical\n",

      "... storing 'chrom' as categorical\n",

      "INFO:root:Inferred model to be normal (non-naive)\n",

      "INFO:root:Inferred RNA input dimension: 34861\n",

      "INFO:root:Inferred ATAC input dimension: [19602, 9956, 10323, 12262, 8982, 7845, 6207, 7099, 8775, 4851, 5230, 21793, 5815, 2587, 3516, 16973, 14702, 12519, 13318, 11534, 9999, 10009] (sum=223897)\n",

      "INFO:root:Inferred model with basename cv_logsplit_01_model_only be normal (non-naive)\n",

      "INFO:root:ChromDecoder with 1 output activations\n",

      "INFO:root:Loaded model with hidden size 16\n",

      "INFO:root:Inferring latent representations\n",

      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",

      "INFO:root:Inferring RNA from ATAC\n",

      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n",

      "INFO:root:Writing RNA from ATAC\n",

      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n"

     ]

    }

   ],

   "source": [

    "%%bash -s  \"$DATA_DIR\" \"$bcc_adata_fname\"\n",

    "\n",

    "python /home/wukevin/projects/babel/bin/predict_model.py --data ${2} --outdir ${1}/bcc/babel_atac_to_rna_bcc --noplot --liftHg19toHg38 --transonly --device 0"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 18,

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n"

     ]

    },

    {

     "data": {

      "text/plain": [

       "(37818, 2)"

      ]

     },

     "execution_count": 18,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# Run the GP on BCC's embedding\n",

    "bcc_vanilla_embed = ad.read_h5ad(os.path.join(\n",

    "    DATA_DIR,\n",

    "    \"bcc/babel_atac_to_rna_bcc/atac_encoded_adata.h5ad\",\n",

    "))\n",

    "\n",

    "bcc_gp_preds = pbmc_gp.predict_proba(bcc_vanilla_embed.X)\n",

    "bcc_gp_preds.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 19,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

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       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>UMAP1</th>\n",

       "      <th>UMAP2</th>\n",

       "      <th>Clusters</th>\n",

       "      <th>Group</th>\n",

       "      <th>depth</th>\n",

       "      <th>FRIP</th>\n",

       "      <th>Barcodes</th>\n",

       "      <th>Internal_Name</th>\n",

       "      <th>Group_Barcode</th>\n",

       "      <th>source_file</th>\n",

       "      <th>n_counts</th>\n",

       "      <th>log1p_counts</th>\n",

       "      <th>n_genes</th>\n",

       "      <th>size_factors</th>\n",

       "      <th>gp_pbmc_pred</th>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>index</th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGAACGACC-1</th>\n",

       "      <td>10.567199</td>\n",

       "      <td>-4.781785</td>\n",

       "      <td>Cluster20</td>\n",

       "      <td>SU009_Tumor_Immune_Pre</td>\n",

       "      <td>62437</td>\n",

       "      <td>0.581338</td>\n",

       "      <td>AAACGAAAGAACGACC-1</td>\n",

       "      <td>SU009_Tumor_Immune_Pre_3</td>\n",

       "      <td>SU009_Tumor_Immune_Pre#AAACGAAAGAACGACC-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>5774.0</td>\n",

       "      <td>8.661294</td>\n",

       "      <td>5774</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.027143</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGAATACTG-1</th>\n",

       "      <td>1.443223</td>\n",

       "      <td>13.324852</td>\n",

       "      <td>Cluster14</td>\n",

       "      <td>SU001_Immune_Post2</td>\n",

       "      <td>7471</td>\n",

       "      <td>0.378932</td>\n",

       "      <td>AAACGAAAGAATACTG-1</td>\n",

       "      <td>SU001_Immune_Post2_860</td>\n",

       "      <td>SU001_Immune_Post2#AAACGAAAGAATACTG-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>1222.0</td>\n",

       "      <td>7.109062</td>\n",

       "      <td>1222</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.708951</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGACACGGT-1</th>\n",

       "      <td>-1.004199</td>\n",

       "      <td>-7.261578</td>\n",

       "      <td>Cluster4</td>\n",

       "      <td>SU009_Tcell_Post</td>\n",

       "      <td>6832</td>\n",

       "      <td>0.712310</td>\n",

       "      <td>AAACGAAAGACACGGT-1</td>\n",

       "      <td>SU009_Tcell_Post_3423</td>\n",

       "      <td>SU009_Tcell_Post#AAACGAAAGACACGGT-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>1112.0</td>\n",

       "      <td>7.014814</td>\n",

       "      <td>1112</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.701958</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGACCCTAT-1</th>\n",

       "      <td>-5.697628</td>\n",

       "      <td>13.187097</td>\n",

       "      <td>Cluster12</td>\n",

       "      <td>SU001_Total_Post2</td>\n",

       "      <td>7808</td>\n",

       "      <td>0.488217</td>\n",

       "      <td>AAACGAAAGACCCTAT-1</td>\n",

       "      <td>SU001_Total_Post2_795</td>\n",

       "      <td>SU001_Total_Post2#AAACGAAAGACCCTAT-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>1063.0</td>\n",

       "      <td>6.969790</td>\n",

       "      <td>1063</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.917634</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGAGGTACC-1</th>\n",

       "      <td>-5.956334</td>\n",

       "      <td>-3.010488</td>\n",

       "      <td>Cluster8</td>\n",

       "      <td>SU009_Tcell_Pre</td>\n",

       "      <td>9788</td>\n",

       "      <td>0.737229</td>\n",

       "      <td>AAACGAAAGAGGTACC-1</td>\n",

       "      <td>SU009_Tcell_Pre_3839</td>\n",

       "      <td>SU009_Tcell_Pre#AAACGAAAGAGGTACC-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>1811.0</td>\n",

       "      <td>7.502186</td>\n",

       "      <td>1811</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.960398</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                        UMAP1      UMAP2   Clusters                   Group  \\\n",

       "index                                                                         \n",

       "AAACGAAAGAACGACC-1  10.567199  -4.781785  Cluster20  SU009_Tumor_Immune_Pre   \n",

       "AAACGAAAGAATACTG-1   1.443223  13.324852  Cluster14      SU001_Immune_Post2   \n",

       "AAACGAAAGACACGGT-1  -1.004199  -7.261578   Cluster4        SU009_Tcell_Post   \n",

       "AAACGAAAGACCCTAT-1  -5.697628  13.187097  Cluster12       SU001_Total_Post2   \n",

       "AAACGAAAGAGGTACC-1  -5.956334  -3.010488   Cluster8         SU009_Tcell_Pre   \n",

       "\n",

       "                    depth      FRIP            Barcodes  \\\n",

       "index                                                     \n",

       "AAACGAAAGAACGACC-1  62437  0.581338  AAACGAAAGAACGACC-1   \n",

       "AAACGAAAGAATACTG-1   7471  0.378932  AAACGAAAGAATACTG-1   \n",

       "AAACGAAAGACACGGT-1   6832  0.712310  AAACGAAAGACACGGT-1   \n",

       "AAACGAAAGACCCTAT-1   7808  0.488217  AAACGAAAGACCCTAT-1   \n",

       "AAACGAAAGAGGTACC-1   9788  0.737229  AAACGAAAGAGGTACC-1   \n",

       "\n",

       "                               Internal_Name  \\\n",

       "index                                          \n",

       "AAACGAAAGAACGACC-1  SU009_Tumor_Immune_Pre_3   \n",

       "AAACGAAAGAATACTG-1    SU001_Immune_Post2_860   \n",

       "AAACGAAAGACACGGT-1     SU009_Tcell_Post_3423   \n",

       "AAACGAAAGACCCTAT-1     SU001_Total_Post2_795   \n",

       "AAACGAAAGAGGTACC-1      SU009_Tcell_Pre_3839   \n",

       "\n",

       "                                                Group_Barcode  \\\n",

       "index                                                           \n",

       "AAACGAAAGAACGACC-1  SU009_Tumor_Immune_Pre#AAACGAAAGAACGACC-1   \n",

       "AAACGAAAGAATACTG-1      SU001_Immune_Post2#AAACGAAAGAATACTG-1   \n",

       "AAACGAAAGACACGGT-1        SU009_Tcell_Post#AAACGAAAGACACGGT-1   \n",

       "AAACGAAAGACCCTAT-1       SU001_Total_Post2#AAACGAAAGACCCTAT-1   \n",

       "AAACGAAAGAGGTACC-1         SU009_Tcell_Pre#AAACGAAAGAGGTACC-1   \n",

       "\n",

       "                                                          source_file  \\\n",

       "index                                                                   \n",

       "AAACGAAAGAACGACC-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "AAACGAAAGAATACTG-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "AAACGAAAGACACGGT-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "AAACGAAAGACCCTAT-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "AAACGAAAGAGGTACC-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "\n",

       "                    n_counts  log1p_counts  n_genes  size_factors  \\\n",

       "index                                                               \n",

       "AAACGAAAGAACGACC-1    5774.0      8.661294     5774           1.0   \n",

       "AAACGAAAGAATACTG-1    1222.0      7.109062     1222           1.0   \n",

       "AAACGAAAGACACGGT-1    1112.0      7.014814     1112           1.0   \n",

       "AAACGAAAGACCCTAT-1    1063.0      6.969790     1063           1.0   \n",

       "AAACGAAAGAGGTACC-1    1811.0      7.502186     1811           1.0   \n",

       "\n",

       "                    gp_pbmc_pred  \n",

       "index                             \n",

       "AAACGAAAGAACGACC-1      0.027143  \n",

       "AAACGAAAGAATACTG-1      0.708951  \n",

       "AAACGAAAGACACGGT-1      0.701958  \n",

       "AAACGAAAGACCCTAT-1      0.917634  \n",

       "AAACGAAAGAGGTACC-1      0.960398  "

      ]

     },

     "execution_count": 19,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "bcc_vanilla_embed.obs['gp_pbmc_pred'] = bcc_gp_preds[:, 1]\n",

    "bcc_vanilla_embed.obs.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 21,

   "metadata": {},

   "outputs": [

    {

     "data": {

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       "  <thead>\n",

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       "      <th></th>\n",

       "      <th>UMAP1</th>\n",

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       "  <tbody>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGAACGACC-1</th>\n",

       "      <td>10.567199</td>\n",

       "      <td>-4.781785</td>\n",

       "      <td>Cluster20</td>\n",

       "      <td>SU009_Tumor_Immune_Pre</td>\n",

       "      <td>62437</td>\n",

       "      <td>0.581338</td>\n",

       "      <td>AAACGAAAGAACGACC-1</td>\n",

       "      <td>SU009_Tumor_Immune_Pre_3</td>\n",

       "      <td>SU009_Tumor_Immune_Pre#AAACGAAAGAACGACC-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>5774.0</td>\n",

       "      <td>8.661294</td>\n",

       "      <td>5774</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.027143</td>\n",

       "      <td>Tumor 4</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGAATACTG-1</th>\n",

       "      <td>1.443223</td>\n",

       "      <td>13.324852</td>\n",

       "      <td>Cluster14</td>\n",

       "      <td>SU001_Immune_Post2</td>\n",

       "      <td>7471</td>\n",

       "      <td>0.378932</td>\n",

       "      <td>AAACGAAAGAATACTG-1</td>\n",

       "      <td>SU001_Immune_Post2_860</td>\n",

       "      <td>SU001_Immune_Post2#AAACGAAAGAATACTG-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>1222.0</td>\n",

       "      <td>7.109062</td>\n",

       "      <td>1222</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.708951</td>\n",

       "      <td>Myeloid</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGACACGGT-1</th>\n",

       "      <td>-1.004199</td>\n",

       "      <td>-7.261578</td>\n",

       "      <td>Cluster4</td>\n",

       "      <td>SU009_Tcell_Post</td>\n",

       "      <td>6832</td>\n",

       "      <td>0.712310</td>\n",

       "      <td>AAACGAAAGACACGGT-1</td>\n",

       "      <td>SU009_Tcell_Post_3423</td>\n",

       "      <td>SU009_Tcell_Post#AAACGAAAGACACGGT-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>1112.0</td>\n",

       "      <td>7.014814</td>\n",

       "      <td>1112</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.701958</td>\n",

       "      <td>Regulatory CD4+ T cells</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGACCCTAT-1</th>\n",

       "      <td>-5.697628</td>\n",

       "      <td>13.187097</td>\n",

       "      <td>Cluster12</td>\n",

       "      <td>SU001_Total_Post2</td>\n",

       "      <td>7808</td>\n",

       "      <td>0.488217</td>\n",

       "      <td>AAACGAAAGACCCTAT-1</td>\n",

       "      <td>SU001_Total_Post2_795</td>\n",

       "      <td>SU001_Total_Post2#AAACGAAAGACCCTAT-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>1063.0</td>\n",

       "      <td>6.969790</td>\n",

       "      <td>1063</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.917634</td>\n",

       "      <td>B</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>AAACGAAAGAGGTACC-1</th>\n",

       "      <td>-5.956334</td>\n",

       "      <td>-3.010488</td>\n",

       "      <td>Cluster8</td>\n",

       "      <td>SU009_Tcell_Pre</td>\n",

       "      <td>9788</td>\n",

       "      <td>0.737229</td>\n",

       "      <td>AAACGAAAGAGGTACC-1</td>\n",

       "      <td>SU009_Tcell_Pre_3839</td>\n",

       "      <td>SU009_Tcell_Pre#AAACGAAAGAGGTACC-1</td>\n",

       "      <td>/home/wukevin/projects/babel/data/bcc/GSE12978...</td>\n",

       "      <td>1811.0</td>\n",

       "      <td>7.502186</td>\n",

       "      <td>1811</td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.960398</td>\n",

       "      <td>CD8 TEx</td>\n",

       "    </tr>\n",

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       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                        UMAP1      UMAP2   Clusters                   Group  \\\n",

       "index                                                                         \n",

       "AAACGAAAGAACGACC-1  10.567199  -4.781785  Cluster20  SU009_Tumor_Immune_Pre   \n",

       "AAACGAAAGAATACTG-1   1.443223  13.324852  Cluster14      SU001_Immune_Post2   \n",

       "AAACGAAAGACACGGT-1  -1.004199  -7.261578   Cluster4        SU009_Tcell_Post   \n",

       "AAACGAAAGACCCTAT-1  -5.697628  13.187097  Cluster12       SU001_Total_Post2   \n",

       "AAACGAAAGAGGTACC-1  -5.956334  -3.010488   Cluster8         SU009_Tcell_Pre   \n",

       "\n",

       "                    depth      FRIP            Barcodes  \\\n",

       "index                                                     \n",

       "AAACGAAAGAACGACC-1  62437  0.581338  AAACGAAAGAACGACC-1   \n",

       "AAACGAAAGAATACTG-1   7471  0.378932  AAACGAAAGAATACTG-1   \n",

       "AAACGAAAGACACGGT-1   6832  0.712310  AAACGAAAGACACGGT-1   \n",

       "AAACGAAAGACCCTAT-1   7808  0.488217  AAACGAAAGACCCTAT-1   \n",

       "AAACGAAAGAGGTACC-1   9788  0.737229  AAACGAAAGAGGTACC-1   \n",

       "\n",

       "                               Internal_Name  \\\n",

       "index                                          \n",

       "AAACGAAAGAACGACC-1  SU009_Tumor_Immune_Pre_3   \n",

       "AAACGAAAGAATACTG-1    SU001_Immune_Post2_860   \n",

       "AAACGAAAGACACGGT-1     SU009_Tcell_Post_3423   \n",

       "AAACGAAAGACCCTAT-1     SU001_Total_Post2_795   \n",

       "AAACGAAAGAGGTACC-1      SU009_Tcell_Pre_3839   \n",

       "\n",

       "                                                Group_Barcode  \\\n",

       "index                                                           \n",

       "AAACGAAAGAACGACC-1  SU009_Tumor_Immune_Pre#AAACGAAAGAACGACC-1   \n",

       "AAACGAAAGAATACTG-1      SU001_Immune_Post2#AAACGAAAGAATACTG-1   \n",

       "AAACGAAAGACACGGT-1        SU009_Tcell_Post#AAACGAAAGACACGGT-1   \n",

       "AAACGAAAGACCCTAT-1       SU001_Total_Post2#AAACGAAAGACCCTAT-1   \n",

       "AAACGAAAGAGGTACC-1         SU009_Tcell_Pre#AAACGAAAGAGGTACC-1   \n",

       "\n",

       "                                                          source_file  \\\n",

       "index                                                                   \n",

       "AAACGAAAGAACGACC-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "AAACGAAAGAATACTG-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "AAACGAAAGACACGGT-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "AAACGAAAGACCCTAT-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "AAACGAAAGAGGTACC-1  /home/wukevin/projects/babel/data/bcc/GSE12978...   \n",

       "\n",

       "                    n_counts  log1p_counts  n_genes  size_factors  \\\n",

       "index                                                               \n",

       "AAACGAAAGAACGACC-1    5774.0      8.661294     5774           1.0   \n",

       "AAACGAAAGAATACTG-1    1222.0      7.109062     1222           1.0   \n",

       "AAACGAAAGACACGGT-1    1112.0      7.014814     1112           1.0   \n",

       "AAACGAAAGACCCTAT-1    1063.0      6.969790     1063           1.0   \n",

       "AAACGAAAGAGGTACC-1    1811.0      7.502186     1811           1.0   \n",

       "\n",

       "                    gp_pbmc_pred            ClustersNamed  \n",

       "index                                                      \n",

       "AAACGAAAGAACGACC-1      0.027143                  Tumor 4  \n",

       "AAACGAAAGAATACTG-1      0.708951                  Myeloid  \n",

       "AAACGAAAGACACGGT-1      0.701958  Regulatory CD4+ T cells  \n",

       "AAACGAAAGACCCTAT-1      0.917634                        B  \n",

       "AAACGAAAGAGGTACC-1      0.960398                  CD8 TEx  "

      ]

     },

     "execution_count": 21,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "bcc_vanilla_embed.obs['ClustersNamed'] = [bcc_cluster_to_name[n] for n in bcc_vanilla_embed.obs['Clusters']]\n",

    "bcc_vanilla_embed.obs.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 24,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "image/png": 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UJU3+Immbou/FDEWlq/In1I6WdJ2ZHZN0zxdJMrO5ip6HuJUiDyq62vchRaWfpubGF7ey5FRJo81sUZk9c0blzjVwFdndiq4U36jo9TlZUSLhFEkT8o5/t5ld5+5l/6zUyDkaPIF+yzB9CV6g+J4kNwUbVfEWaPBrvk/DN3Cvd99RNPEc54+KVlg9rKi3wgGKXpsvUHThwVEqYcI6lxjpUPxKujsUJU3WK3qfOlDR5/OrFCVhB34+P1vRSqqXuvufiz1/gnZo/8/al+Ttf1LDf97m92f7iaLn/fAB286SdFmpg8utNvvnvM33lbH66jva/3PmL4pWvt2v6H3r2Yr61JykwT/rr5P0vdy+olT7M92inkL/q8EXGLiiz9FbFX1P/qaof9rBivqdvEDR9/wlKi+BAwCoZ+7OjRs3bty4ceNW8KboD0XPu51Xw/FMVtTANX9Mf5Z0whDHPVdR34X841zSSUWeO/+4ngH//42klw5x7AxJv42J8dMSvvYL848v4/krK4akOXlfb//tx4pqmhc6brGiSaP+xz9d5vknKZokyT//NyVlhjn2cEXJm/xjVxdx3mOH+b6vlDRtiONfo6j5d36MjxX5dZukNTHH90n6mqTpRcR4iaTPKZpgOnaYxx6qaOJy4Ln25o5vHebYIyXdFTPWL5T6Oi3jdT1aUUmW/HNvlvSPw7yuvhVznEv6YJHnfnSI18d6SccNcWxrgdfm/Uk/ZwPGkH/uR8uIsURR8iw/1uuHOe49Mcdsk2TV+voHjOU/Ysbyh2qPI/DXdH6B1/YvJb2iiOOnK+oB8bCkC4d57IGKEiD55/qRpLnDHDtV0cR8/rF3SGouYpz5P4PfLvL5Kfe4/HEO+dwMEee8mFgF3y+GiHNqTJxzhjnm5iHet3blXjuxz72ihO43Cry2lhU55qp/pisq+5h/zG8lvbDIMU9VtELnd5LOKud7zo0bN27c6u9W8wFw48aNGzdu3Or3lpvs6Iv5Y/I1NRzTV2PGs1FDTNAPONYKTMA8JamliOPjJgL6J3/GFHH8ZO2fKHBFVycOOREw4PgL889dxvNXVgxFV3Lnf93fl9RUxLFzFF1ZG/v8FXn+VXnH7ZP07hK+bstNuuSfv22Y444d4vv+70We+9UxP0cPFnnsWTHn3SXpnWV87w+WNHmYx9yed66dKmGyLvee8bO8GHskPa/U8Zb4tX0i5nnaLGlekcd/psDzXEzy6dECr487hnu+c8ePlvSHmOPnJ/mcDTh//nkfHebxzYquvD5S0UThrwp8/RcXce5Pxxz3y2p83TFj+d+YsXyrFmMJ9PW8KPc+mf81XaYi3rdjvudD/gxLujjmXB8t8TwXxsR4RxHH5f8MfrvI85V7XP4YLyzzezRJgy8Y+EEZcX6eF2OHpIOHOebmAj+3eyUtLPK8n405vlvShCKOrfpnuqQf5D22S9IhZX7vDirnOG7cuHHjVn83eo4AAIChTFd8CYGg5ZqKZWZTFF1pPFCvpFPd/bHhjnd3V9Ts8w95u1pj4hbrUUVXpu8q4vzdki7K29ykqBFt3TKzF2twebWHJC11977hjnf39Yp6KZR7/jmKGoIP9El3/26xMXLf+zMVlQcZ6GNlDuuH7l5Uzxh3v03RpMxAM83s8LjH98v1cIgb3wfc/XvFDXO/cfzNhyhxZGZv0ODeOae7e9Eljtx9j6S3KSrR0+8ARQ3PE5F7nuJK453m7vcWE8PdP6noSuSBRisqFVWOrZLeOtTzPeDcuyX9W8yu48s8d6Web2Ze6KZoEnOLolJhV2lwObVuSWe6e1wJvnzPi9lWk88X1ddYQviEBvegWOXuHyrmfXsgd+91978U2p/rVfGBvM3L3f2/SjzPhYrKfA1U7nt03XP3LYqSBAOdbGbTio2R67VxXN7m77r738oc1qfdfW0xD3T3jylKsgw0ScP8PlXDz/T8EpPt7r652HPmnf/pco4DANQfkiMAAGAokwps31rNQQzwzxrct+Mqd7+72AAe9UrJn8SRyu87cpGX1rfie4oSOgNV3Kg8YWfFbDsvN6lbFHe/UdEKm3J8RPv/3vqIpEtLDeJR34uL8za/qYzm232Kn8weyv/GbBvu+/5OSc/P23aju3+txHMX66N599e5+/dLDZL7efjvvM2Lyx7V8E5V1LtgoB+7e6mvt/dr8M/mGbnkS6n+292zJTz+Z4quYh6o3t8X8v1B0jJFq/i+WuQxcZ8xW4ONqDT1NJaKmNkMSW/P29wl6X0JnfIc7d83YocGv58U61N594/MfT2NKr8xe1z/kKGcpcEXsSwvcyyPSyopoaX436fifmcYqFaf6ePz7peVGAEANBaSIwAAYCgtBbaX08Q6hLgVFv9TahB3v0XSfXmbX2xmcY2Bh7JTg6/6HO7cWxQ1hx5oTonnrbY35d3vlHRDGXGuKvUAMzNFTasH+ra7509iF+vHefdHK2qMXYqb3P3hEo/piNk23Pd9Ucy2z5V43qKY2WQNvvr46xWEzH99PD/XrDkJce8LV5YaJPc9vTFv8yGSXlrGmEpKYOVez7/N21zv7wv5XqwoKXaxmcWtwogT9xlTq8+XehpLpd6iwatGllewmmA4b827/4MKznW7BielXlNmrLrn7vcoalQ/0L+YWf73b5BcEmBZ3uaOUi4YyfOdYlbBDuTuf1TUzHygFxZ6D6jxZ3p+MmR+mecEADQQkiMAAGAocSW1pKhWc1XlJgqOztv8QO4P83LklzmSpFeVGOM3uTJCpcqfWJ9QRoyqMLNnKWrSPdCaMicyblSUUCrFPA2+ovv2Ms4t6e+lzfInPI8qMcwtZZz3KQ3+2gt+33MTSPkTgo+XUuKqRAs0+Oe97OdZ0ZXA+Up9nov16rz7OzW4RFaxriki/nAedvfHyzh3vbwv7JX0+2FuDyrq1ZRvoqIG3n8ys9OLOFfcZ0zVP19y6mkslTo2Ztt3kjhRrqTWi/M2V/Ie3Sfpz3mbk3rvqBf5q0eepyjBNZx3KOplNlDJF4wMcF2Zx/0wZtsrCzy2lp/p+RcpzDezz5jZqHLPDwBIPz4EAADAUHoKbJ+oqMF2Nc3R/mU7JOmuCuLdGbPtpYr/I7+Q/BUgxcr/Q75ukyOKL+2Tf4V7Udx9n5ndq8F9LYYSNzF9uZkVXdIrxkF596eUeHwl3/exA+4P9X0/XFEvnIEqSVYMJ+55Xp3rMRFKqc/zsMxsrKTZeZvvruAq5ELvC6VI+/vCX939yGIeaGaHKEqs/bP2n8wdJ+kbZtY6TN+JuM+YiUWOM7R6Gkul8t9ju9y93NdlMefKv+jy42ZWbqlKSZqZdz/4e0edWa1oRebA8oDvk3T9MMfll0nrllRyKcScfYoSn+X4Xcy2lyn+IpRafqZ/W9K52j8R+glJS8zsW4p6kOT3pAMANDiSIwAAYCiFmglPVPWTI3F/7OY34izFn4o8x1CGbbZcwN68++X0NKiWZ8VsW19BvAdUWnLkuTHbjqjg/HEOKfHx1fi+PztmW5KTNnHP87zA5yj1eS42Zv4V/5W8LzygqKfMwMneWr0v1P3farlmxtdJus7MTlVUZvDAAQ+5xMx+5+75Tbb7xT1XE4MOsnj1NJZK5b9vV/u9I7/xdaWSeO+oG+6+18y+KumCAZuPN7ND3T1uFZ7M7EgNLh/17VLLYg2QreDYB2K2xf3uINXwM93d7zGzL0v6YN6uGZIuknSRmT0l6VZFifLbFZUpqyRxAwCoc5TVAgAAQ3lc8WVF4iZukxa6We6WmG355SmGkz+Z2YgmxmyrpA5/qcdWY1KsUG+dQqrxfY97Lca9ZkOpx+e5GEHfF3IlffJ7JfC+UAR3X62opNZAJul/zKzQ352PxWyrxeeLVF9jKZuZjdfgxCvvHfXvKu3/3mGSzhzi8fmrRlxl9PUaIPTn+sQCj6316+U8SV8cYv+zFPVEuURRCc2tZnajmf1T7mcLANBgSI4AAICCcv00Ho3Z9fIqD0WS4v4oLbV/xXDH8ofvYNV43ocSN/k9Ehwcs21HgudL6/Mc+vUZdzzvC8X7qgb3izhc0psLPD5uFdpLcz13qi1uLLX4rKsU7x0p5O6dktrzNp9uZgfmP9bMDpa0JG/zL9x9QwVDKPt90913avCFNIXeN2v6enH3Xnc/T1EPux8pKic2lDGS3ijpm5IeNbOP06MEABoLb+oAAGA4v9Xghtz5jdGrYXvMtrEx24oVd2zcOUa6ajzvQ4nrAzDJ3bdWMIY0yF+9IA3uuRNS/vO81d3TMOkZ+vUZdzzvC0Vy914z+6kGX/F+vKS1MYfE9S+aoKjHVFypniTFjeUFZjY2N/mbFrV+75CkI9293P4VI9lXJL19wP1WSW9VVK5uoPdo8Pd0eYXnLvt9M9f7KT+hWeh9sy4+0939TkmLzOxZkhZKeq2ifij5v+8ONFnSxZJOMrMTRsDvIQAwIrByBAAADOfmmG3HmFlzlccRVxZkYgXx4o4tt1dAI9sas62SRtGlHrspZtuMCs6fFptjtiWZrMh/niea2cQEzxdK0PeFXPmn/CvveV8oTVzj75cUeOyfFN+/6rhwwynarRp8FXmzpNfUYCxlc/ftkvbkba7me4c09AQzCnD3dRrcFD2/fJYknZV3v1PSmgpPH/pzfWuBx9bVZ7q7P+Xu33T397j7YZKmKkpIfVnx72VS1OvlmmqNEQCQLJIjAABgOD/S4HIJ0yQtqvI4umK2za0g3gtitsX90T7SPRWzbU4F8UptvBo3cRq6UXg9eiJmW5Jfd1qf500a/P5UyfvCHA3+G4n3hdLErVyIbWrv7i7phphdQ/VaSIS7b5P0q3oYSwD5P88vruK5pHS8d9SrK/LuzzezF/XfMbP5kl6U95ivuftw5aGGkzGzMWUeG/c7QdzvDlKdv17c/Ul3X+3uH3T32ZJeJul7MQ99g5m9qcrDAwAkgOQIAAAYkrtnJf0iZtfZVR7KBg2um15JPfhXxGyLK6sy0sU9Jy8rJ1CuTne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",

      "text/plain": [

       "<Figure size 1800x1200 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    }

   ],

   "source": [

    "with open(os.path.join(DATA_DIR, \"bcc/bcc_cluster_to_celltype.json\")) as source:\n",

    "    bcc_cluster_to_name = {f\"Cluster{i+1}\": cellname for i, cellname in enumerate(json.load(source))}\n",

    "\n",

    "fig, ax = plt.subplots(dpi=300)\n",

    "gp_pbmc_ordered_celltypes = bcc_vanilla_embed.obs.groupby(\"ClustersNamed\").agg('median').sort_values('gp_pbmc_pred').index[::-1]\n",

    "sns.boxplot(\n",

    "    x=\"ClustersNamed\", y=\"gp_pbmc_pred\", data=bcc_vanilla_embed.obs, \n",

    "    order=gp_pbmc_ordered_celltypes, ax=ax\n",

    ")\n",

    "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",

    "ax.set(\n",

    "    title='Confidence on BCC celltypes',\n",

    "    xlabel=\"BCC Celltype\",\n",

    "    ylabel=\"GP-estimated confidence\"\n",

    ")\n",

    "ax.axhline(0.5, color='grey', linestyle='--')\n",

    "fig.show()"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "In the above plot, the x-axis corresponds to the cell types present in the BCC dataset. Each box-plot shows, for each celltype, the distribution of predicted likelihood of being \"in-distribution\" and therefore \"confident.\" We see that the four tumor celltypes have the lowest predicted confidence, as do endothelial cells and myeloid cells. These are consistent with what we'd expect biologically. We additionally see that B cells are relatively low-confidence; this may suggest that B cells in this BCC tissue sample are forming larger complexes (Kinker et al., https://www.frontiersin.org/articles/10.3389/fcell.2021.678127/full), thus adopting cell signatures unlike those seen in training B-cell examples.\n",

    "\n",

    "Overall, this plot shows that training a Gaussian Process classifier on BABEL's embedding can provide a good estimate of uncertainty when attempting to generalize BABEL to new data."

   ]

  }

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