{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering\n",
    "![dandelion_logo](img/dandelion_logo_illustration.png)\n",
    "\n",
    "We now move on to filtering out BCR contigs (and corresponding cells if necessary) from the BCR data and transcriptome object loaded in *scanpy*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import *dandelion* module**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(os.path.expanduser('/Users/kt16/Documents/Github/dandelion'))\n",
    "import dandelion as ddl\n",
    "# change directory to somewhere more workable\n",
    "os.chdir(os.path.expanduser('/Users/kt16/Downloads/dandelion_tutorial/'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import modules for use with scanpy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.6.0 anndata==0.7.4 umap==0.4.6 numpy==1.19.4 scipy==1.5.3 pandas==1.1.4 scikit-learn==0.23.2 statsmodels==0.12.1 python-igraph==0.8.3 leidenalg==0.8.3\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scanpy as sc\n",
    "import warnings\n",
    "import functools\n",
    "import seaborn as sns\n",
    "import scipy.stats\n",
    "import anndata\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "sc.logging.print_header()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import the transcriptome data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 30471 × 31915\n",
       "    obs: 'sampleid', 'batch'\n",
       "    var: 'feature_types', 'genome', 'gene_ids-0', 'gene_ids-1', 'gene_ids-2', 'gene_ids-3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = ['sc5p_v2_hs_PBMC_1k', 'sc5p_v2_hs_PBMC_10k', 'vdj_v1_hs_pbmc3', 'vdj_nextgem_hs_pbmc3']\n",
    "adata_list = []\n",
    "for sample in samples:\n",
    "    adata = sc.read_10x_h5(sample +'/' + sample + '_filtered_feature_bc_matrix.h5', gex_only=True)\n",
    "    adata.obs['sampleid'] = sample\n",
    "    # rename cells to sample id + barcode\n",
    "    adata.obs_names = [str(sample)+'_'+str(j) for j in adata.obs_names]\n",
    "    adata.var_names_make_unique()\n",
    "    adata_list.append(adata)\n",
    "adata = adata_list[0].concatenate(adata_list[1:])\n",
    "# rename the obs_names again, this time cleaving the trailing -#\n",
    "adata.obs_names = [str(j).split('-')[0] for j in adata.obs_names]\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm using a wrapper called `pp.recipe_scanpy_qc` to run through a generic [scanpy](https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html) workflow. You can skip this if you already have a pre-processed `AnnData` object for the subsequent steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 30471 × 31915\n",
       "    obs: 'sampleid', 'batch', 'scrublet_score', 'n_genes', 'percent_mito', 'n_counts', 'is_doublet', 'filter_rna'\n",
       "    var: 'feature_types', 'genome', 'gene_ids-0', 'gene_ids-1', 'gene_ids-2', 'gene_ids-3'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddl.pp.recipe_scanpy_qc(adata)\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To proceed, the `.obs` need to contain a `filter_rna` column.\n",
    "\n",
    "If you have a pre-processed/filltered `AnnData` object that is ready, just add a `'filter_rna'` column into the `.obs` slot with every value set to `False` and you should be good to go:\n",
    "\n",
    "```python\n",
    "adata.obs['filter_rna'] = False\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adata.obs['filter_rna'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filter cells that are potental doublets and poor quality in both the BCR data and transcriptome data**\n",
    "\n",
    "We use the function `pp.filter_bcr` to mark and filter out cells and contigs from both the BCR data and transcriptome data in `AnnData`. The operation will remove bad quality cells based on transcriptome information as well as remove BCR doublets (multiplet heavy chain, and/or light chains) from the BCR data. In some situations, a single cell can have multiple heavy/light chain contigs although they have an identical V(D)J+C alignment; in situations like this, the contigs with lesser umis will be dropped and the umis transferred to duplicate_count column. The same procedure is applied to both heavy chain and light chains before identifying doublets. \n",
    "\n",
    "Cells in the gene expression object without BCR information will not be affected i.e. the `AnnData` object can hold non-B cells. Run `?ddl.pp.filter_bcr` to check what each option does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sequence_id</th>\n",
       "      <th>sequence</th>\n",
       "      <th>rev_comp</th>\n",
       "      <th>productive</th>\n",
       "      <th>v_call</th>\n",
       "      <th>d_call</th>\n",
       "      <th>j_call</th>\n",
       "      <th>sequence_alignment</th>\n",
       "      <th>germline_alignment</th>\n",
       "      <th>junction</th>\n",
       "      <th>...</th>\n",
       "      <th>fwr3_aa</th>\n",
       "      <th>fwr4_aa</th>\n",
       "      <th>cdr1_aa</th>\n",
       "      <th>cdr2_aa</th>\n",
       "      <th>cdr3_aa</th>\n",
       "      <th>sequence_alignment_aa</th>\n",
       "      <th>v_sequence_alignment_aa</th>\n",
       "      <th>d_sequence_alignment_aa</th>\n",
       "      <th>j_sequence_alignment_aa</th>\n",
       "      <th>mu_freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sc5p_v2_hs_PBMC_1k_AACTCCCAGGCTAGGT_contig_2</td>\n",
       "      <td>ATACTTTCTGAGAGTCCTGGACCTCCTGTGCAAGAACATGAAACAT...</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>IGHV4-61*02</td>\n",
       "      <td>IGHD3-3*01</td>\n",
       "      <td>IGHJ6*02</td>\n",
       "      <td>CAGGTGCAGCTGCAGGAGTCGGGCCCA...GGACTGGTGAAGCCTT...</td>\n",
       "      <td>CAGGTGCAGCTGCAGGAGTCGGGCCCA...GGACTGGTGAAGCCTT...</td>\n",
       "      <td>TGTGCGAGAGAAAATTACGATTTTTGGAGTGGTTATTACCACGGTG...</td>\n",
       "      <td>...</td>\n",
       "      <td>NYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYC</td>\n",
       "      <td>WGQGTTVTVSS</td>\n",
       "      <td>GGSISSGSYY</td>\n",
       "      <td>IYTSGST</td>\n",
       "      <td>ARENYDFWSGYYHGADV</td>\n",
       "      <td>QVQLQESGPGLVKPSQTLSLTCTVSGGSISSGSYYWSWIRQPAGKG...</td>\n",
       "      <td>QVQLQESGPGLVKPSQTLSLTCTVSGGSISSGSYYWSWIRQPAGKG...</td>\n",
       "      <td>YDFWSGY</td>\n",
       "      <td>YHGADVWGQGTTVTVSS</td>\n",
       "      <td>0.008571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sc5p_v2_hs_PBMC_1k_AACTCCCAGGCTAGGT_contig_3</td>\n",
       "      <td>GGCTGGGGTCTCAGGAGGCAGCGCTCTGGGGACGTCTCCACCATGG...</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>IGLV2-5*01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IGLJ3*02</td>\n",
       "      <td>CAGTCTGCCCTGATTCAGCCTCCCTCC...GTGTCCGGGTCTCCTG...</td>\n",
       "      <td>CAGTCTGCCCTGATTCAGCCTCCCTCC...GTGTCCGGGTCTCCTG...</td>\n",
       "      <td>TGCTGCTCATATACAAGCAGTGCCACTTTCTTGGGTGTTC</td>\n",
       "      <td>...</td>\n",
       "      <td>TQPSGVPDRFSGSKSGNTASMTISGLQAEDEADY*C</td>\n",
       "      <td>RRRDQADRP</td>\n",
       "      <td>SSDVGSYDY</td>\n",
       "      <td>NVN</td>\n",
       "      <td>CSYTSSATFLG</td>\n",
       "      <td>QSALIQPPSVSGSPGQSVTISCTGTSSDVGSYDYVSWYQQHPGTVP...</td>\n",
       "      <td>QSALIQPPSVSGSPGQSVTISCTGTSSDVGSYDYVSWYQQHPGTVP...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LGVRRRDQADRP</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sc5p_v2_hs_PBMC_1k_AACTCCCAGGCTAGGT_contig_1</td>\n",
       "      <td>ACTGCGGGGGTAAGAGGTTGTGTCCACCATGGCCTGGACTCCTCTC...</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>IGLV5-45*03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IGLJ3*02</td>\n",
       "      <td>CAGGCTGTGCTGACTCAGCCGTCTTCC...CTCTCTGCATCTCCTG...</td>\n",
       "      <td>CAGGCTGTGCTGACTCAGCCGTCTTCC...CTCTCTGCATCTCCTG...</td>\n",
       "      <td>TGTATGATTTGGCACAGCAGCGCTTGGGTGTTC</td>\n",
       "      <td>...</td>\n",
       "      <td>QQGSGVPSRFSGSKDASANAGILLISGLQSEDEADYYC</td>\n",
       "      <td>FGGGTKLTVL</td>\n",
       "      <td>SGINVGTYR</td>\n",
       "      <td>YKSDSDK</td>\n",
       "      <td>MIWHSSAWV</td>\n",
       "      <td>QAVLTQPSSLSASPGASASLTCTLRSGINVGTYRIYWYQQKPGSPP...</td>\n",
       "      <td>QAVLTQPSSLSASPGASASLTCTLRSGINVGTYRIYWYQQKPGSPP...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VFGGGTKLTVL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sc5p_v2_hs_PBMC_1k_AACTCTTGTCATCGGC_contig_3</td>\n",
       "      <td>AGCAGAGCTCTGGGGAGTCTGCACCATGGCTTGGACCCCACTCCTC...</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>IGLV4-69*01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IGLJ3*02</td>\n",
       "      <td>CAGCTTGTGCTGACTCAATCGCCCTCT...GCCTCTGCCTCCCTGG...</td>\n",
       "      <td>CAGCTTGTGCTGACTCAATCGCCCTCT...GCCTCTGCCTCCCTGG...</td>\n",
       "      <td>TGTCAGACCTGGGGCACTGGCATTCTTGGGTGTTC</td>\n",
       "      <td>...</td>\n",
       "      <td>SKGDGIPDRFSGSSSGAERYLTISSLQSEDEADYYC</td>\n",
       "      <td>SAEGPS*PS</td>\n",
       "      <td>SGHSSYA</td>\n",
       "      <td>LNSDGSH</td>\n",
       "      <td>QTWGTGILG</td>\n",
       "      <td>QLVLTQSPSASASLGASVKLTCTLSSGHSSYAIAWHQQQPEKGPRY...</td>\n",
       "      <td>QLVLTQSPSASASLGASVKLTCTLSSGHSSYAIAWHQQQPEKGPRY...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GCSAEGPS*PS*</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sc5p_v2_hs_PBMC_1k_AACTCTTGTCATCGGC_contig_1</td>\n",
       "      <td>AGAGCTCTGGGGAGTCTGCACCATGGCTTGGACCCCACTCCTCTTC...</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>IGLV4-69*01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IGLJ1*01</td>\n",
       "      <td>CAGCTTGTGCTGACTCAATCGCCCTCT...GCCTCTGCCTCCCTGG...</td>\n",
       "      <td>CAGCTTGTGCTGACTCAATCGCCCTCT...GCCTCTGCCTCCCTGG...</td>\n",
       "      <td>TGTCAGACCTGGGGCACTGGCATTTATGTCTTC</td>\n",
       "      <td>...</td>\n",
       "      <td>SKGDGIPDRFSGSSSGAERYLTISSLQSEDEADYYC</td>\n",
       "      <td>FGTGTKVTVL</td>\n",
       "      <td>SGHSSYA</td>\n",
       "      <td>LNSDGSH</td>\n",
       "      <td>QTWGTGIYV</td>\n",
       "      <td>QLVLTQSPSASASLGASVKLTCTLSSGHSSYAIAWHQQQPEKGPRY...</td>\n",
       "      <td>QLVLTQSPSASASLGASVKLTCTLSSGHSSYAIAWHQQQPEKGPRY...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YVFGTGTKVTVL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7507</th>\n",
       "      <td>vdj_nextgem_hs_pbmc3_TTTGCGCTCTGTCAAG_contig_2</td>\n",
       "      <td>ATCACATAACAACCACATTCCTCCTCTAAAGAAGCCCCCGGGAGCC...</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>IGHV1-69*01,IGHV1-69D*01</td>\n",
       "      <td>IGHD3-22*01</td>\n",
       "      <td>IGHJ4*02</td>\n",
       "      <td>CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAAGTGAAGAAGCCTG...</td>\n",
       "      <td>CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAGGTGAAGAAGCCTG...</td>\n",
       "      <td>TGTGCGAGGGGGAAGTATTACTATGATAAAAGTGGGTCTCCACCTC...</td>\n",
       "      <td>...</td>\n",
       "      <td>NYAQKFQGRVSITADESTTTAYMELSSLRSEDSAVYYC</td>\n",
       "      <td>WGQGTLVTVSS</td>\n",
       "      <td>GGIFSSYA</td>\n",
       "      <td>IIPIFGAT</td>\n",
       "      <td>ARGKYYYDKSGSPPPIYSFDY</td>\n",
       "      <td>QVQLVQSGAEVKKPGSSVKVSCKVSGGIFSSYAISWVRQAPGQGLE...</td>\n",
       "      <td>QVQLVQSGAEVKKPGSSVKVSCKVSGGIFSSYAISWVRQAPGQGLE...</td>\n",
       "      <td>YYYDKSG</td>\n",
       "      <td>FDYWGQGTLVTVSS</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7508</th>\n",
       "      <td>vdj_nextgem_hs_pbmc3_TTTGGTTGTAAGGATT_contig_1</td>\n",
       "      <td>AGAGCTCTGGAGAAGAGCTGCTCAGTTAGGACCCAGAGGGAACCAT...</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>IGKV3-20*01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IGKJ2*01,IGKJ2*02</td>\n",
       "      <td>GAAATTGTGTTGACGCAGTCTCCAGGCACCCTGTCTTTGTCTCCAG...</td>\n",
       "      <td>GAAATTGTGTTGACGCAGTCTCCAGGCACCCTGTCTTTGTCTCCAG...</td>\n",
       "      <td>TGTCAGCAGTATGATGAGTCACCTCTGACTTTT</td>\n",
       "      <td>...</td>\n",
       "      <td>SRATGIPDRFSGSGSGTDFTLTISRLVPEDFAVYYC</td>\n",
       "      <td>FGQGTKLEIK</td>\n",
       "      <td>QSLTNSQ</td>\n",
       "      <td>GAS</td>\n",
       "      <td>QQYDESPLT</td>\n",
       "      <td>EIVLTQSPGTLSLSPGERATLSCRASQSLTNSQLAWYQQKPGQAPR...</td>\n",
       "      <td>EIVLTQSPGTLSLSPGERATLSCRASQSLTNSQLAWYQQKPGQAPR...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TFGQGTKLEIK</td>\n",
       "      <td>0.034161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7509</th>\n",
       "      <td>vdj_nextgem_hs_pbmc3_TTTGGTTGTAAGGATT_contig_2</td>\n",
       "      <td>AGCTCTGGGAGAGGAGCCCCAGCCCTGAGATTCCCAGGTGTTTCCA...</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>IGHV3-9*01</td>\n",
       "      <td>IGHD5-18*01,IGHD5-5*01</td>\n",
       "      <td>IGHJ6*03</td>\n",
       "      <td>GAAGTGCAGCTGGTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...</td>\n",
       "      <td>GAAGTGCAGCTGGTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...</td>\n",
       "      <td>TGTGCAAAAGACGGATACAGCTATCGTTCGTCATACTACTTTTACA...</td>\n",
       "      <td>...</td>\n",
       "      <td>GYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTALYYC</td>\n",
       "      <td>WGKGTTVTVSS</td>\n",
       "      <td>GFSFDDYV</td>\n",
       "      <td>ISWNSGRT</td>\n",
       "      <td>AKDGYSYRSSYYFYMDV</td>\n",
       "      <td>EVQLVESGGGLVQPGRSLRLSCAASGFSFDDYVMHWVRQAPGKGLE...</td>\n",
       "      <td>EVQLVESGGGLVQPGRSLRLSCAASGFSFDDYVMHWVRQAPGKGLE...</td>\n",
       "      <td>GYSYR</td>\n",
       "      <td>YYFYMDVWGKGTTVTVSS</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7510</th>\n",
       "      <td>vdj_nextgem_hs_pbmc3_TTTGTCACAGTAGAGC_contig_1</td>\n",
       "      <td>AGCTCTGAGAGAGGAGCCCAGCCCTGGGATTTTCAGGTGTTTTCAT...</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>IGHV3-23*01,IGHV3-23D*01</td>\n",
       "      <td>IGHD4-17*01</td>\n",
       "      <td>IGHJ4*02</td>\n",
       "      <td>GAGGTGCAGCTGTTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...</td>\n",
       "      <td>GAGGTGCAGCTGTTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...</td>\n",
       "      <td>TGTGCGAAAGATTTTAGGTCGCCATACGGTGACTACTACTTTGACT...</td>\n",
       "      <td>...</td>\n",
       "      <td>YYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYC</td>\n",
       "      <td>WGQGTLVTVSS</td>\n",
       "      <td>GFTFSSYA</td>\n",
       "      <td>ISGSGGST</td>\n",
       "      <td>AKDFRSPYGDYYFDY</td>\n",
       "      <td>EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLE...</td>\n",
       "      <td>EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLE...</td>\n",
       "      <td>YGD</td>\n",
       "      <td>YFDYWGQGTLVTVSS</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7511</th>\n",
       "      <td>vdj_nextgem_hs_pbmc3_TTTGTCACAGTAGAGC_contig_2</td>\n",
       "      <td>GTGGGTCCAGGAGGCAGAACTCTGGGTGTCTCACCATGGCCTGGAT...</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>IGLV3-25*03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IGLJ1*01</td>\n",
       "      <td>TCCTATGAGCTGACACAGCCACCCTCG...GTGTCAGTGTCCCCAG...</td>\n",
       "      <td>TCCTATGAGCTGACACAGCCACCCTCG...GTGTCAGTGTCCCCAG...</td>\n",
       "      <td>TGTCAATCAGCAGACAGCAGTGGTACTTATCTTTATGTCTTC</td>\n",
       "      <td>...</td>\n",
       "      <td>ERPSGIPERFSGSSSGTTVTLTISGVQAEDEADYYC</td>\n",
       "      <td>FGTGTKVTVL</td>\n",
       "      <td>ALPKQY</td>\n",
       "      <td>KDS</td>\n",
       "      <td>QSADSSGTYLYV</td>\n",
       "      <td>SYELTQPPSVSVSPGQTARITCSGDALPKQYAYWYQQKPGQAPVLV...</td>\n",
       "      <td>SYELTQPPSVSVSPGQTARITCSGDALPKQYAYWYQQKPGQAPVLV...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YVFGTGTKVTVL</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7512 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         sequence_id  \\\n",
       "0       sc5p_v2_hs_PBMC_1k_AACTCCCAGGCTAGGT_contig_2   \n",
       "1       sc5p_v2_hs_PBMC_1k_AACTCCCAGGCTAGGT_contig_3   \n",
       "2       sc5p_v2_hs_PBMC_1k_AACTCCCAGGCTAGGT_contig_1   \n",
       "3       sc5p_v2_hs_PBMC_1k_AACTCTTGTCATCGGC_contig_3   \n",
       "4       sc5p_v2_hs_PBMC_1k_AACTCTTGTCATCGGC_contig_1   \n",
       "...                                              ...   \n",
       "7507  vdj_nextgem_hs_pbmc3_TTTGCGCTCTGTCAAG_contig_2   \n",
       "7508  vdj_nextgem_hs_pbmc3_TTTGGTTGTAAGGATT_contig_1   \n",
       "7509  vdj_nextgem_hs_pbmc3_TTTGGTTGTAAGGATT_contig_2   \n",
       "7510  vdj_nextgem_hs_pbmc3_TTTGTCACAGTAGAGC_contig_1   \n",
       "7511  vdj_nextgem_hs_pbmc3_TTTGTCACAGTAGAGC_contig_2   \n",
       "\n",
       "                                               sequence rev_comp productive  \\\n",
       "0     ATACTTTCTGAGAGTCCTGGACCTCCTGTGCAAGAACATGAAACAT...        F          T   \n",
       "1     GGCTGGGGTCTCAGGAGGCAGCGCTCTGGGGACGTCTCCACCATGG...        F          F   \n",
       "2     ACTGCGGGGGTAAGAGGTTGTGTCCACCATGGCCTGGACTCCTCTC...        F          T   \n",
       "3     AGCAGAGCTCTGGGGAGTCTGCACCATGGCTTGGACCCCACTCCTC...        F          F   \n",
       "4     AGAGCTCTGGGGAGTCTGCACCATGGCTTGGACCCCACTCCTCTTC...        F          T   \n",
       "...                                                 ...      ...        ...   \n",
       "7507  ATCACATAACAACCACATTCCTCCTCTAAAGAAGCCCCCGGGAGCC...        F          T   \n",
       "7508  AGAGCTCTGGAGAAGAGCTGCTCAGTTAGGACCCAGAGGGAACCAT...        F          T   \n",
       "7509  AGCTCTGGGAGAGGAGCCCCAGCCCTGAGATTCCCAGGTGTTTCCA...        F          T   \n",
       "7510  AGCTCTGAGAGAGGAGCCCAGCCCTGGGATTTTCAGGTGTTTTCAT...        F          T   \n",
       "7511  GTGGGTCCAGGAGGCAGAACTCTGGGTGTCTCACCATGGCCTGGAT...        F          T   \n",
       "\n",
       "                        v_call                  d_call             j_call  \\\n",
       "0                  IGHV4-61*02              IGHD3-3*01           IGHJ6*02   \n",
       "1                   IGLV2-5*01                     NaN           IGLJ3*02   \n",
       "2                  IGLV5-45*03                     NaN           IGLJ3*02   \n",
       "3                  IGLV4-69*01                     NaN           IGLJ3*02   \n",
       "4                  IGLV4-69*01                     NaN           IGLJ1*01   \n",
       "...                        ...                     ...                ...   \n",
       "7507  IGHV1-69*01,IGHV1-69D*01             IGHD3-22*01           IGHJ4*02   \n",
       "7508               IGKV3-20*01                     NaN  IGKJ2*01,IGKJ2*02   \n",
       "7509                IGHV3-9*01  IGHD5-18*01,IGHD5-5*01           IGHJ6*03   \n",
       "7510  IGHV3-23*01,IGHV3-23D*01             IGHD4-17*01           IGHJ4*02   \n",
       "7511               IGLV3-25*03                     NaN           IGLJ1*01   \n",
       "\n",
       "                                     sequence_alignment  \\\n",
       "0     CAGGTGCAGCTGCAGGAGTCGGGCCCA...GGACTGGTGAAGCCTT...   \n",
       "1     CAGTCTGCCCTGATTCAGCCTCCCTCC...GTGTCCGGGTCTCCTG...   \n",
       "2     CAGGCTGTGCTGACTCAGCCGTCTTCC...CTCTCTGCATCTCCTG...   \n",
       "3     CAGCTTGTGCTGACTCAATCGCCCTCT...GCCTCTGCCTCCCTGG...   \n",
       "4     CAGCTTGTGCTGACTCAATCGCCCTCT...GCCTCTGCCTCCCTGG...   \n",
       "...                                                 ...   \n",
       "7507  CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAAGTGAAGAAGCCTG...   \n",
       "7508  GAAATTGTGTTGACGCAGTCTCCAGGCACCCTGTCTTTGTCTCCAG...   \n",
       "7509  GAAGTGCAGCTGGTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...   \n",
       "7510  GAGGTGCAGCTGTTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...   \n",
       "7511  TCCTATGAGCTGACACAGCCACCCTCG...GTGTCAGTGTCCCCAG...   \n",
       "\n",
       "                                     germline_alignment  \\\n",
       "0     CAGGTGCAGCTGCAGGAGTCGGGCCCA...GGACTGGTGAAGCCTT...   \n",
       "1     CAGTCTGCCCTGATTCAGCCTCCCTCC...GTGTCCGGGTCTCCTG...   \n",
       "2     CAGGCTGTGCTGACTCAGCCGTCTTCC...CTCTCTGCATCTCCTG...   \n",
       "3     CAGCTTGTGCTGACTCAATCGCCCTCT...GCCTCTGCCTCCCTGG...   \n",
       "4     CAGCTTGTGCTGACTCAATCGCCCTCT...GCCTCTGCCTCCCTGG...   \n",
       "...                                                 ...   \n",
       "7507  CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAGGTGAAGAAGCCTG...   \n",
       "7508  GAAATTGTGTTGACGCAGTCTCCAGGCACCCTGTCTTTGTCTCCAG...   \n",
       "7509  GAAGTGCAGCTGGTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...   \n",
       "7510  GAGGTGCAGCTGTTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...   \n",
       "7511  TCCTATGAGCTGACACAGCCACCCTCG...GTGTCAGTGTCCCCAG...   \n",
       "\n",
       "                                               junction  ...  \\\n",
       "0     TGTGCGAGAGAAAATTACGATTTTTGGAGTGGTTATTACCACGGTG...  ...   \n",
       "1              TGCTGCTCATATACAAGCAGTGCCACTTTCTTGGGTGTTC  ...   \n",
       "2                     TGTATGATTTGGCACAGCAGCGCTTGGGTGTTC  ...   \n",
       "3                   TGTCAGACCTGGGGCACTGGCATTCTTGGGTGTTC  ...   \n",
       "4                     TGTCAGACCTGGGGCACTGGCATTTATGTCTTC  ...   \n",
       "...                                                 ...  ...   \n",
       "7507  TGTGCGAGGGGGAAGTATTACTATGATAAAAGTGGGTCTCCACCTC...  ...   \n",
       "7508                  TGTCAGCAGTATGATGAGTCACCTCTGACTTTT  ...   \n",
       "7509  TGTGCAAAAGACGGATACAGCTATCGTTCGTCATACTACTTTTACA...  ...   \n",
       "7510  TGTGCGAAAGATTTTAGGTCGCCATACGGTGACTACTACTTTGACT...  ...   \n",
       "7511         TGTCAATCAGCAGACAGCAGTGGTACTTATCTTTATGTCTTC  ...   \n",
       "\n",
       "                                     fwr3_aa      fwr4_aa     cdr1_aa  \\\n",
       "0     NYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYC  WGQGTTVTVSS  GGSISSGSYY   \n",
       "1       TQPSGVPDRFSGSKSGNTASMTISGLQAEDEADY*C    RRRDQADRP   SSDVGSYDY   \n",
       "2     QQGSGVPSRFSGSKDASANAGILLISGLQSEDEADYYC   FGGGTKLTVL   SGINVGTYR   \n",
       "3       SKGDGIPDRFSGSSSGAERYLTISSLQSEDEADYYC    SAEGPS*PS     SGHSSYA   \n",
       "4       SKGDGIPDRFSGSSSGAERYLTISSLQSEDEADYYC   FGTGTKVTVL     SGHSSYA   \n",
       "...                                      ...          ...         ...   \n",
       "7507  NYAQKFQGRVSITADESTTTAYMELSSLRSEDSAVYYC  WGQGTLVTVSS    GGIFSSYA   \n",
       "7508    SRATGIPDRFSGSGSGTDFTLTISRLVPEDFAVYYC   FGQGTKLEIK     QSLTNSQ   \n",
       "7509  GYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTALYYC  WGKGTTVTVSS    GFSFDDYV   \n",
       "7510  YYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYC  WGQGTLVTVSS    GFTFSSYA   \n",
       "7511    ERPSGIPERFSGSSSGTTVTLTISGVQAEDEADYYC   FGTGTKVTVL      ALPKQY   \n",
       "\n",
       "       cdr2_aa                cdr3_aa  \\\n",
       "0      IYTSGST      ARENYDFWSGYYHGADV   \n",
       "1          NVN            CSYTSSATFLG   \n",
       "2      YKSDSDK              MIWHSSAWV   \n",
       "3      LNSDGSH              QTWGTGILG   \n",
       "4      LNSDGSH              QTWGTGIYV   \n",
       "...        ...                    ...   \n",
       "7507  IIPIFGAT  ARGKYYYDKSGSPPPIYSFDY   \n",
       "7508       GAS              QQYDESPLT   \n",
       "7509  ISWNSGRT      AKDGYSYRSSYYFYMDV   \n",
       "7510  ISGSGGST        AKDFRSPYGDYYFDY   \n",
       "7511       KDS           QSADSSGTYLYV   \n",
       "\n",
       "                                  sequence_alignment_aa  \\\n",
       "0     QVQLQESGPGLVKPSQTLSLTCTVSGGSISSGSYYWSWIRQPAGKG...   \n",
       "1     QSALIQPPSVSGSPGQSVTISCTGTSSDVGSYDYVSWYQQHPGTVP...   \n",
       "2     QAVLTQPSSLSASPGASASLTCTLRSGINVGTYRIYWYQQKPGSPP...   \n",
       "3     QLVLTQSPSASASLGASVKLTCTLSSGHSSYAIAWHQQQPEKGPRY...   \n",
       "4     QLVLTQSPSASASLGASVKLTCTLSSGHSSYAIAWHQQQPEKGPRY...   \n",
       "...                                                 ...   \n",
       "7507  QVQLVQSGAEVKKPGSSVKVSCKVSGGIFSSYAISWVRQAPGQGLE...   \n",
       "7508  EIVLTQSPGTLSLSPGERATLSCRASQSLTNSQLAWYQQKPGQAPR...   \n",
       "7509  EVQLVESGGGLVQPGRSLRLSCAASGFSFDDYVMHWVRQAPGKGLE...   \n",
       "7510  EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLE...   \n",
       "7511  SYELTQPPSVSVSPGQTARITCSGDALPKQYAYWYQQKPGQAPVLV...   \n",
       "\n",
       "                                v_sequence_alignment_aa  \\\n",
       "0     QVQLQESGPGLVKPSQTLSLTCTVSGGSISSGSYYWSWIRQPAGKG...   \n",
       "1     QSALIQPPSVSGSPGQSVTISCTGTSSDVGSYDYVSWYQQHPGTVP...   \n",
       "2     QAVLTQPSSLSASPGASASLTCTLRSGINVGTYRIYWYQQKPGSPP...   \n",
       "3     QLVLTQSPSASASLGASVKLTCTLSSGHSSYAIAWHQQQPEKGPRY...   \n",
       "4     QLVLTQSPSASASLGASVKLTCTLSSGHSSYAIAWHQQQPEKGPRY...   \n",
       "...                                                 ...   \n",
       "7507  QVQLVQSGAEVKKPGSSVKVSCKVSGGIFSSYAISWVRQAPGQGLE...   \n",
       "7508  EIVLTQSPGTLSLSPGERATLSCRASQSLTNSQLAWYQQKPGQAPR...   \n",
       "7509  EVQLVESGGGLVQPGRSLRLSCAASGFSFDDYVMHWVRQAPGKGLE...   \n",
       "7510  EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLE...   \n",
       "7511  SYELTQPPSVSVSPGQTARITCSGDALPKQYAYWYQQKPGQAPVLV...   \n",
       "\n",
       "      d_sequence_alignment_aa  j_sequence_alignment_aa   mu_freq  \n",
       "0                     YDFWSGY        YHGADVWGQGTTVTVSS  0.008571  \n",
       "1                         NaN             LGVRRRDQADRP  0.000000  \n",
       "2                         NaN              VFGGGTKLTVL  0.000000  \n",
       "3                         NaN             GCSAEGPS*PS*  0.000000  \n",
       "4                         NaN             YVFGTGTKVTVL  0.000000  \n",
       "...                       ...                      ...       ...  \n",
       "7507                  YYYDKSG           FDYWGQGTLVTVSS  0.047619  \n",
       "7508                      NaN              TFGQGTKLEIK  0.034161  \n",
       "7509                    GYSYR       YYFYMDVWGKGTTVTVSS  0.028571  \n",
       "7510                      YGD          YFDYWGQGTLVTVSS  0.000000  \n",
       "7511                      NaN             YVFGTGTKVTVL  0.000000  \n",
       "\n",
       "[7512 rows x 81 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first we read in the 4 bcr files\n",
    "bcr_files = []\n",
    "for sample in samples:\n",
    "    file_location = sample +'/dandelion/data/'+sample+'_b_filtered_contig_igblast_db-pass_genotyped.tsv'\n",
    "    bcr_files.append(pd.read_csv(file_location, sep = '\\t'))\n",
    "bcr = bcr_files[0].append(bcr_files[1:])\n",
    "bcr.reset_index(inplace = True, drop = True)\n",
    "bcr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scanning for poor quality/ambiguous contigs with 3 cpus\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Annotating in anndata obs slot : 100%|██████████| 30471/30471 [00:00<00:00, 79083.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finishing up filtering\n",
      "Initializing Dandelion object\n"
     ]
    }
   ],
   "source": [
    "# The function will return both objects. \n",
    "vdj, adata = ddl.pp.filter_bcr(bcr, adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default mode is to filter any remaining 'doublet' light chains, but some may be interested in keeping them. The option to change the behaviour is by toggling:\n",
    "```python\n",
    "filter_lightchains=False\n",
    "```\n",
    "\n",
    "Another default behavour is that if the cell in the BCR table cannot be found in the transcriptomic data, it will also be removed from the BCR data. This can be changed by toggling:\n",
    "```python\n",
    "filter_missing=False\n",
    "```\n",
    "\n",
    "Also, when contigs are marked as poor quality, the default behaviour is to remove the contigs associated with the barcode, and not the barcode from the transcriptome data. This can be toggled to remove the entire cell if the intention is to retain a conservative dataset for both BCR and transcriptome data:\n",
    "```python\n",
    "filter_poorqualitybcr=True\n",
    "```\n",
    "\n",
    "And lastly, the default behaviour is to rescue the heavy chain contig with the highest umi if there are multiple contigs for a single cell. The function requires a minimum fold-difference of 2 between the highest and lowest umi in order to rescue the contig. However, if the contigs have similar number of umis, or if the sum of the umis are very low, then the entire cell will be filtered. The fold-difference cut-off can be specified via the option `umi_foldchange_cutoff`. This can be toggled to be ignored i.e. drop all multiple IGH contigs:\n",
    "```python\n",
    "rescue_igh = False\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the output V(D)J table**\n",
    "\n",
    "The vdj table is returned as a `Dandelion` class object in the `.data` slot (described in further detail in the next notebook); if a file was provided for `filter_bcr` above, a new file will be created in the same folder with the `filtered` prefix. Note that this `vdj` table is indexed based on contigs (sequence_id)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dandelion class object with n_obs = 838 and n_contigs = 1700\n",
       "    data: 'sequence_id', 'sequence', 'rev_comp', 'productive', 'v_call', 'd_call', 'j_call', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'v_cigar', 'd_cigar', 'j_cigar', 'stop_codon', 'vj_in_frame', 'locus', 'junction_length', 'np1_length', 'np2_length', 'v_sequence_start', 'v_sequence_end', 'v_germline_start', 'v_germline_end', 'd_sequence_start', 'd_sequence_end', 'd_germline_start', 'd_germline_end', 'j_sequence_start', 'j_sequence_end', 'j_germline_start', 'j_germline_end', 'v_score', 'v_identity', 'v_support', 'd_score', 'd_identity', 'd_support', 'j_score', 'j_identity', 'j_support', 'fwr1', 'fwr2', 'fwr3', 'fwr4', 'cdr1', 'cdr2', 'cdr3', 'cell_id', 'c_call', 'consensus_count', 'umi_count', 'v_call_10x', 'd_call_10x', 'j_call_10x', 'junction_10x', 'junction_10x_aa', 'v_call_genotyped', 'germline_alignment_d_mask', 'sample_id', 'c_sequence_alignment', 'c_germline_alignment', 'c_sequence_start', 'c_sequence_end', 'c_score', 'c_identity', 'c_support', 'c_call_10x', 'junction_aa_length', 'fwr1_aa', 'fwr2_aa', 'fwr3_aa', 'fwr4_aa', 'cdr1_aa', 'cdr2_aa', 'cdr3_aa', 'sequence_alignment_aa', 'v_sequence_alignment_aa', 'd_sequence_alignment_aa', 'j_sequence_alignment_aa', 'mu_freq', 'duplicate_count'\n",
       "    metadata: 'sample_id', 'isotype', 'lightchain', 'status', 'vdj_status', 'productive', 'umi_counts_heavy', 'umi_counts_light', 'v_call_heavy', 'v_call_light', 'j_call_heavy', 'j_call_light', 'c_call_heavy', 'c_call_light'\n",
       "    distance: None\n",
       "    edges: None\n",
       "    layout: None\n",
       "    graph: None"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check the AnnData object as well**\n",
    "\n",
    "And the `AnnData` object is indexed based on cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 16492 × 31915\n",
       "    obs: 'sampleid', 'batch', 'scrublet_score', 'n_genes', 'percent_mito', 'n_counts', 'is_doublet', 'filter_rna', 'has_bcr', 'filter_bcr_quality', 'filter_bcr_heavy', 'filter_bcr_light', 'bcr_QC_pass', 'filter_bcr'\n",
       "    var: 'feature_types', 'genome', 'gene_ids-0', 'gene_ids-1', 'gene_ids-2', 'gene_ids-3'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.obs` slot in the `AnnData` object now contains a few new columns related to BCR:\n",
    "\n",
    "1) **has_bcr :** `True/False` statement marking cells with a matching BCR (pre-contig filtering).\n",
    "\n",
    "2) **filter_bcr_quality :** `True/False` recommendation for filtering cells identified as having poor quality contigs if `filter_poorqualitybcr=True` (pre-contig filtering).\n",
    "\n",
    "3) **filter_bcr_heavy :** `True/False` recommendation for filtering cells identified as heavy chain 'doublets' (after rescue if `rescue_igh=True`) (pre-contig filtering).\n",
    "\n",
    "4) **filter_bcr_light :** `True/False` recommendation for filtering cells identifed as having multiple light chain contigs (pre-contig filtering).\n",
    "\n",
    "***Most importantly:***\n",
    "\n",
    "5) **bcr_QC_pass :** `True/False` statement marking cells where BCR contigs were removed from #1 due to contigs failing QC. (post-contig filtering)\n",
    "\n",
    "6) **filter_bcr :** `True/False` recommendation for filter for cells flagged in 2-4 (post-contig filtering).\n",
    "\n",
    "So this means that to go forward, you want to only select cells that have BCR that passed QC (`has_bcr == True` and `bcr_QC_pass == True`) with filtering recommendation to be false (`filter_bcr == False`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The number of cells that actually has a matching BCR can be tabluated.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>filter_bcr</th>\n",
       "      <th>False</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_bcr</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>15603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "filter_bcr  False\n",
       "has_bcr          \n",
       "False       15603\n",
       "True          889"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(adata.obs['has_bcr'], adata.obs['filter_bcr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>bcr_QC_pass</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_bcr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>15603</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>51</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bcr_QC_pass  False  True\n",
       "has_bcr                 \n",
       "False        15603     0\n",
       "True            51   838"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(adata.obs['has_bcr'], adata.obs['bcr_QC_pass'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>filter_bcr</th>\n",
       "      <th>False</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bcr_QC_pass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>15654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "filter_bcr   False\n",
       "bcr_QC_pass       \n",
       "False        15654\n",
       "True           838"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(adata.obs['bcr_QC_pass'], adata.obs['filter_bcr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now actually filter the AnnData object and run through a standard workflow starting by filtering genes and normalizing the data**\n",
    "\n",
    "Because the 'filtered' `AnnData` object was returned as a filtered but otherwise unprocessed object, we still need to normalize and run through the usual process here. The following is just a standard scanpy workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.var` of view, copying.\n"
     ]
    }
   ],
   "source": [
    "# filter genes\n",
    "sc.pp.filter_genes(adata, min_cells=3)\n",
    "# Normalize the counts\n",
    "sc.pp.normalize_total(adata, target_sum=1e4)\n",
    "# Logarithmize the data\n",
    "sc.pp.log1p(adata)\n",
    "# Stash the normalised counts\n",
    "adata.raw = adata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Identify highly-variable genes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)\n",
    "sc.pl.highly_variable_genes(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filter the genes to only those marked as highly-variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = adata[:, adata.var.highly_variable]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed. Scale the data to unit variance.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.obs` of view, copying.\n",
      "... storing 'sampleid' as categorical\n",
      "Trying to set attribute `.var` of view, copying.\n",
      "... storing 'feature_types' as categorical\n",
      "Trying to set attribute `.var` of view, copying.\n",
      "... storing 'genome' as categorical\n"
     ]
    }
   ],
   "source": [
    "sc.pp.regress_out(adata, ['n_counts', 'percent_mito'])\n",
    "sc.pp.scale(adata, max_value=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Run PCA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.tl.pca(adata, svd_solver='arpack')\n",
    "sc.pl.pca_variance_ratio(adata, log=True, n_pcs = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computing the neighborhood graph, umap and clusters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing the neighborhood graph\n",
    "sc.pp.neighbors(adata)\n",
    "# Embedding the neighborhood graph\n",
    "sc.tl.umap(adata)\n",
    "# Clustering the neighborhood graph\n",
    "sc.tl.leiden(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualizing the clusters and whether or not there's a corresponding BCR**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 989.28x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color=['leiden', 'bcr_QC_pass'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualizing some B cell genes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 989.28x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color=['IGHM', 'JCHAIN'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Save AnnData**\n",
    "\n",
    "We can save this `AnnData` object for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... storing 'feature_types' as categorical\n",
      "... storing 'genome' as categorical\n"
     ]
    }
   ],
   "source": [
    "adata.write('adata.h5ad', compression = 'gzip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Save dandelion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save the vdj object, we have two options - either save the `.data` and `.metadata` slots with pandas' functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "vdj.data.to_csv('filtered_vdj_table.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or save the whole Dandelion class object with either `.write_h5`, which saves the class to a HDF5 format, or using a pickle-based `.write_pkl` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "vdj.write_h5('dandelion_results.h5', complib = 'bzip2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "vdj.write_pkl('dandelion_results.pkl.pbz2') # this will automatically use bzip2 for compression, swith the extension to .gz for gzip"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (dandelion)",
   "language": "python",
   "name": "dandelion"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
