{
 "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dandelion==0.1.0 pandas==1.1.4 numpy==1.19.4 matplotlib==3.3.3 networkx==2.5 scipy==1.5.3 skbio==0.5.6\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import dandelion as ddl\n",
    "# change directory to somewhere more workable\n",
    "os.chdir(os.path.expanduser('/Users/kt16/Downloads/dandelion_tutorial/'))\n",
    "ddl.logging.print_header()"
   ]
  },
  {
   "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",
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       "        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, 52865.97it/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', 'locus_heavy', 'locus_light', 'productive_heavy', 'productive_light', 'v_call_genotyped_heavy', 'v_call_genotyped_light', 'j_call_heavy', 'j_call_light', 'c_call_heavy', 'c_call_light', 'umi_count_heavy_0', 'umi_count_light_0', 'umi_count_light_1', 'umi_count_light_2', 'junction_aa_heavy', 'junction_aa_light', 'status', 'productive', 'isotype', 'vdj_status_detail', 'vdj_status'\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": [
       "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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       "\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>has_bcr</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_BCR</th>\n",
       "      <td>15603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "filter_bcr  False\n",
       "has_bcr          \n",
       "True          889\n",
       "No_BCR      15603"
      ]
     },
     "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",
       "      <th>No_BCR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_bcr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>51</td>\n",
       "      <td>838</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_BCR</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bcr_QC_pass  False  True  No_BCR\n",
       "has_bcr                         \n",
       "True            51   838       0\n",
       "No_BCR           0     0   15603"
      ]
     },
     "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>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_BCR</th>\n",
       "      <td>15603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "filter_bcr   False\n",
       "bcr_QC_pass       \n",
       "False           51\n",
       "True           838\n",
       "No_BCR       15603"
      ]
     },
     "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": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}
