datalab/genomics/Explore 1000 Genomes Samples.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "<!-- Copyright 2015 Google Inc. All rights reserved. -->\n",
    "\n",
    "<!-- Licensed under the Apache License, Version 2.0 (the \"License\"); -->\n",
    "<!-- you may not use this file except in compliance with the License. -->\n",
    "<!-- You may obtain a copy of the License at -->\n",
    "\n",
    "<!--     http://www.apache.org/licenses/LICENSE-2.0 -->\n",
    "\n",
    "<!-- Unless required by applicable law or agreed to in writing, software -->\n",
    "<!-- distributed under the License is distributed on an \"AS IS\" BASIS, -->\n",
    "<!-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -->\n",
    "<!-- See the License for the specific language governing permissions and -->\n",
    "<!-- limitations under the License. -->\n",
    "\n",
    "# Explore the 1000 Genomes Sample Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates exploring the sample information for the 1000 Genomes dataset stored in BigQuery.  Specifically you will:\n",
    "* Use the `%%sql` statement to write and execute SQL statements within the notebook\n",
    "* Extract data from BigQuery and create a local dataset that can be manipulated in Python\n",
    "* Refine and pivot your local dataset via the Pandas Python library\n",
    "* Visualize different aspects of your dataset via the Matplotlib and Seaborn Python libraries\n",
    "\n",
    "Related Links:\n",
    "* [BigQuery](https://cloud.google.com/bigquery/)\n",
    "* BigQuery [SQL reference](https://cloud.google.com/bigquery/query-reference)\n",
    "* [1,000 Genomes Data Description](http://googlegenomics.readthedocs.org/en/latest/use_cases/discover_public_data/1000_genomes.html)\n",
    "* This notebook is based on the [Google Genomics](https://cloud.google.com/genomics/) BigQuery example [Exploring the Phenotypic Data](https://github.com/googlegenomics/bigquery-examples/tree/master/1000genomes/data-stories/exploring-the-phenotypic-data)\n",
    "\n",
    "----\n",
    "\n",
    "NOTE:\n",
    "\n",
    "* If you're new to notebooks, or want to check out additional samples, check out the full [list](../) of general notebooks.\n",
    "* For additional Genomics samples, check out the full [list](./) of Genomics notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with data in BigQuery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's take a look at the [1000 Genomes](http://www.1000genomes.org/) sample dataset table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "\n",
       "    <div class=\"bqsv\" id=\"1_144435458533\"></div>\n",
       "    <script>\n",
       "      require(['extensions/bigquery', 'element!1_144435458533', 'style!/static/extensions/bigquery.css'],\n",
       "        function(bq, dom) {\n",
       "          bq.renderSchema(dom, [{\"type\": \"STRING\", \"name\": \"Sample\", \"description\": \"Sample ID\"}, {\"type\": \"STRING\", \"name\": \"Family_ID\", \"description\": \"Family ID\"}, {\"type\": \"STRING\", \"name\": \"Population\", \"description\": \"3 letter population code\"}, {\"type\": \"STRING\", \"name\": \"Population_Description\", \"description\": \"Description of Population\"}, {\"type\": \"STRING\", \"name\": \"Gender\", \"description\": \"Gender\"}, {\"type\": \"STRING\", \"name\": \"Relationship\", \"description\": \"Relationship to other members of the family\"}, {\"type\": \"STRING\", \"name\": \"Unexpected_Parent_Child\", \"description\": \"sample id for unexpected parent child relationships\"}, {\"type\": \"STRING\", \"name\": \"Non_Paternity\", \"description\": \"sample ids for annotated non paternal relationships\"}, {\"type\": \"STRING\", \"name\": \"Siblings\", \"description\": \"sample ids for any siblings\"}, {\"type\": \"STRING\", \"name\": \"Grandparents\", \"description\": \"sample ids for any grand parents\"}, {\"type\": \"STRING\", \"name\": \"Avuncular\", \"description\": \"sample ids for any avuncular relationships\"}, {\"type\": \"STRING\", \"name\": \"Half_Siblings\", \"description\": \"sample ids for any half siblings\"}, {\"type\": \"STRING\", \"name\": \"Unknown_Second_Order\", \"description\": \"sample ids for any unknown second order relations\"}, {\"type\": \"STRING\", \"name\": \"Third_Order\", \"description\": \"sample ids for any third order cryptic relations. As mentioned above, this analysis was not as widely run as the other relatedness analyses and as such there may still be unannotated third order relations in the set\"}, {\"type\": \"BOOLEAN\", \"name\": \"In_Low_Coverage_Pilot\", \"description\": \"The sample is in the low coverage pilot experiment\"}, {\"type\": \"STRING\", \"name\": \"LC_Pilot_Platforms\", \"description\": \"low coverage pilot sequencing platforms \"}, {\"type\": \"STRING\", \"name\": \"LC_Pilot_Centers\", \"description\": \"low coverage pilot sequencing centers\"}, {\"type\": \"BOOLEAN\", \"name\": \"In_High_Coverage_Pilot\", \"description\": \"The sample is in the high coverage pilot\"}, {\"type\": \"STRING\", \"name\": \"HC_Pilot_Platforms\", \"description\": \"high coverage sequencing platforms\"}, {\"type\": \"STRING\", \"name\": \"HC_Pilot_Centers\", \"description\": \"high coverage sequencing centers\"}, {\"type\": \"BOOLEAN\", \"name\": \"In_Exon_Targetted_Pilot\", \"description\": \"The Sample is in the exon targetted pilot experiment\"}, {\"type\": \"STRING\", \"name\": \"ET_Pilot_Platforms\", \"description\": \"exon targetted sequencing platforms,\"}, {\"type\": \"STRING\", \"name\": \"ET_Pilot_Centers\", \"description\": \"exon targetted sequencing centers,\"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_Sequence_in_Phase1\", \"description\": \"Has sequence low coverage sequence in the 20101123.sequence.index file or exome sequence in the 20110522 sequence index file\"}, {\"type\": \"STRING\", \"name\": \"Phase1_LC_Platform\", \"description\": \"phase1 low coverage sequencing platforms\"}, {\"type\": \"STRING\", \"name\": \"Phase1_LC_Centers\", \"description\": \"phase1 low coverage sequencing centers\"}, {\"type\": \"STRING\", \"name\": \"Phase1_E_Platform\", \"description\": \"phase1 exome sequencing platforms\"}, {\"type\": \"STRING\", \"name\": \"Phase1_E_Centers\", \"description\": \"phase1 exome sequencing centers\"}, {\"type\": \"BOOLEAN\", \"name\": \"In_Phase1_Integrated_Variant_Set\", \"description\": \"The sample is genotyped in the phase1 integrated call set on autosomes and chrX\"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_Phase1_chrY_SNPS\", \"description\": \"The sample is genotyped in the chrY phase1 snp set\"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_phase1_chrY_Deletions\", \"description\": \"The sample is genotyepd in the chrY phase1 deletions\"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_phase1_chrMT_SNPs\", \"description\": \"The sample is genotyped in the phase1 chrMT snps\"}, {\"type\": \"STRING\", \"name\": \"Main_project_LC_Centers\", \"description\": \"low coverage sequencing centers for final sequencing round\"}, {\"type\": \"STRING\", \"name\": \"Main_project_LC_platform\", \"description\": \"low coverage sequencing platform for final sequencing round\"}, {\"type\": \"FLOAT\", \"name\": \"Total_LC_Sequence\", \"description\": \"The total amount of low coverage sequence available\"}, {\"type\": \"FLOAT\", \"name\": \"LC_Non_Duplicated_Aligned_Coverage\", \"description\": \"The non duplicated aligned coverage for the low coverage sequence data.  This was calculated using the ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/alignment_indices/20130502.low_coverage.alignment.index.bas.gz file, the (mapped bases - duplicated bases) was summed for each sample and divided by 2.75GB and rounded to 2dp\"}, {\"type\": \"STRING\", \"name\": \"Main_Project_E_Centers\", \"description\": \"Exome sequencing centers for the final sequencing round\"}, {\"type\": \"STRING\", \"name\": \"Main_Project_E_Platform\", \"description\": \"Exome sequencing platform for the final sequencing round\"}, {\"type\": \"FLOAT\", \"name\": \"Total_Exome_Sequence\", \"description\": \"The total amount of exome sequence available\"}, {\"type\": \"FLOAT\", \"name\": \"X_Targets_Covered_to_20x_or_greater\", \"description\": \"The percentage of targets covered to 20x or greater as calculated by the picard function CalculateHsMetrics using these targets ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/exome_pull_down_targets_phases1_and_2/20120518.consensus.annotation.bed\"}, {\"type\": \"FLOAT\", \"name\": \"VerifyBam_E_Omni_Free\", \"description\": \"Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.\"}, {\"type\": \"FLOAT\", \"name\": \"VerifyBam_E_Affy_Free\", \"description\": \"Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.\"}, {\"type\": \"FLOAT\", \"name\": \"VerifyBam_E_Omni_Chip\", \"description\": \"Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.\"}, {\"type\": \"FLOAT\", \"name\": \"VerifyBam_E_Affy_Chip\", \"description\": \"Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.\"}, {\"type\": \"FLOAT\", \"name\": \"VerifyBam_LC_Omni_Free\", \"description\": \"Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.\"}, {\"type\": \"FLOAT\", \"name\": \"VerifyBam_LC_Affy_Free\", \"description\": \"Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.\"}, {\"type\": \"FLOAT\", \"name\": \"VerifyBam_LC_Omni_Chip\", \"description\": \"Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.\"}, {\"type\": \"FLOAT\", \"name\": \"VerifyBam_LC_Affy_Chip\", \"description\": \"Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.\"}, {\"type\": \"FLOAT\", \"name\": \"LC_Indel_Ratio\", \"description\": \"Both Indel ratios are the ratio of insertions to deletions found in that sample using a quick test (based on samtools). If the ratio is higher than 5 the sample is withdrawn.\"}, {\"type\": \"FLOAT\", \"name\": \"E_Indel_Ratio\", \"description\": \"Both Indel ratios are the ratio of insertions to deletions found in that sample using a quick test (based on samtools). If the ratio is higher than 5 the sample is withdrawn.\"}, {\"type\": \"BOOLEAN\", \"name\": \"LC_Passed_QC\", \"description\": \"These are binary flags showing if the sample passed QC, All samples which have passed QC have bam files. Only samples which have both exome and low coverage data are found under ftp/data and listed in the standard alignment index. The small number of other samples are found in ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/phase3_EX_or_LC_only_alignment/\"}, {\"type\": \"BOOLEAN\", \"name\": \"E_Passed_QC\", \"description\": \"These are binary flags showing if the sample passed QC, All samples which have passed QC have bam files. Only samples which have both exome and low coverage data are found under ftp/data and listed in the standard alignment index. The small number of other samples are found in ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/phase3_EX_or_LC_only_alignment/\"}, {\"type\": \"BOOLEAN\", \"name\": \"In_Final_Phase_Variant_Calling\", \"description\": \"Any sample which has both LC and E QC passed bams is in the final analysis set\"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_Omni_Genotypes\", \"description\": \"Omni Genotypes in ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20120131_omni_genotypes_and_intensities/Omni25_genotypes_2141_samples.b37.vcf.gz   \"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_Axiom_Genotypes\", \"description\": \"Axiom Genotypes in ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20110210_Affymetrix_Axiom/Affymetrix_Axiom_DB_2010_v4_b37.vcf.gz         \"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_Affy_6_0_Genotypes\", \"description\": \"Affy 6.0 Genotypes in  ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20121128_corriel_p3_sample_genotypes/\"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_Exome_LOF_Genotypes\", \"description\": \"ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20121009_broad_exome_chip/ALL.wgs.broad_exome_lof_indel_v2.20121009.snps_and_indels.snpchip.genotypes.vcf.gz\"}, {\"type\": \"FLOAT\", \"name\": \"EBV_Coverage\", \"description\": \"This was calculated by looking at the alignment of the data to NC_007605 in the low coverage bam files and using that to calculate coverage   \"}, {\"type\": \"STRING\", \"name\": \"DNA_Source_from_Coriell\", \"description\": \"This was the annotated DNA Source from Coriell     \"}, {\"type\": \"BOOLEAN\", \"name\": \"Has_Sequence_from_Blood_in_Index\", \"description\": \"In the later stages of the project some populations has multiple study ids, one to indicate sequencing from blood. This data for each sample has not been treated independently in the alignment process but when there is both LCL and Blood sourced data they are both together in single bams\"}, {\"type\": \"STRING\", \"name\": \"Super_Population\", \"description\": \"From ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/20131219.superpopulations.tsv\"}, {\"type\": \"STRING\", \"name\": \"Super_Population_Description\", \"description\": \"From ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/20131219.superpopulations.tsv\"}]);\n",
       "        }\n",
       "      );\n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "[{ 'name': 'Sample', 'type': 'STRING', 'mode':'None', 'description': 'Sample ID' },\n",
       " { 'name': 'Family_ID', 'type': 'STRING', 'mode':'None', 'description': 'Family ID' },\n",
       " { 'name': 'Population', 'type': 'STRING', 'mode':'None', 'description': '3 letter population code' },\n",
       " { 'name': 'Population_Description', 'type': 'STRING', 'mode':'None', 'description': 'Description of Population' },\n",
       " { 'name': 'Gender', 'type': 'STRING', 'mode':'None', 'description': 'Gender' },\n",
       " { 'name': 'Relationship', 'type': 'STRING', 'mode':'None', 'description': 'Relationship to other members of the family' },\n",
       " { 'name': 'Unexpected_Parent_Child', 'type': 'STRING', 'mode':'None', 'description': 'sample id for unexpected parent child relationships' },\n",
       " { 'name': 'Non_Paternity', 'type': 'STRING', 'mode':'None', 'description': 'sample ids for annotated non paternal relationships' },\n",
       " { 'name': 'Siblings', 'type': 'STRING', 'mode':'None', 'description': 'sample ids for any siblings' },\n",
       " { 'name': 'Grandparents', 'type': 'STRING', 'mode':'None', 'description': 'sample ids for any grand parents' },\n",
       " { 'name': 'Avuncular', 'type': 'STRING', 'mode':'None', 'description': 'sample ids for any avuncular relationships' },\n",
       " { 'name': 'Half_Siblings', 'type': 'STRING', 'mode':'None', 'description': 'sample ids for any half siblings' },\n",
       " { 'name': 'Unknown_Second_Order', 'type': 'STRING', 'mode':'None', 'description': 'sample ids for any unknown second order relations' },\n",
       " { 'name': 'Third_Order', 'type': 'STRING', 'mode':'None', 'description': 'sample ids for any third order cryptic relations. As mentioned above, this analysis was not as widely run as the other relatedness analyses and as such there may still be unannotated third order relations in the set' },\n",
       " { 'name': 'In_Low_Coverage_Pilot', 'type': 'BOOLEAN', 'mode':'None', 'description': 'The sample is in the low coverage pilot experiment' },\n",
       " { 'name': 'LC_Pilot_Platforms', 'type': 'STRING', 'mode':'None', 'description': 'low coverage pilot sequencing platforms ' },\n",
       " { 'name': 'LC_Pilot_Centers', 'type': 'STRING', 'mode':'None', 'description': 'low coverage pilot sequencing centers' },\n",
       " { 'name': 'In_High_Coverage_Pilot', 'type': 'BOOLEAN', 'mode':'None', 'description': 'The sample is in the high coverage pilot' },\n",
       " { 'name': 'HC_Pilot_Platforms', 'type': 'STRING', 'mode':'None', 'description': 'high coverage sequencing platforms' },\n",
       " { 'name': 'HC_Pilot_Centers', 'type': 'STRING', 'mode':'None', 'description': 'high coverage sequencing centers' },\n",
       " { 'name': 'In_Exon_Targetted_Pilot', 'type': 'BOOLEAN', 'mode':'None', 'description': 'The Sample is in the exon targetted pilot experiment' },\n",
       " { 'name': 'ET_Pilot_Platforms', 'type': 'STRING', 'mode':'None', 'description': 'exon targetted sequencing platforms,' },\n",
       " { 'name': 'ET_Pilot_Centers', 'type': 'STRING', 'mode':'None', 'description': 'exon targetted sequencing centers,' },\n",
       " { 'name': 'Has_Sequence_in_Phase1', 'type': 'BOOLEAN', 'mode':'None', 'description': 'Has sequence low coverage sequence in the 20101123.sequence.index file or exome sequence in the 20110522 sequence index file' },\n",
       " { 'name': 'Phase1_LC_Platform', 'type': 'STRING', 'mode':'None', 'description': 'phase1 low coverage sequencing platforms' },\n",
       " { 'name': 'Phase1_LC_Centers', 'type': 'STRING', 'mode':'None', 'description': 'phase1 low coverage sequencing centers' },\n",
       " { 'name': 'Phase1_E_Platform', 'type': 'STRING', 'mode':'None', 'description': 'phase1 exome sequencing platforms' },\n",
       " { 'name': 'Phase1_E_Centers', 'type': 'STRING', 'mode':'None', 'description': 'phase1 exome sequencing centers' },\n",
       " { 'name': 'In_Phase1_Integrated_Variant_Set', 'type': 'BOOLEAN', 'mode':'None', 'description': 'The sample is genotyped in the phase1 integrated call set on autosomes and chrX' },\n",
       " { 'name': 'Has_Phase1_chrY_SNPS', 'type': 'BOOLEAN', 'mode':'None', 'description': 'The sample is genotyped in the chrY phase1 snp set' },\n",
       " { 'name': 'Has_phase1_chrY_Deletions', 'type': 'BOOLEAN', 'mode':'None', 'description': 'The sample is genotyepd in the chrY phase1 deletions' },\n",
       " { 'name': 'Has_phase1_chrMT_SNPs', 'type': 'BOOLEAN', 'mode':'None', 'description': 'The sample is genotyped in the phase1 chrMT snps' },\n",
       " { 'name': 'Main_project_LC_Centers', 'type': 'STRING', 'mode':'None', 'description': 'low coverage sequencing centers for final sequencing round' },\n",
       " { 'name': 'Main_project_LC_platform', 'type': 'STRING', 'mode':'None', 'description': 'low coverage sequencing platform for final sequencing round' },\n",
       " { 'name': 'Total_LC_Sequence', 'type': 'FLOAT', 'mode':'None', 'description': 'The total amount of low coverage sequence available' },\n",
       " { 'name': 'LC_Non_Duplicated_Aligned_Coverage', 'type': 'FLOAT', 'mode':'None', 'description': 'The non duplicated aligned coverage for the low coverage sequence data.  This was calculated using the ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/alignment_indices/20130502.low_coverage.alignment.index.bas.gz file, the (mapped bases - duplicated bases) was summed for each sample and divided by 2.75GB and rounded to 2dp' },\n",
       " { 'name': 'Main_Project_E_Centers', 'type': 'STRING', 'mode':'None', 'description': 'Exome sequencing centers for the final sequencing round' },\n",
       " { 'name': 'Main_Project_E_Platform', 'type': 'STRING', 'mode':'None', 'description': 'Exome sequencing platform for the final sequencing round' },\n",
       " { 'name': 'Total_Exome_Sequence', 'type': 'FLOAT', 'mode':'None', 'description': 'The total amount of exome sequence available' },\n",
       " { 'name': 'X_Targets_Covered_to_20x_or_greater', 'type': 'FLOAT', 'mode':'None', 'description': 'The percentage of targets covered to 20x or greater as calculated by the picard function CalculateHsMetrics using these targets ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/exome_pull_down_targets_phases1_and_2/20120518.consensus.annotation.bed' },\n",
       " { 'name': 'VerifyBam_E_Omni_Free', 'type': 'FLOAT', 'mode':'None', 'description': 'Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.' },\n",
       " { 'name': 'VerifyBam_E_Affy_Free', 'type': 'FLOAT', 'mode':'None', 'description': 'Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.' },\n",
       " { 'name': 'VerifyBam_E_Omni_Chip', 'type': 'FLOAT', 'mode':'None', 'description': 'Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.' },\n",
       " { 'name': 'VerifyBam_E_Affy_Chip', 'type': 'FLOAT', 'mode':'None', 'description': 'Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.' },\n",
       " { 'name': 'VerifyBam_LC_Omni_Free', 'type': 'FLOAT', 'mode':'None', 'description': 'Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.' },\n",
       " { 'name': 'VerifyBam_LC_Affy_Free', 'type': 'FLOAT', 'mode':'None', 'description': 'Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.' },\n",
       " { 'name': 'VerifyBam_LC_Omni_Chip', 'type': 'FLOAT', 'mode':'None', 'description': 'Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.' },\n",
       " { 'name': 'VerifyBam_LC_Affy_Chip', 'type': 'FLOAT', 'mode':'None', 'description': 'Value from UMich's VerifyBamID BAM QC program http://genome.sph.umich.edu/wiki/VerifyBamID.  The Free measures use a statistical model based on the haplotypes discovered by the chip. The Chip measure considers the genotypes available for that individual from that chip. We use greater than 3% as a cut off for our our low coverage samples and greater than 3.5% for our exome samples.' },\n",
       " { 'name': 'LC_Indel_Ratio', 'type': 'FLOAT', 'mode':'None', 'description': 'Both Indel ratios are the ratio of insertions to deletions found in that sample using a quick test (based on samtools). If the ratio is higher than 5 the sample is withdrawn.' },\n",
       " { 'name': 'E_Indel_Ratio', 'type': 'FLOAT', 'mode':'None', 'description': 'Both Indel ratios are the ratio of insertions to deletions found in that sample using a quick test (based on samtools). If the ratio is higher than 5 the sample is withdrawn.' },\n",
       " { 'name': 'LC_Passed_QC', 'type': 'BOOLEAN', 'mode':'None', 'description': 'These are binary flags showing if the sample passed QC, All samples which have passed QC have bam files. Only samples which have both exome and low coverage data are found under ftp/data and listed in the standard alignment index. The small number of other samples are found in ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/phase3_EX_or_LC_only_alignment/' },\n",
       " { 'name': 'E_Passed_QC', 'type': 'BOOLEAN', 'mode':'None', 'description': 'These are binary flags showing if the sample passed QC, All samples which have passed QC have bam files. Only samples which have both exome and low coverage data are found under ftp/data and listed in the standard alignment index. The small number of other samples are found in ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/phase3_EX_or_LC_only_alignment/' },\n",
       " { 'name': 'In_Final_Phase_Variant_Calling', 'type': 'BOOLEAN', 'mode':'None', 'description': 'Any sample which has both LC and E QC passed bams is in the final analysis set' },\n",
       " { 'name': 'Has_Omni_Genotypes', 'type': 'BOOLEAN', 'mode':'None', 'description': 'Omni Genotypes in ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20120131_omni_genotypes_and_intensities/Omni25_genotypes_2141_samples.b37.vcf.gz   ' },\n",
       " { 'name': 'Has_Axiom_Genotypes', 'type': 'BOOLEAN', 'mode':'None', 'description': 'Axiom Genotypes in ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20110210_Affymetrix_Axiom/Affymetrix_Axiom_DB_2010_v4_b37.vcf.gz         ' },\n",
       " { 'name': 'Has_Affy_6_0_Genotypes', 'type': 'BOOLEAN', 'mode':'None', 'description': 'Affy 6.0 Genotypes in  ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20121128_corriel_p3_sample_genotypes/' },\n",
       " { 'name': 'Has_Exome_LOF_Genotypes', 'type': 'BOOLEAN', 'mode':'None', 'description': 'ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20121009_broad_exome_chip/ALL.wgs.broad_exome_lof_indel_v2.20121009.snps_and_indels.snpchip.genotypes.vcf.gz' },\n",
       " { 'name': 'EBV_Coverage', 'type': 'FLOAT', 'mode':'None', 'description': 'This was calculated by looking at the alignment of the data to NC_007605 in the low coverage bam files and using that to calculate coverage   ' },\n",
       " { 'name': 'DNA_Source_from_Coriell', 'type': 'STRING', 'mode':'None', 'description': 'This was the annotated DNA Source from Coriell     ' },\n",
       " { 'name': 'Has_Sequence_from_Blood_in_Index', 'type': 'BOOLEAN', 'mode':'None', 'description': 'In the later stages of the project some populations has multiple study ids, one to indicate sequencing from blood. This data for each sample has not been treated independently in the alignment process but when there is both LCL and Blood sourced data they are both together in single bams' },\n",
       " { 'name': 'Super_Population', 'type': 'STRING', 'mode':'None', 'description': 'From ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/20131219.superpopulations.tsv' },\n",
       " { 'name': 'Super_Population_Description', 'type': 'STRING', 'mode':'None', 'description': 'From ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/20131219.superpopulations.tsv' }]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gcp.bigquery as bq\n",
    "samples_table = bq.Table('genomics-public-data:1000_genomes.sample_info')\n",
    "samples_table.schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the table schema that a number of different annotations exist for each genomic sample. \n",
    "\n",
    "To get a feel for the dataset, let's see how the samples are distributed across populations and super populations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%sql --module pops\n",
    "SELECT\n",
    "  population,\n",
    "  population_description,\n",
    "  super_population,\n",
    "  super_population_description,\n",
    "  COUNT(population) AS population_count,\n",
    "FROM\n",
    "  $samples_table\n",
    "GROUP BY\n",
    "  population,\n",
    "  population_description,\n",
    "  super_population,\n",
    "  super_population_description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_description</th>\n",
       "      <th>super_population</th>\n",
       "      <th>super_population_description</th>\n",
       "      <th>population_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GBR</td>\n",
       "      <td>British in England and Scotland</td>\n",
       "      <td>EUR</td>\n",
       "      <td>European</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIN</td>\n",
       "      <td>Finnish in Finland</td>\n",
       "      <td>EUR</td>\n",
       "      <td>European</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHS</td>\n",
       "      <td>Southern Han Chinese, China</td>\n",
       "      <td>EAS</td>\n",
       "      <td>East Asian</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PUR</td>\n",
       "      <td>Puerto Rican in Puerto Rico</td>\n",
       "      <td>AMR</td>\n",
       "      <td>American</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CDX</td>\n",
       "      <td>Chinese Dai in Xishuangbanna, China</td>\n",
       "      <td>EAS</td>\n",
       "      <td>East Asian</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CLM</td>\n",
       "      <td>Colombian in Medellin, Colombia</td>\n",
       "      <td>AMR</td>\n",
       "      <td>American</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Iberian populations in Spain</td>\n",
       "      <td>EUR</td>\n",
       "      <td>European</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PEL</td>\n",
       "      <td>Peruvian in Lima, Peru</td>\n",
       "      <td>AMR</td>\n",
       "      <td>American</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PJL</td>\n",
       "      <td>Punjabi in Lahore,Pakistan</td>\n",
       "      <td>SAS</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>KHV</td>\n",
       "      <td>Kinh in Ho Chi Minh City, Vietnam</td>\n",
       "      <td>EAS</td>\n",
       "      <td>East Asian</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ACB</td>\n",
       "      <td>African Caribbean in Barbados</td>\n",
       "      <td>AFR</td>\n",
       "      <td>African</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GWD</td>\n",
       "      <td>Gambian in Western Division, The Gambia</td>\n",
       "      <td>AFR</td>\n",
       "      <td>African</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ESN</td>\n",
       "      <td>Esan in Nigeria</td>\n",
       "      <td>AFR</td>\n",
       "      <td>African</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BEB</td>\n",
       "      <td>Bengali in Bangladesh</td>\n",
       "      <td>SAS</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MSL</td>\n",
       "      <td>Mende in Sierra Leone</td>\n",
       "      <td>AFR</td>\n",
       "      <td>African</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>STU</td>\n",
       "      <td>Sri Lankan Tamil in the UK</td>\n",
       "      <td>SAS</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ITU</td>\n",
       "      <td>Indian Telugu in the UK</td>\n",
       "      <td>SAS</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CEU</td>\n",
       "      <td>Utah residents with Northern and Western Europ...</td>\n",
       "      <td>EUR</td>\n",
       "      <td>European</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>YRI</td>\n",
       "      <td>Yoruba in Ibadan, Nigeria</td>\n",
       "      <td>AFR</td>\n",
       "      <td>African</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CHB</td>\n",
       "      <td>Han Chinese in Bejing, China</td>\n",
       "      <td>EAS</td>\n",
       "      <td>East Asian</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>JPT</td>\n",
       "      <td>Japanese in Tokyo, Japan</td>\n",
       "      <td>EAS</td>\n",
       "      <td>East Asian</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LWK</td>\n",
       "      <td>Luhya in Webuye, Kenya</td>\n",
       "      <td>AFR</td>\n",
       "      <td>African</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ASW</td>\n",
       "      <td>African Ancestry in Southwest US</td>\n",
       "      <td>AFR</td>\n",
       "      <td>African</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MXL</td>\n",
       "      <td>Mexican Ancestry in Los Angeles, California</td>\n",
       "      <td>AMR</td>\n",
       "      <td>American</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TSI</td>\n",
       "      <td>Toscani in Italy</td>\n",
       "      <td>EUR</td>\n",
       "      <td>European</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>GIH</td>\n",
       "      <td>Gujarati Indian in Houston,TX</td>\n",
       "      <td>SAS</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population                             population_description  \\\n",
       "0         GBR                    British in England and Scotland   \n",
       "1         FIN                                 Finnish in Finland   \n",
       "2         CHS                        Southern Han Chinese, China   \n",
       "3         PUR                        Puerto Rican in Puerto Rico   \n",
       "4         CDX                Chinese Dai in Xishuangbanna, China   \n",
       "5         CLM                    Colombian in Medellin, Colombia   \n",
       "6         IBS                       Iberian populations in Spain   \n",
       "7         PEL                             Peruvian in Lima, Peru   \n",
       "8         PJL                         Punjabi in Lahore,Pakistan   \n",
       "9         KHV                  Kinh in Ho Chi Minh City, Vietnam   \n",
       "10        ACB                      African Caribbean in Barbados   \n",
       "11        GWD            Gambian in Western Division, The Gambia   \n",
       "12        ESN                                    Esan in Nigeria   \n",
       "13        BEB                              Bengali in Bangladesh   \n",
       "14        MSL                              Mende in Sierra Leone   \n",
       "15        STU                         Sri Lankan Tamil in the UK   \n",
       "16        ITU                            Indian Telugu in the UK   \n",
       "17        CEU  Utah residents with Northern and Western Europ...   \n",
       "18        YRI                          Yoruba in Ibadan, Nigeria   \n",
       "19        CHB                       Han Chinese in Bejing, China   \n",
       "20        JPT                           Japanese in Tokyo, Japan   \n",
       "21        LWK                             Luhya in Webuye, Kenya   \n",
       "22        ASW                   African Ancestry in Southwest US   \n",
       "23        MXL        Mexican Ancestry in Los Angeles, California   \n",
       "24        TSI                                   Toscani in Italy   \n",
       "25        GIH                      Gujarati Indian in Houston,TX   \n",
       "\n",
       "   super_population super_population_description  population_count  \n",
       "0               EUR                     European               107  \n",
       "1               EUR                     European               105  \n",
       "2               EAS                   East Asian               171  \n",
       "3               AMR                     American               150  \n",
       "4               EAS                   East Asian               109  \n",
       "5               AMR                     American               148  \n",
       "6               EUR                     European               162  \n",
       "7               AMR                     American               130  \n",
       "8               SAS                  South Asian               158  \n",
       "9               EAS                   East Asian               124  \n",
       "10              AFR                      African               123  \n",
       "11              AFR                      African               180  \n",
       "12              AFR                      African               173  \n",
       "13              SAS                  South Asian               144  \n",
       "14              AFR                      African               128  \n",
       "15              SAS                  South Asian               128  \n",
       "16              SAS                  South Asian               118  \n",
       "17              EUR                     European               183  \n",
       "18              AFR                      African               186  \n",
       "19              EAS                   East Asian               108  \n",
       "20              EAS                   East Asian               105  \n",
       "21              AFR                      African               116  \n",
       "22              AFR                      African               112  \n",
       "23              AMR                     American               107  \n",
       "24              EUR                     European               112  \n",
       "25              SAS                  South Asian               113  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bq.Query(pops, samples_table=samples_table).to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to further analyze our query results locally within the IPython notebook, let's convert the result set into a [Pandas dataframe](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_description</th>\n",
       "      <th>super_population</th>\n",
       "      <th>super_population_description</th>\n",
       "      <th>population_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GBR</td>\n",
       "      <td>British in England and Scotland</td>\n",
       "      <td>EUR</td>\n",
       "      <td>European</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIN</td>\n",
       "      <td>Finnish in Finland</td>\n",
       "      <td>EUR</td>\n",
       "      <td>European</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHS</td>\n",
       "      <td>Southern Han Chinese, China</td>\n",
       "      <td>EAS</td>\n",
       "      <td>East Asian</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PUR</td>\n",
       "      <td>Puerto Rican in Puerto Rico</td>\n",
       "      <td>AMR</td>\n",
       "      <td>American</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CDX</td>\n",
       "      <td>Chinese Dai in Xishuangbanna, China</td>\n",
       "      <td>EAS</td>\n",
       "      <td>East Asian</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  population               population_description super_population  \\\n",
       "0        GBR      British in England and Scotland              EUR   \n",
       "1        FIN                   Finnish in Finland              EUR   \n",
       "2        CHS          Southern Han Chinese, China              EAS   \n",
       "3        PUR          Puerto Rican in Puerto Rico              AMR   \n",
       "4        CDX  Chinese Dai in Xishuangbanna, China              EAS   \n",
       "\n",
       "  super_population_description  population_count  \n",
       "0                     European               107  \n",
       "1                     European               105  \n",
       "2                   East Asian               171  \n",
       "3                     American               150  \n",
       "4                   East Asian               109  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pops_df = bq.Query(pops, samples_table=samples_table).to_dataframe()\n",
    "pops_df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas dataframes have a [`dataframe.plot()`](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.plot.html?highlight=dataframe.plot#pandas.DataFrame.plot) method that allows us to quickly render a number of standard visualizations. Let's draw a bar chart of the per-population sample counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3def0dc450>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AsASlDACAJShlAAAsQSkDAGAJTvQC4HO4XA1XK0oZgM/hcjVcrShlAD6Jy9VwNaKUAQCO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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3def0dc8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "pops_df.plot(kind='bar', y='population_count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a dataframe object, we can compute arbitrary rollups and aggregations locally. For example, let's aggregate the count of samples from the population level to the super population level. We'll do this via the [`dataframe.groupby`](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.groupby.html?highlight=groupby#pandas.DataFrame.groupby) operation, which is generally of the form: \n",
    "\n",
    "```dataframe.groupby(\"column name\").aggregation_func()```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3deef5bf10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WLNGgQYNUXFysd99919nluY3FixcrPDxckrR27Vrt3r1bubm5Sk1N1cyZM51c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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3deef6e610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "superpop_df = pops_df.groupby('super_population').sum()\n",
    "superpop_df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the distribution of genome samples across population and super population is relatively uniform, the exception being the AFR (African) super population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genome sample metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore a few quantitative attributes of the genomic samples. We'll keep our super population sample classification, but also add some metrics around the extent to which given samples were sequenced, for exomic and low coverage regions. We'll further annotate our samples with a tag for the laboratory/center that produced performed the sequencing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%sql --module  metrics\n",
    "select \n",
    "    super_population\n",
    "    , total_lc_sequence -- lc=low coverage\n",
    "    , total_exome_sequence\n",
    "    , Main_Project_E_Centers\n",
    "from $samples_table\n",
    "where \n",
    "    total_exome_sequence is not null\n",
    "    and total_lc_sequence is not null\n",
    "    and Main_Project_E_Centers is not null\n",
    "    and Main_Project_E_Centers != 'BCM,BGI' -- remove this single outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we can convert these results to a Pandas dataframe for local analysis and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>super_population</th>\n",
       "      <th>total_lc_sequence</th>\n",
       "      <th>total_exome_sequence</th>\n",
       "      <th>Main_Project_E_Centers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EUR</td>\n",
       "      <td>14714705600</td>\n",
       "      <td>9580861000</td>\n",
       "      <td>WUGSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EUR</td>\n",
       "      <td>30285067857</td>\n",
       "      <td>5856213714</td>\n",
       "      <td>BCM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EUR</td>\n",
       "      <td>25134554475</td>\n",
       "      <td>8125011458</td>\n",
       "      <td>BCM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EUR</td>\n",
       "      <td>42906753668</td>\n",
       "      <td>15144863140</td>\n",
       "      <td>BI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EUR</td>\n",
       "      <td>22115533074</td>\n",
       "      <td>11754108800</td>\n",
       "      <td>WUGSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EUR</td>\n",
       "      <td>21621189887</td>\n",
       "      <td>9003406100</td>\n",
       "      <td>WUGSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EUR</td>\n",
       "      <td>16331033600</td>\n",
       "      <td>9145497300</td>\n",
       "      <td>WUGSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EUR</td>\n",
       "      <td>22262720677</td>\n",
       "      <td>5851507215</td>\n",
       "      <td>BCM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EUR</td>\n",
       "      <td>24284331308</td>\n",
       "      <td>6640380441</td>\n",
       "      <td>BCM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EUR</td>\n",
       "      <td>23778576248</td>\n",
       "      <td>9595593000</td>\n",
       "      <td>WUGSC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  super_population  total_lc_sequence  total_exome_sequence  \\\n",
       "0              EUR        14714705600            9580861000   \n",
       "1              EUR        30285067857            5856213714   \n",
       "2              EUR        25134554475            8125011458   \n",
       "3              EUR        42906753668           15144863140   \n",
       "4              EUR        22115533074           11754108800   \n",
       "5              EUR        21621189887            9003406100   \n",
       "6              EUR        16331033600            9145497300   \n",
       "7              EUR        22262720677            5851507215   \n",
       "8              EUR        24284331308            6640380441   \n",
       "9              EUR        23778576248            9595593000   \n",
       "\n",
       "  Main_Project_E_Centers  \n",
       "0                  WUGSC  \n",
       "1                    BCM  \n",
       "2                    BCM  \n",
       "3                     BI  \n",
       "4                  WUGSC  \n",
       "5                  WUGSC  \n",
       "6                  WUGSC  \n",
       "7                    BCM  \n",
       "8                    BCM  \n",
       "9                  WUGSC  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = bq.Query(metrics, samples_table=samples_table).to_dataframe()\n",
    "df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a feel for the quantitative sample attributes, let's see how their values are distributed among the dataset samples by plotting histograms. Note that a histogram of any dataframe attribute can be easily rendered with the pattern `dataframe.attribute_name.hist()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3deee3af50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f3deec945d0>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QBw4csH5DSM7X7RH5okWLrL/DXhcREYG8vDwcPnwYv/jFL6zX35aWliI/Px95eXnIysrC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\nRCRh/w/PxGgL85GnTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3def0dcd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(alpha=0.5, bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the joint distribution of these two traits look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nZ8PX1xf79u3D+vXrTbbp3r07jh49ioEDB6KwsBDp6ekIDAxssqaCgjIxpVuV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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3deee0bdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot('total_exome_sequence', 'total_lc_sequence', df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only a very slight positive correlation. Let's further annotate our scatter chart by rendering each mark with a color according to its super population assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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85zvfobCwkMWLF7Ns2TJ/3i6EENet2267jddee41HH32UDh06kJGR4XvtwpZ7ffv2RVEU\nzGYzXbt25eTJkxw8eJAtW7bw4YcfAlBdXe07ftCgQU1+dnR0NF26dAG8zdg/++wzYmNj6devH4qi\nYDAYGDBgAEeOHCEuLo6ePXsSFhYGeJtaHDlypFGIXVhvg7///e+sW7fON4o8c+YMHTt2vGxdJSUl\nvg5QDXU1uOGGGwBISUmhtLS0ye8Hfo7E3nzzTd5//31sNm9blczMTOk0L4QQfjCZTMyfP5/nn3+e\noqIiFEXhzJkzAOzdu7fRsbt370bXderq6jh8+DCdOnWiW7du3HXXXWRnZ5Odnc1HH33kO765+2Dl\n5eUcO3YMgC+//JKsrCwyMjLYtWsXHo8HTdPYuXMnWVlZ6LrOgQMHqK+vx+PxkJubS2ZmJtHR0Zet\nF6CiooJ33nmH7Oxsli1bRnJysi/sLjeKi4uL4/DhwwDs2LGDrKws32v+hOb5/BqJmUymRluxQPM/\nPCGEELB69Wo++OADVFUlLCyM4cOHs2LFCn784x8zaNCgRr+0Y2NjeeCBBzhz5gxz584lJiaG+++/\nnyeffJL33nsPXdfp0aMHjz32mF+fHRcXR3Z2NocOHcJutzNv3jzCwsIYN24cc+bMAWDixIl07dqV\nrVu30rFjR+bPn8+pU6cYN24c3bp1Iz09nWXLll1Ur6IoKIpCVFQUffr04fvf/z6dO3dutPXWyJEj\n+fnPf87o0aPp2rWr772LFi1i4cKFGAwGIiMjfVt7XRh6/lzKVHQ/ou6+++5jwYIFPPzww3z44Yes\nXLmSNWvW8Nprr/n1gwyEYGz+GUw1BVs9IDX5K9hqCrZ6IHhraokPPviA48eP8+CDD7ZSRfja+vlj\n69atLF++nOeff77VPr8t+DUSW7BgAfPnz+fYsWOMGzcOi8USkPn+QghxPfNn5HEpzz//PLm5uY3O\n8+yzz17R+RpGVqHGr5EYgKZpHDt2DF3X6dKlC0aj372DAyIY/xYWTDUFWz0gNfkr2GoKtnogeGsS\nbc+vJGq4AQfetG64UXj+zThx/XI5vE2LTQkJ7VyJEOJ641eI/fSnP/X9e319PUVFRaSkpLBhw4aA\nFSZCQ8ma1VR+uQMA26DBxE659HoVIYQIBL9C7MKw2rx5s983C8W1y+Vw+AIMoPLLHdiGDJMRmRCi\nzVzVPPkRI0awZcuW1q5FCCGEuCJXfE+sYRGcy+UKWFEiNJgSErANGtzocqKMwoQIXT179qR79+6+\nx1OnTuW+++5j/PjxrFixwtcya+vWrbzxxhu8+uqrrFixgueee47k5GTq6+u54447uOOOO9qs5iu+\nJ2Y0GklLS+OZZ54JWFEidMROmYZtyDBAJnYIEeosFouvvdWVmDZtGo8//jhlZWVMmTKFm2++mdjY\n2ABUeLGruicmxPkkvIRoH3vzivn861NYzUamj84gJsrSLnU0rNSy2+106tSJ/Pz84Aqxf/3rX00u\nghs7dmyrFSSEEKJ5Z4qqeWPVXtweDwCni6r5r7uGtOicdXV1zJo1y/f4Zz/7GTfffLPf7z916hQn\nT54kLS2tRXVcCb9C7M9//jN79uyhW7duABw6dIgbbrjB1+1YQkwIIdrWKUeVL8AACourcWluTEbD\nVZ/TbDZf8nJic5081qxZw/bt28nLy+PXv/51k9vNtDa/Qiw1NZXf/OY39OvXD4Dc3FzefPNN/vu/\n/zugxQkhhLi0tCQbRoOK5vYGWWqirUUB1hS73U55ebkvnMrLy4mJifG9PnXqVB5//HH27NnDgw8+\nyK233kpERERAarmQX1PsDx065Asw8O5507CJmhBCiLaXGBvOz27py4DuiYzsm8J9s24I2GcNHTqU\nlStXAuB2u1m1ahXDhw/3vd5wT6xPnz6MGzeO7OzsgNVyIb9CzGq1NhpifvTRR1it1oAVJYQQonlZ\nnezcNaUXsyd0IyoirMXna7gn1vDPiy++CMAvfvELjh8/zsyZM7nlllvo3LkzM2fOBC5uHHzffffx\nzjvv4HQ6W1yPP/xqAHz48GF+9atf8c033wDQrVs3nnvuOd/OnO0hGJt/BlNNwVYPSE3+Craagq0e\nCN6aRNvz655YVlYWK1asoKqqCuCiDTKFEEKI9uDX5USPx8N7773HK6+8QmRkJPn5+Xz11VeBrk0I\nIYRokl8htmTJEjZv3sy6desACA8P53e/+11ACxNCCCGa41eIbd26lRdeeME3mSM2Npb6+nq/PmDj\nxo1MnjyZiRMn8pe//OWi10tKSrjnnnuYOXMm06ZNY8WKFVdQvhBCiOuZXyFmNptR1W8P9Zy3wK4p\nbrebxYsXs3TpUnJycsjJyeHIkSONjnn77bfp1asXK1eu5K233uLZZ59F07Qr+ApCCCGuV36FWLdu\n3Vi5ciUej4f8/HwWLVrEoEGDmn1fbm4uaWlppKamYjKZmDp1KuvXr290TEJCgm/CSHV1NXa7HaPR\nr/kmQgghrnN+hdiCBQvYtm0bDoeD2bNn43a7efTRR5t9X2FhIR06dPA9TkpKorCwsNEx3/ve9zh8\n+DCjR49mxowZPPbYY1f4FUKfo8yJo6xt1lQIIcTl9OzZs9E6sb/+9a++10pKSujduzfvvvtuo/cs\nX76c6dOnM2PGDKZPn37RQCXQ/BryREZGXtVEjub6bQG8+uqr9OjRg+zsbE6cOMHcuXNZuXJls9P4\ng3FNxtXU9N76Q2zKPQ3AyL4pzJ7QrV3rCTSpyT/BVlOw1QPBWVOoa2orlo8//pgxY8aQk5PDnDlz\nACgoKOC1117jgw8+IDIyEqfTSXFxcVuW7F+I5eTkcOONN2Kz2XjppZfYvXs3Dz30EH369GnyfUlJ\nSZw5c8b3uKCggKSkpEbH7Ny5k/vvvx/Ad+nx6NGj3HBD0y1UgnGh45XW5Chz8tlX+b7Hn32VT+80\nOwn2lndDCdbFoFJT84KtpmCrB4K3prZ2wHGYTSe/xGq0cHPXm7BbowP2WWvWrOHxxx/n4YcfprCw\nkKSkJIqLi4mIiCA8PBzwdndKTU0NWA2X4tflxFdeeQWbzUZubi5ffPEFM2fO5Omnn272fX369OH4\n8ePk5+dTX1/PmjVrmDBhQqNjMjIy2Lx5MwBFRUUcPXqUTp06XcVXEUKI60dBlYPsXe9zqCiPrwv2\n8b87/9Hic17Yduqf//wnAGfOnKG4uJgePXowadIk1qxZA3gvP8bFxTFhwgQWLFjAp59+2uIarpRf\nI7GGiRZffPEFt99+OzNmzOCNN97w630LFy7knnvuwePxcPvtt5OZmem7pjpnzhx+9rOf8dhjjzFj\nxgx0XefRRx9t0zb+7SnBbmVw9wR2HHQAMLh7QquMwoQQ177TFYW49W9nip+tKsLldmEymK76nJfb\nimXNmjVMmjQJgMmTJ/PYY48xd+5cVFXl9ddfJzc3ly1btrBkyRL27t3LL3/5y6uu4Ur5FWKqqvqm\nyL/yyisAuFwuvz5g7NixF+031nA9Fbxrzl599VV/673mTB2RztCe3kusFwaYy+ENN9k5WQhxoU7R\nHTCqRjSPd0lSSlRyiwKsKTk5ORQVFfHRRx8B4HA4OH78OJ07dwa8O5v07duXkSNHsmDBguALsccf\nf5ylS5cye/ZsOnXqxNGjRxk2bFiga7tuXGr0VbJmNZVf7gDANmgwsVOmtXVZQogglhARx08Gfo8t\n+TuxGi18J3N0QD7n6NGj1NTUsHHjRt9zL7/8MqtXr2b27Nk4HA569+4NwP79++nYsWNA6rgcv0Js\n4MCB/PnPf/Y97tKlCwsXLvQ9XrRoEYsWLWr14q5XLofDF2AAlV/uwDZkmIzIhBCNZMR2JiO2c6ud\nr+GeWIMxY8ZgNpuZOHFio+MmTpzIww8/zC233MJzzz3H2bNnMZvNxMXFtXkWtMqq4l27drXGaYQQ\nQrSjffv2+XVc9+7dycnJAeDNN98MZEnN8mt2omhbpoQEbIMG+x7bBg2WUZgQQlyC9HcKUrFTpmEb\n4r3vKAEmhBCXJiEWxCS8hBCiaa1yOdFgMLTGaYQQQogr4leIffHFF1RUVPgeV1RU+LpsALz//vut\nX5kQQgjRDL9C7LnnnsNm+7YvWEREBM8++2zAihLXriJnCUXOkvYuQwhxjfD7cuL5HekNBoPfG2MK\n0eCDfR/z+p5sXt+TzSfHNrR3OUKIy1i3bh09evQgLy8PgPz8fHr06MFLL73kO6Zha5bFixcD3gXQ\nN954I7NmzWL69OmsXbu2TWr1K8QiIiIarQXbtWuXr2uxEP4ocpawNX+n7/Eux24ZkQkRpFavXs1N\nN93kWwsGkJqa2qhrx8cff0zXrl19AxxFUZg7dy4ffvghf/zjHxs1xAgkv2YnPvroo/zyl78kMzMT\ngCNHjvDyyy8HtDAhhBBNq9i3n6LPN2GwWugwbQphMTEtPmd1dTW5ubn87W9/45577mHevHmAd5uV\njIwM9uzZQ58+ffjnP//JzTffzNmzZ33v1XUdgM6dO2MymSgpKSE2NrbFNTXFrxAbMGAAq1ev9o3G\n+vfvf910mhetI94ay7DUAXxxzNtOq3/CDcRbA/uHW4hrmfNMAUffeBPd7Tn3+Aw9fjW/xeddv349\no0ePJiUlhdjYWPbu3Ut0tHefsqlTp5KTk0N8fDwGg4HExMRGIdZgz549qKpKTCuEanP8Xidmt9u5\n6aabAliKuNbd0msyPSJ7AkiACdFCzlOnfAEGUFtwFo/LhWpqWSf7nJwc7r77bsC77crq1av50Y9+\nBMDo0aN56aWXiI+PZ8qUKY3ep+s6y5YtY8WKFeTl5fHyyy83mksRKE2G2PDhwy/7mqIojabZC+EP\nCS8hWkd4WidUoxGP5t2KJTy1Y4sDrKysjK1bt/LNN98A4PF4UFWVO+64AwCTyUTv3r154403WLNm\nDevWrfO9t+Ge2Ny5c9mwYQMvv/wy48ePD3iQNRliy5cvD+iHCyGEuDqWxEQyfnoPxZu3YLBaSZr4\n3Raf85NPPmHmzJk8+eSTvufuvPNOzpw543v8k5/8hKFDhxIVFdXovbqu++6JjR8/nuXLl7N69Wqm\nT5/e4rqa0mSIpaam+nWS22+/XQJPCCHaWGRWJpFZma12vpycHH760582em7ixIn85S9/8T3Oysoi\nKysL8I6+zp+deP6o6xe/+AW/+tWvAh5iit4QnS0wa9asS25pHUgOR2Wbfl5zEhJsQVVTsNUDUpO/\ngq2mYKsHgrcm0fZkKxYhhBAhS0JMCCFEyJIQE0IIEbJaJcT69evXGqe5LrgcDlwOR3uXIYQQ14Qm\nZyf+61//anKO/9ixYwEaTccUl1eyZjWVX3o7VtgGDSZ2yrQrPkdDAMqGmUII0UyIvf76602+uSHE\nRPNK8k5R+OUewgHNo1G6fQu2IcOuKIxaIwSFEOJa0mSIZWdnt1Ud17SvNh/nyN4z1Bt6E6PlE+fM\nBeBY/iYmJMz06xwuh8MXYACVX+644hAUQojmvPLKK+Tk5KCqKqqq8tRTT9G3b180TWP06NHMnj2b\nRx55xHf8p59+yh/+8Ac8Hg+apnHXXXfx/e9/v83q9bt3YmVlJUePHqWurs733JAhQwJS1LWkosxJ\n3kEHitGIEm7BUZVEpBpBcbqJQ9ox+jlLpBWTECIo7Ny5k88++4wPPvgAk8lEWVkZ9fX1AHzxxRf0\n7t2btWvX+kLM5XLxxBNPsHz5cpKSknC5XOTn57dpzX6F2Jo1a3j22WcpLy8nKSmJEydO0KNHDz74\n4INA13dNUaOiqFbr+XpgKjUJje81anWlABjNl+76bEpIwDZocKPLiTIKE+L6diKvmH1fnyHMbGDo\n6C5ERlladL6ioiLsdjumcz0Yz9+tZM2aNfzgBz/gnXfeYefOnQwYMIDq6mrcbrfvOJPJRJcuXVpU\nw5Xya3biK6+8wvvvv096ejqffPIJS5cupU+fPoGu7ZoQZbeS0d0bNkbVQGyW1RdgDduRlBd8TtHR\n5RQdXU55weeXPVfslGmk3P8LUu7/hdwPE+I6V1JUzf+t2sfJYyUcOejg4w/3tPico0aNoqCggEmT\nJvHkk0+yfft2AOrq6tiyZQs33ngjN998s2+zTLvdzvjx4xk3bhyPPPIIq1atohWaQF0Rv0ZiRqOR\n+Ph43G7ygrYYAAAgAElEQVQ34P2izz//vF8fsHHjRn7/+9/j8Xi4/fbbL+rLBbB161aWLFmCpmnE\nxMRcc/fiBo7oTFbPRN/j0tpeRESHEW+NRasrxVm23/eas2w/ETG9mxyRXUhmLApx/Sl2VOHxfBsY\npcU1aJobo9Fw1ecMDw9nxYoV7Nixg61bt/LQQw/xyCOPYLVaGTp0KGFhYXznO9/hj3/8I7/5zW9Q\nFIWnn36aQ4cOsWnTJv73f/+XTZs2sWTJktb4in7xK8TMZjMej4e0tDSys7NJSUnB6XQ2+z63283i\nxYt54403SEpK4vbbb2fChAm+HaIBKioqeOqpp3j99ddJTk6mpOTa3LI+ym7lq83HyTvoDZyM7gnE\nj2j5vTCZsSjE9SkhyYbBoOI+t6dYfGJkiwKsgaqqDB06lKFDh9KtWzc++OADwsLC+PLLLxk/fjwA\n5eXlbN68mZEjRwLQrVs3unXrxsyZM5kwYULwhdh//ud/UlVVxfz581m0aBGVlZX89re/bfZ9ubm5\npKWl+brhT506lfXr1zcKsVWrVjFx4kSSk5MBAr6VdXtpmODRIP/ocbpkhRGT0AGrvadvNGa19/SN\nwpobYcmMRSGuX/bYcCbf0of9u89gNhsZOLxzi8959OhRFEUhPT0dgH379hEbG8u//vUvPvvsM9+9\nshUrVrB69Wr69+/P7t27GTZsGAD79++nY8eOLa7jSvgVYomJiURFRREVFcWbb74JwJEjR5p9X2Fh\nIR06dPA9TkpKIjc3t9Exx48fR9M07rzzTqqrq7nrrruYNWvWlXyHkJPaYT92m4Oas/tR3b2JTh5N\nrcEb7NEJ3p+XjLCEEM1J6WQnpZO9+QP9VFNTw+LFi6msrMRgMJCens748eOpq6vzBRh49wt74YUX\neOyxx3j99df57W9/i8ViITw8nGeeeabV6vGHXyH2yCOPXLTVyvz585udnejPjp6aprFv3z6WLVuG\n0+lkzpw59O/f3/c3gcsJxm0PmqopIcHGDQPLKS9YR0LcCVRVQVVUXNWH+PzrBDburgJgZF+Y3iea\nU1/v9F0acH69E9vk8ViSki44qQ3PjaMo2rQFgPiRw0npleFXPe1FavJPsNUUbPVAcNYU6nr37s27\n77570fMXDizsdjubNm0CaLTXWHtoMsRKSkooLi6mrq6Ow4cP+56vqKigpqam2ZMnJSU12hG0oKCA\npAt+EScnJxMTE4PFYsFisTB48GAOHDjQbIgF415CzdWU2d1MgVqBR1NQUNDqnWieMLbuLcClhQPw\n2Vf59IjQ0DR3o/eWFFdjUi8+v3nsd0ns1R/wXnZsqCFY91uSmpoXbDUFWz0QvDWJttdkiH300Ue8\n9dZbnD17ttGswsjISO69995mT96nTx+OHz9Ofn4+iYmJrFmzhhdffLHRMRMmTGDx4sW43W7q6+vJ\nzc1l7ty5V/l1gp/RaMKtW/G4awFQrV2oqg9vdIwhLv6K1oTJPTAhxPWqyRC7++67ufvuu3nllVf4\n+c9/fuUnNxpZuHAh99xzj2+KfWZmpm+4OmfOHDIzMxkzZgwzZsxAVVVmz57t2/r6WlPjtICnI6py\nHDXMgiUqi5jU7zL47DF2HHSguT1kdTGhmJ3ETpmGbYj3ZmlLQqrI6Z3tKV1BhBDXIkX3c2Xa4cOH\n2bJlC4qiMHz48EYzDNtDMF5KaKqmrzYf59C2w7ira+gYdpqMrnbSZn7bX+wfG75ha8kmtIjThFuM\n3NRlEJPSx7eonr9tX8kux27Au7C6JedrDcF6CUhqalqw1QPBW5Noe3517Pjwww+ZO3cuBw4cYN++\nfdx9992sXLky0LVdMyrKnBzZewZ3dQ2aDkedHcjbuJdP1u4EwFHmZM+pU2gRpwGoqdXYXpDrG0Vd\njbNVRb4AA9jl2N2i8wkhRDDya3bi66+/zooVK0g4d1nL4XDwk5/8hJkz/evALrzqMFGvGMEARdbO\nHMsrYWBZ84vGW0LzeCeIGNWWL4IUQohg49dITFEUX4ABJCQk+DV9XnhF2a10zIhHM5gBMOguSsNT\ncBojAUiwWxmWlY7Z6V0kGG4xMiS5b4vuYyVGxhNhDKfIWUyRs5gIY7jcFxNC+GXdunX06NGDvLw8\nAPLz8+nbty+zZs1i1qxZ3HLLLbhcLlasWMHw4cOZNWsWU6ZM4e23327zWv0aiXXq1Ik//OEPvj1i\n/vGPf9CpU6eAFnat6TOwIyfzSqisqqXWZcCtqGR1jCLBbkWrK2XSwGiGOmdTWldKbJTlqgOn4ZKh\nXlVHtVbjO0+1VkORbPsihPDD6tWruemmm8jJyWHevHkAdO7c+aL1woqiMG3aNB5//HHKysqYMmUK\nN998c5t2XvIrxJ566ikWL17MjBkzABg5ciRPPfVUQAu71kTZrXTtncShfWfRquqoVuFQYRXbtq0i\nLaoA8Lac6pY8+qo/45NjG3z3wfql9ATAqPq9ZZwQIsRUlR6lpPBrDAYziWmjMJmjWnzO6upqcnNz\n+dvf/sY999zjC7HLaZgbaLfb6dSpE/n5+cEXYvfee+9F3TluueUW2U/sCp1BZ7+mUVKvYTAb6RRW\ng9F1BJfbismgXrKDvb8d6oucJY0mcuw9e4iu9ky+KfO2B2vY9kUIcW2oqyki/9BqdN3je5zR784W\nn3f9+vWMHj2alJQUYmNj2bt3L9HR0Zw4ccLXuWPQoEEsXLiw0bYrp06d4uTJk6SlpbW4hivRZIi5\nXC5cLhdut7tR1/qKigq/utiLbznKnOw46KCsTqPG7UGvqaeswtXke06u/Af1u3IxqsYm+ycWOUso\nrS276PkbU0dyY6q3y7QEmBDXltpqhy/AAOqcJXg8GmoLr77k5ORw9913AzB58mRWr17Nj370I9LS\n0i66nAjezTK3b99OXl4ev/71rxttpNkWmvy2r776Kn/6058AGDBggO/5iIiIa7qrRqBobg919W5Q\nAB1Ol5nIr+xASlwF0LiD/fqdK4nc/CkA4aZwuEyH+vMvIcbXqNRoTmqizAxLHSDBJcQ1zBKZjKIY\n0HXvDGRLREKLA6ysrIytW7fyzTffAODxeFBVlTvuuOOSxyuKwtSpU3n88cfZs2cPDz74ILfeeisR\nEREtquNKNPmN582bx7x583jyySf92npFXF6C3coNXeLYXrAPt0enwmYk1lrPAUciwwbdSFyUBaM5\nBpfDQUltGftKDjL03HtrXDWEG60XnfP8S4jddhaQkldGjMWOdcAABk6ZHHSLQYUQrcdsjaFTz1mU\nFe5GNZhJSB3W4nN+8sknzJw5kyeffNL33J133tmoB+75dF33XVLs06cP48aNIzs7m/vvv7/FtfjL\nr9iWAGsd36k9QFbpJqq0KrSeNiI7WVH1MNzVYEwY59t+RfNopHZwczrDTkqe9zKhaVAPlKhL/+cK\nr6jzHWdQDHhy91FbWAhq+CWPF0JcGyKiUomISm218+Xk5DTqkwswceLEy3aqVxSl0XKr++67j9mz\nZ/PjH/8Yq/Xiv3gHgt9tp4JNsI0ymmuD43I4OP3qn9A8Gg5KiRsVQ0WECd2okhwZT5x9ImffzvYe\n7ISK+kr+dZO34/+oWDspVu8fFKu9J9HnzWD85NgGDh3ZwdD/O0q4KZyoMG/rmz6/+RWVlwkxx7kF\n1gn2tvlD1iBYWwVJTU0LtnogeGsSbU/mX7cDo6qgKGAwKOgG73rzmqo9MOjc32jO6EQdsfG9breg\nRJnwnPk/33svnME4KX08g5L646xahyd3H+Dtem9JSqLyEv8nz9nsbTYMMLh7AlNHpAfuiwohRIBJ\niLURU0KCd3uVfTtQahXyK1xERBkxKyYibFnUVR3DEB6Bu6YaOihExPUhvlNXtLpSipo5d7w1FmZ+\nD9fIpqfjN8yQbLDjoIOhPZPafEQmhBCtRUKsDRkG2lE7xWOqNVCOia89Rgyage/ZuuIuz8MdEY4a\nbsGoGLB39XacN5pjsNp74izbDzSewXgh2VdMCHG9kRBrI1pdKc6y/ShGAx7FQGc8nDzXlPdIbhE1\npVYSYs6iG9247Ml0OC+oopNHExHTG+CyAeaPBLuVwd0TGl1OlFGYECKUSYi1sboaD8ZaCx7dQ3eX\ngU6xGp76zyn32Nhx0LtHW1G3AtLTG/c5bCq8rmTjy6kj0hna0zthRAJMCBHqJMTaiNEcg2rJor74\na1QUyivi6WgvI1wNp4paUuJKOV0US02d2a/zNbSj2lC9+4o3vpTwEkJcKyTE2pDFPpyvV1Wi19UB\nED2kGINBwWwx4XF628e4Esvpl9ad6Co3rirHJe9znb+erKaDGwYkA7C9IJcu1h50S0rxuyatrhRo\n2WVKIYRoLxJibaimuAhXcS3VYd7LfkX5RUTbNSIiwrDF9WJCVk8iosNQP93E6S+97b4u7Jnocjh8\nAebW3aTklVHSNZpCVcFR4+Fv3xxiWFY9d8+4odl6ygs+bzRhJLoFHfSFEKI9SIi1oYKz1WhqGCat\nBoCjRzoQm5lKrx6ZvpGQ88wRzu7b4XtP5SV6JlbUV1Lj8p4jsmckgyNc1AFHtWj2AlsPH2NycQZN\n7eXcMNGkwaU66AshRLDza2dn0XJaXSn2ZAv1BjMq57ZOMFiISsn0vV5e8DllxWu9i54zL32e8kgD\nhzuFAWCIMGDIiiDSYkN1RZARXoOetIny+M2sPbq+Tb7XlSpylvgmogghREvJSKwNNFy2swBpN0Rw\nOs+OgoLbZcVZ8AVFNWfQNBcedw0mU4R30XOHajitY+s1+KL7YocGJJOfGUMvq4tYixtFq8ZqVKnC\nhcejEm4xs69kL0MS+112xuKVrD+7GpfaB+2DfR/zxTHvKNPfSShCCNEUCbEAO/+ynauuni6JRaTa\nT+PRVcoqIkGroqrSgKLXYDS6qHPXYQoPJywiEfsdE7F2aDwki7fG0j/hBr7ha2INLjyKCcXtxmJ0\ns79GoVJXSbSaGn0+XHriRmutP7tQw8QT+Pae3jeleXx+YjsK3tZauxy7GZTUX7aLEUK0iIRYG9HK\ny3HWujCHa2iYsITV06lDIaoCoNPQCFrXPbjdTsLj+1wUYA0mpY+nX2QCrsLPABPF5dUA7C+NRjGd\nBWBY6gBM5fsoambiRmuFV0NTYXfZAar3bsGIEc3toXTbNnZ2cLOl/jBFtaVYDRaizdIoVQjROiTE\nAsxojsFkSKSitoh6jOiaArqOOcyFoly8gYCCgqKEYUsYfMnzFTrOoFXswuDKR9VdoLuwmM3sK4zD\nU5LJ6K5mbuxoJS0yguPHc3zvC+TEjfe+yGV3Xgm9U3LpFlmGbSBUn6ihcqeGW1XYdHI3YR1sRJis\nVNfXEGEKZ7CMwoQQrUBCLMDKCz7H6TyFSzHicMTi0kykphSiKDq6DqoKug463g2fFUVBNXw7r/D8\ne0vbtq0iTPsGW1gNHsWMxRKF0aCTnj6ZuMwkxgCGTeupXLaDIzYVRoRjjI5u9e90foeQFQc+4fOK\nr4hK0uhi8+BCRVcUrCkm2FNOgWajusZFVWUdiTHRWA0WvtdtFl1jMlq9LiHE9UdCLIAa7oe5qmvw\neFTi4sr5aldPrNZawsNrQdHxNASYoqKqFoxhkSiKN8TOX9TsuaErxqRSzt1SQtHrKC6vxmI2kxAW\nTYLZ6t2z7Ny9KMWpoB2pwtA3AsVobLWJG58c2+DrENI1Jot9Zd/4XtMVD0a3jqLruA0KLtVAUk0p\n0zfV8E2XavJHpDIkeYAEmBCi1UiIBZiuaVDrBMW7QWV0dCUx9grqXEaMJheKAsc8JupR6GOJAMBs\nS0ev0Kj8codvTZi6twxDcgr1mhWnYsRi0nDrGnvPxhPntJBwqW5VR8A+biKm2NhWCbAiZwm7HLvR\nPG7CK+o5XrYbJcpEuMVImcvJoToPvUzeAK4/UY+xzNuZBC2MjDw3hRGZaHoGpLe4FCGEANpgndjG\njRuZPHlyk1tcA+Tm5tKrVy/Wrl0b6JLajNEcg8WaAW43OlBfb6Rrxgkiwp0oqpsyj4dyj4cjbpWv\ndSuYovC466irPEZVxVfetlLnFjV7qt0cr3Oj46bKo/OV08Xy2lK+dB7g09xVwLd7lgFoHhdq315Y\nO2S26n2w8rpKOm0/xpD/O8KwdUcZc1AnMtwApnq2uTRW1mp8fcyJ+aiKqqrUG824VQMqBsxKBDsO\nOnyTQIQQoqUCGmJut5vFixezdOlScnJyyMnJ4ciRI5c87oUXXmDMmDHo+sWTHUJZZNRATm6NYt+B\nTMLCNKzWOlRVx2z0EKtCmAKDjPXcZqrFU5mH21VOfW0xda4TmAb18J3ndIadr81W1p7twEdVLv5V\nW0e3PZXcvNlBykfr+eyVpQDETpnG4WkDyBkdzf+fcoZPjm1o3e9T6SLjhDeEVLdOx2+KmGwbhIqK\nQVGp1OHfmWbKfnAz8d/5DuHxsZgMKmcSs6ixtv79OSHE9S2glxNzc3NJS0sjNTUVgKlTp7J+/Xoy\nMxtPHc/OzmbSpEns2bMnkOW0G6PHTG2tGYNBw2h0+55XFQWjR8ejgEmvoyG/db2esqoKsr57O990\ntHH0xG7Ohrmo99TijjtJtVZPdLVGVn6d71xhB/dTcOQkxpQIdmjHMEaZQXNftB7rSrZtuRRbWARG\n1Yi12oWpXsNdXUztv79A7+bB7dGJqnKjKPCxdQfDs7IY0fdOjh2vYd9pDROyh5kQonUFNMQKCwvp\n0KGD73FSUhK5ubkXHbN+/XreeustHnvsMZSGBVPXCFNCAqm9Uti334OmXfjjVjAp4OLc7A7fs3Co\ntpp9R7dx0xkDnXZWU1JbxpkMO9t7RaCgoaiAooOuonjC8GdQff6kjKvpmBFvjaVb5mDcnx7H4tS8\nhRpVjEdPkpyWSMfDJWScdGJQVNzWOsJqD3PGup0hQ4YzcNYEYmMjMHg8V/SZQgjRlICGmD+B9Lvf\n/Y758+ejKAq6rvt9OTEhIfgWzF6uJvfs21D/v9UoCrjdKgaD9xe5x+MdjYUrOrqvl4WODrh0A6dO\n5VK5sxJzlAmT0UjK0TKMKTpGm5Hw5HgKktyknXCjqyY8fXpxw/BeAIyqGszW/J0YjQaGpQ6gZ1pn\nzlYVsad0H0ajd+LFntJ9TOw5isTI+Cv6jrd7hrPbuI56U733v6+uY/BAsttCzxN1aB4wGRTsBdXU\nRJkxGlWcX++kx+TxWOIirvInG1ih9GepvQRbPRCcNYm2F9AQS0pK4syZM77HBQUFJCUlNTpm7969\nPPTQQwCUlpayceNGjEYjEyZMaPLcDkdl6xfcAgkJtsvWVHiiFFVXUBUPTmcYYWH1mEwe1HODJwMK\n364U88q0eCjT3HjSQU0xEKPHUHOyBtCxGiz03lVMiqMWs8XIiRSV/f0qqNy+kknp4xmdMIpRaYMp\nLqkm3hqLw1FJsbMaTXM3qqu4pBrF6d8mnA1cxdUohjAMERF4as5NOunWhcS8IiIrXYCCZtQBHYvB\nDB4VzeOmpLialKTQ+u/WXoKtpmCrB4K3JtH2Ajqxo0+fPhw/fpz8/Hzq6+tZs2bNReG0fv16NmzY\nwIYNG5g8eTKLFi1qNsBCTXVVHSlJDqzWOiIjawkL83DpQaru+18P0DW1O6asKAAMioGo7okM7TqU\nDnVhpOSVYTFaqFM0ks5UEV5Rxy7Hbt89r8TI+Eb3vRp6Ljbon3DDVd0Xa5gBaYy2Y4pPwD5uAgNm\nzqVnaRimCBsm1YjFrWLs1AlbuB3w9k+81OaeQgjRUgEdiRmNRhYuXMg999yDx+Ph9ttvJzMzk3ff\nfReAOXPmBPLjg0ZddTEpyWdR1aYulX6baprLzXFnFCNSe4G7CEOE9zKcYjQyOX08xeF9qfr8TQBq\ntVq/65iUPp5BSf2Bq5/YAd4ZkLYhwwBvqDnPHMEYYQQ1lsqwMpzuWrYMj6RXbHduTB0pASaECJiA\nL3YeO3YsY8eObfTc5cJryZIlgS6nXdTValgUD41mb1yGrgOVHtLDK6kuWEOkLQHwTmlv6LqR1CkG\n05DhlG7bhsVgJa+zmZooc7Ojq5bOTDxfQzCVF3yOs3w/hjGRuA5XUL27jtNZMdREmdmhHaNf5Biu\n7K6bEEL4Tzp2tIGkxLNUl3gu2fD3QooCYbFG7LVu3CXFVDprSL3hDoxh0RjNMb6Fwtti+rA/2TtV\nvVufcOZ260BidPplz9vSmYmXcv42M8boaPS+FvakhFMUKVPohRBtQ0IswLS6UvAcxuU2EKYrfgUZ\ngGJSoc4DTidlxaUkp6eTs/kYOw460Nweqp0uoiOjuCHxCKl6AXX5VsqdvS+53UpDu6gGgdrLy2Qy\n0yUtg6ISbz/Fq73vJoQQ/pIQayP6ua71zRzl/V/3twfW5tdi6xaJo8zJjoMO3/M1tRpJUS7Sowu8\nT2gaNUW7A7bdyvnO76x/4e7Q45NH0zel9S5bCiFEUyTEAsxojkFTs9C13WgGN7ruwtjMT93l9lCb\nV0PVN5W4MrqSHB9HSUUpbkM1mscDbiuWMAMRpipUxUOkux5PSTn1QNmpDcRPuq3R+RpmJu5y7Mbl\n1ull73nVAXOpXZsv3B1awksI0VYkxAIsZ/MxDmy1MCAtmsT4UoxGBaOx/hJHfjv6qjGqfNPByjcx\nEYQn2fh37jJUrRo9RaO0xoS7JInRsRYGdy4n0uRBr6kDDTijU31kN9EDb4QL1qxMSh9Pxal4dueV\nsNsdgbn0GFNHpF/Rd3E5HL4AA6j8cge2IcNk9qEQot0EvIv99cxR5uSrvYXEqNXEx5bj8TT14/52\nir1ZgU6RKpXWWg6VHSHNXcyEMI1pUQojbB7iEgronliIs05DUazgAvbqcHFv5Ua17PumDoPbO11f\nuskLIa4FEmJtwHNulGU212Mxu5o93oJCw97O0apCN5PRtwy6uxVsBh2D7kHV3WA0YrBEwLnlYoFc\nWHz+Vi+B/iwhhPCHXE4MoAS7le4Zsezf7qSsIoJOKd62Uc1RFHA4NcrdOlGqd4RmONcxyq1A+l43\n5shSIjLCUKp0bJ2HE57RHeCyoZJgtzK4e4JvcsjVdpO/cKGzaBmtrhQg4JNxQl3DVQPZAUFcSEIs\nwMYNTMVYvZ3YqEoU5XLtpi5WXK+hK1Du0ak+WUOnKDOKruKujWRw6QmUgjrcR6Autpq4uzr4FSgT\nsyIYlGjAEBffol8GEl6to7zg80YzOy+1PELgW1oC3r98Xem9XHFtkxALsBhrLd3jHBjQ/A4wgD7R\nFsJcGjvP1mLaXE6pKZIIu4WItFIYEwlEotd6UCwqpWfXUK8MaPKX4IWzCpkyrYXfTLTE+QvFAZxl\n+9tkeUSouXBpyY6DDob2TJJmu8JH7okFWGVFLVZTFVbLpWYkXpqu69gMKoMsYYyNjsRQH0FMJxXr\nwDCUTCOEG0AFNTkMxaiiGIw4y/b7Lk1d6FKzChvWegkhRCiTEAswj1aBqrqbP7CBDgoKKt4tWrrb\njCg3ZaH1TKQuzIoHFd1sxGNUQAElPByluYVnDazn/mkDRc4SX69GcTGjOQarvafvcUNfTNFYw73c\nBrIzuLiQXE4MtPpDV3QZUa91o5gN3vfo3jcmdChHNXj3G/PoCooCLt2CXlCFIRI0pRxb5+GX/SVo\nSkjAfGMqdZwCwEzHgN7XCkSfxmtRdPLoixaKi4tNHZHO0J7efQglwMSFJMQCSKsrxeUsQEdFwdP8\nGzzg2liEnmbF2CMaFR1FAVOYjqapKIp35mJhYSxVRUaSi0oINyooWpVvduLl6nDHVaJqJgDcxkq0\nutKA/OJsqz6N1woJL/9IeInLkRALME1z4fF4lzI3hNDleFA4acyi8FAkHaLr6Zxc4tv9WVU9OGut\nGI0aSUklpKR4gCjQdDjcTA315Xi0Wji3g7RHc6LVl8svUCFEyJN7YgFkNMfgDkvBoHpQ1aYDDMCl\nGTmo9acuphPFjnhA8TUNVlUIM9VjUDUMhoZRnQJGBSXDTIXmvf/kKHPiKHNytqrId0/KGBaNarCg\n40bX3ei6h+qCrwMyuaO1dpAWQgh/yEgswE4XGEmJ9O9Yk9HNsCFfYzTqGAwauq402g3aYHBfIggV\n3EYNx9Ec9u7O5dPT6VRH7Scs/iw2q8l3Tyo8tg+VZ7d736EpVOXvovL9bURk9iVh9vdb9B0v3Gyz\ntXaQFkKI5kiIBdDqPRuoO1VKyuVvVzWiqjqxMZW4XMZzgdW4u0dDgOl64393uUxomkK0+SSRpki0\nyHyUWnCFGX33pOzxg6ktP4xHq0erKEN3u3GVllD26XqAqw6yy03ikPASQrQFuZwYIEXOEvaVHKSs\nKtKPfcS+pSgQFqZhMHgndZz/3gtHYW43uFxG3G4FVdUA6BF3mul2N9PsbvoY6nzHGs0xqGE2PJ5q\nCAe9RoNK79T/6j27r+rS4qUmcci0eiFEW5KRWADp1no8sQ50FBQ/eiZe6FL30Bqe83hAURTCwjTC\nwrwBVuMMo1NkGZpuRDe46axqREV3Jd4ai1ZXiq45MYbF4KotR7G4wGbA4Lb4v85MCCGCjPz2CpB4\nayxdbT1I6rj1itaJ+cs7a7FxMFot9Vh0FygqYZZwdNXKDakjGh2jqEbC7HG4dAViQak3XlE3+opz\njVij7NZGm22CTOIQQrQ9CbEAGhmdTnXVdnTd/8a/LaEo4HIZMBg9eLQ6IuNv8E2jr3FaUC1ZeGq9\n8/GjuowiPKvpzvcX+mrzcfLO9bHL6J7AwBGdZRKHEKJdSYgFkD3STLXS/GVEj6fxLMSroesKbreC\ns86MUu/GGKnQId6799e34WMjq8cI+gzoeMVrxCrKnL4AA8g76CCrZ6JvRCaEEO1BQiyATtSEoes6\nFtXbMupSPB5vAPmzz9jleDxwKK8jim4gOakIdAOnjqj8u2w3w9I6NwqfA/uqiUmvo3PyVX+cEEIE\nDQmxAFLqzmDyjcQuHWSqCrruR0uqJqgqZKQVcPR4Kl/t6onR7UKr9nAi498crNhORmU/kmx2yusq\nUQXzlocAABriSURBVAxlfHhwGxnHUpiceaPflxKj7FYyuic0upwYJa2AhBDtTEIsgFIiwygqanh0\n+ZtiLb1fpgNGo4eU5LOUlEZS7wyjzhhBXFEaR+NOUmQ7RWS9hY5JeSQlOjBpTjz7j7F/7XZShg0h\nftJtfn3OwBGdyeqZCCABJoQIChJiAaTWhaNrOooxMLM6GhY9K4CierCGO+nTKw/do1BQEM+h4x2J\nP+OhuMNpJqb1wFJTiEdzYa53Y+gegScdqtiD4UgkMZmT/PpMCS8hRDCRxc4BtHL7CVxlLpoahbUm\no0HHbPJuvpmUXITVWoutIub/tXfvwVFX9//Hn5+9ZLO5JyQmIARMwIDcpAR+IpFQwYASUkCUaEWh\nsdI6A6iD1WijOCiooNVaBmTo1NF6mX7BIg7WWkAStaCCYCwQJQRzkdwv5Lr38/tjzUpIgAWyyQbf\njxlm+Oye/exrP/kk7z2fPXsO8boEJgy9mkCjCZ0G+mA9+kA9hhA9mMDSVnTWBTWFEMKf+byI5eXl\nMXPmTNLS0ti0aVOn+7dv305GRgazZ88mMzOTgoICX0fqEdUNbZxobkMfGeCz5+jqMqTB6MRgdLin\nrUKH0RXI/LGpAEQG9SMiQI/epP9x8U3QmfTyVkYI0Wf59M+X0+lk1apVbN68mR07drBjxw6OHz/e\noc2gQYN48803ef/997n//vt54oknfBmpR0XQil7fM70waL+8qAgyWwkOsjJ6RCFGnRGz5SjVx9/B\n3lqO0WBC0xnQdHo0gwFNrycwbKgsyyKE6JN8WsTy8/OJj49n4MCBGI1GZs2axa5duzq0GTduHKGh\noQCMHTuWiooKX0bqUYMifH+JTimw2Qy4XD8t29L+/36Rp+gf9gN11Yd/au9yoGkBoOkwBEQQEpNM\n5MCbfJ5TCCF8wadFrLKykv79+3u2Y2NjqaysPGv7LVu2kJqa6stIPtXY0OaZlsllb2BAeMsFTf57\nsdrXF3M6dO5LhDrl+TcoqgyLxYrD7kJpBpwuFy0tOk5WDqKqKZXIK89ewBzW+g6flZ3++oQQwh/4\ndHSidgFjx/ft28fWrVt5++23vWofExN6sbF84tNdxziaXw7AiDH9mTApmsbirtb/6l7uS4guWttM\nBBgtHceQKAgLa6OuHOz2UziVxsnGOMqbriJQFwvVdpKv1xPZL6jTfsuLdtNQ5Z4TMeKK0Rw/MajD\n60uZNuyi8vrbzw0kkzf8LQ/4ZybR83xaxGJjYykvL/dsV1RUEBsb26ldQUEBOTk5bN68mfDwcK/2\nXV3d1G05L1VjQxtH88txONxLm3zzVRn948NpaQsjILjFp8+t+7EvbQ60oztjFKRyKRw6I6aCBuq0\nZmyame+JpSXMSVSIDYNOT11dMw6Xs8PjHNZ6an445NmuLPmKb//XhsMR1OH1Xehw+5iYUL/6uYFk\n8oa/5QH/zSR6nk8vJ44aNYri4mLKysqw2Wx88MEHTJs2rUObkydPsnTpUtauXcvgwYN9GadH1TVa\nyC1M7JHLiUq5LykqzX1J0aXcxcylBRJyxTiClQFnixN9czMR9jLP42TWDSFEX+fTnpjBYCAnJ4es\nrCxcLhfz588nMTGRd955B4DMzEzWr19PY2MjK1eu9Dxmy5YtvozV7cIizIwY059vvnIXiKHDAzGb\nLUSbG3x+OdE9iEND06CyPIzgUDuaTtFmjaK8eihTb5mAeYKide/HtNpb0fcrI2H8EK4fOeasBcxg\nisQcMYK2hqMAhPQbycCrBsmUU0IIv6Mp1RN9he7nj5cSjh+rwtKwz7PcSXm5najwmvM80nvtM3Sc\neZvLpaOuPoxDnw/BEKpHbw4gNDgah8NJ2pyR2ExtOGtqsXz6GY7vCjHo3GuIRd2Sfs7nax/U0T78\n/vS1xC6Gv14Ckkzn5m95wH8ziZ4n0051oyCzhdaKQs+2ydSMy/XT51aXqnMBcw+lP/xdPN+fvAJn\nsA2rzUSkzkxosLvHtL/2Y47UfYtqtDP1SCVhAe5ftKYD+wmd8P/OOQHwmd8dk96XEMLfSBHzEYdy\noNM5cDggwEeTdmiaQikoMVZwLOHHry5UjcFmNHBbxjVYGnPRqg4yWQ+VgRqt9laCDGYMOvmxCyEu\nDzLhUDdq/ywJwGazoykNp6v7KlhXF36V0ojo58QRYEVZ+9HPHIXLoCPIbMHZ9NPsKLFmRc3wn3pW\noeOTvV6GRQgh/JW8Je9m4XEpBEeOpPDYCWzWPRgMzvM/yEua1sXnYjoYFwonq8fT2BYFekhOiiEm\nIoh6zUCQwUyrw/1ZlvmGFOJnTQCQAiaEuCxIEfMBgymSAQMD2Xc0nwGxNZ6Vmy91pKJLge7Mz8VQ\nmHROrE1WrLomhsZHMmvSEExBoe5eYcNRgo1m9KGJjBuYdmkBcE9sDBAjn48JIfyAFDEfsZnayG+C\npspY4hNquSKu8ZL3qTujJ+aebEqjzW6gObQCS3gdX1k1QgtqWBIz39MrBHdhPXO04YXasfd79v84\nzD45KYZZk4Zc0usRQohLJUXMR+oaLcQOKyJJaYQEaFzyx48K0MBiDUQpHUaDA5tmwGrXkV8ZTnNo\nBfofn+NIw1GqmmvQMHkK1qmKTz3f+zKFDiE0OvmCill1Q5ungAHs/7aaiSNipUcmhOhVUsR8RHM0\nkWTSYdEUwTrtopfFtLbpMZpcaMqFrU2PUzOC5uJ4VRz/a7qKoQPCIMKF3voRAEGBBoxnLP/isNZ7\nCpjT0Uxz9X4spwoJihpNeFzKpbxMIYToVTI60UfCQ0zoNPfhdV3Cfo4fjuWbvfE0/KuB5rd/oLQ+\nGItLERVRz1UR5Rz63sHM0QO4afA1xESaiQgJYKR5NEZr50l9lXLiclg8220NR71e0Tkmwkxy0k+D\nQdyDR6QXJoToXdIT85ErwofwjTGaEHslNuV+t3DmoIzzsVoMVFVEcEVzMY4WB86IEGIjm7C5dKAU\ng8MqCNDbsVV9RbJex/iB11BYFE/l4Wa2HNpPfGIUv5g02DP0v7X+fwDo9IFoF/FdsVmThjBxhHsC\nZylgQgh/ID0xHxozZDHbGzQOtyocjvMf6vbvgSmnwl5r5f+ONTOw5VOsAQaO9L+RE/0moCnlWeJG\np7kYFl2PUe/et62uiJrin9ZrK/q22jNVVHhcCjEJCwiJTkZvDAHAHDHiggd5xESYpYAJIfyG9MR8\nSDO1YTYGMshk5VzfFmsvXi2tgTTUhhD69XfYaxoZMMzM93FB6JsHodc0TjkD+KE6nJi4WnAEUNYY\nzbBA9/xxDS02Wtvs1J6yoGGkXxeFxmCKJHLgTZc8SlEIIfyFFDEfCwkMwKDZCNArz/B4l0ujri6Y\nkBALDpcR5XL3rL45moix9Adoa8Jl1jG0zMrOMQOIUgHo9QobGt+W9WefoQ5bxVUY9QOwqxP8YmAD\nrRY73zfGUU8IJouDMIeLxLPMNi/FSwhxuZAi5kPR5ihGxF2LveK/6A3u4R0Op462tkAOHB1GfP86\n+sfWoCkor4ymxHWK622HcP04urDaNJLI6gQ0lxHQQG+jKbyWWp0dl9VEdBB8U5XIqBExfPy/Yppt\nQRAClkAXC+eNIjLM1IuvXgghfE+KmI9N6z+OH5oLsLc0otc70ICqmkia20IoPBFMcV0woGi1mChP\nOkCh3cTQUis2XQhVIUNw6fQYDXpMej3VA47TYGrA0HAlBl0YRoOO5KQYrr5qCMMr8HyP6xcjY7k6\nMdrvlqoQQojuJkWsB5gMRhyGUKxWKy6Xorw6niCzEYvThbMlmoAAF47+PxAWFsiBUWF8F+/AaIki\nqspIv8AIggMCAbjpurlYNReRp10ObB9kISMHhRA/R1LEfOynme2P4gw0YAy+mtmjrgPACrQ0WogK\nC2Rvw172V1ZhtOtQ/cIIMJpR+iZMTe7vZiUkxTA4Lu6czyXFSwjxcyNFrAecOYfh6doLz4yIGxkf\ney15P+zlWL17Yc1rJsQyKWIMIAtSCiFEV6SI9RBvRgRGm6OYN3QWNW11nm0hhBBnJ0XMD0nxEkII\n78iMHUIIIfosKWJCCCH6LCliQggh+iwpYkIIIfosKWJCCCH6LCliQggh+iwpYkIIIfosKWJCCCH6\nLJ8Xsby8PGbOnElaWhqbNm3qss3TTz9NWloaGRkZHDlyxNeRhBBCXCZ8WsScTierVq1i8+bN7Nix\ngx07dnD8+PEObXJzcykuLuajjz5i1apVrFy50peRhBBCXEZ8WsTy8/OJj49n4MCBGI1GZs2axa5d\nuzq02bVrF3PnzgVg7NixNDY2UlNT48tYQgghLhM+LWKVlZX079/fsx0bG0tlZWWHNlVVVcSdtsRI\nXFwcFRUVvowlhBDiMuHTCYA1TfOqnVLqgh8XExN6UZl8yd8y+VsekEze8rdM/pYH/DOT6Hk+7YnF\nxsZSXl7u2a6oqCA2NrZDmyuuuKJDz6urNkIIIURXfFrERo0aRXFxMWVlZdhsNj744AOmTZvWoc20\nadPYtm0bAIcOHSIsLIzo6GhfxhJCCHGZ8OnlRIPBQE5ODllZWbhcLubPn09iYiLvvPMOAJmZmaSm\nppKbm8tNN92E2WxmzZo1vowkhBDiMqKpMz+QEkIIIfoImbFDCCFEnyVFTAghRJ8lRUwIIUSf1SeK\nWENDA4sXL2bGjBn85je/obGxsVOb8vJyFi5cyKxZs0hPT+f111/v9hz+OA/k+TJt376djIwMZs+e\nTWZmJgUFBb2eqV1+fj7XXHMNH330kV9k+vzzz5kzZw7p6eksXLiwV/PU1dWRlZXFr371K9LT03n3\n3Xd9mic7O5vrr7+e2bNnn7VNT5/b58vUG+e2N8cJevbc/tlTfcBzzz2nNm3apJRS6tVXX1Vr167t\n1KaqqkodOXJEKaVUc3OzSktLU4WFhd2WweFwqOnTp6vS0lJls9lURkZGp/3v2bNH3XvvvUoppQ4d\nOqRuu+22bnv+i8301VdfqcbGRqWUUrm5uX6Rqb3dwoUL1X333ac+/PDDXs906tQpdcstt6jy8nKl\nlFK1tbW9mufPf/6zWrdunSfLxIkTld1u91mmL7/8Uh0+fFilp6d3eX9Pn9veZOrpc9ubTEr17Lkt\nlOoTPbHdu3d75lecO3cuO3fu7NQmJiaGESNGABAcHExiYiJVVVXdlsEf54H0JtO4ceMIDQ31ZPL1\nlF7eZAJ44403mDFjBlFRUT7N422m999/n7S0NM8UaL7M5U2emJgYmpubAWhpaSEiIgKDwXffiElO\nTiYsLOys9/fGHKfny9TT57Y3maBnz23RRy4n1tbWer4AHR0dTW1t7Tnbl5WVcfToUcaMGdNtGfxx\nHkhvMp1uy5YtpKam+iyPt5kqKyvZtWsXd955J+D99GS+zFRcXMypU6dYuHAh8+bN83wBv7fy3H77\n7RQWFpKSkkJGRgaPPfaYz/J4w9/nOO2Jc9sbPX1uCx9/2flCLF68uMt3dg888ECHbU3TznlitLS0\nsGzZMh5//HGCg4O7LZ8v54G8WBey73379rF161befvttn+UB7zI988wzrFixAk3TUEp1Oma9kcnh\ncHDkyBFee+012trayMzM5Nprr2XIkCG9kmfjxo0MHz6cN954g5KSEhYvXsx7771HSEhIt+fxVk+e\n2xeip85tb/T0uS38qIj97W9/O+t9/fr1o7q6mpiYGKqqqs7aTbfb7SxbtoyMjAymT5/erfn8cR5I\nbzIBFBQUkJOTw+bNmwkPD/dZHm8zHT58mAcffBCA+vp68vLyMBgMnaYk68lMcXFxREZGEhgYSGBg\nIMnJyRQUFPikiHmT5+DBg/zud78D8Fx6PHHiBKNHj+72PN7w1zlOe/Lc9kZPn9uij1xOvPHGG/nn\nP/8JwLZt27osUEopHn/8cRITE1m0aFG3Z/DHeSC9yXTy5EmWLl3K2rVrGTx4sM+yXEimXbt2sXv3\nbnbv3s3MmTNZuXKlT3/Jvf3ZHThwAKfTSVtbG/n5+QwdOrTX8iQkJLB3714AampqOHHiBIMGDfJJ\nHm/44xynPX1ue6Onz23hRz2xc7nvvvt44IEH2Lp1K1deeSUvvfQS4L7+nJOTw6ZNmzhw4ADbt28n\nKSmJOXPmAPDQQw8xZcqUbsngj/NAepNp/fr1NDY2elbMNhgMbNmypVcz9TRvMiUmJnLDDTeQkZGB\nTqfjtttu81kR8ybPkiVLeOyxx8jIyEApxcMPP0xERIRP8oD7d+WLL76goaGB1NRUli5disPh8OTp\njTlOz5epp89tbzKJnidzJwohhOiz+sTlRCGEEKIrUsSEEEL0WVLEhBBC9FlSxIQQQvRZUsSEED8b\n3k7gC/Dll18yd+5cRo4cyb///e8O92VlZTFhwgTPd/lE75EiJoT42bj11lvZvHmzV20HDBjAs88+\nS3p6eqf77r33Xp5//vnujicughQxIcTPRlcT+JaUlHDvvfcyb948fv3rX1NUVATAlVdeSVJSEjpd\n5z+TkyZNIigoqEcyi3OTIiYAeOWVV7Db7edt99prr1FXV+fVPhcuXMiePXsuMZkQvpWTk0NOTg7v\nvvsuf/jDH3jqqad6O5K4AH1ixg7he+vXrycrKwuj0XjOdq+//jqTJ0/2epkJf5kkVoiutLS0cOjQ\nIZYvX+65zZs3c8J/SBETnneemZmZ6PV6Nm/ezBNPPEFpaSlKKbKyspgzZw4bNmygqqqKZcuWYTKZ\neOGFF6iqquLll1/GarXicDj4/e9/zy233OL1c+fm5rJx40asVisBAQFkZ2czduxY/vjHPxIcHEx2\ndjY1NTUsWLCA9evXc/XVV7N27Vo+/fRTAFJSUnj44YfR6XQ8+uijBAQEUFxcTGlpKdOmTWPq1Kms\nX7+eiooKFi1axN133w1AUVERa9asob6+Hrvdzj333MO8efPOmnPnzp28/PLL6PV6nE4nOTk5TJw4\nkaqqKp5++mnKy8uxWCykp6ezZMkSAPbv3+85thMnTuTjjz9m06ZNDB06lOHDh3Pw4EHMZjNAh+2v\nv/6aF154wbOe2PLly0lNTaWsrIxbb72VzMxM8vLyaGtr45lnnmH8+PEAfPzxx/zlL3/B4XCg0+l4\n9tlnSUpKOuv+hHvO1dDQ0PMuvdPVmzF5g+YnemctTuFvkpKSVGtrq1JKqeXLl6uXX35ZKeVeMTsl\nJUUdO3ZMKaXUL3/5S8//lXKviOx0OpVSSlVXV6spU6Z4Vtu966671J49e876nMXFxWrBggWqqalJ\nKaXUd999p6ZOnaqUUspisajZs2er//znP+qee+5Rb731llJKqTfffFMtWrRI2e12ZbPZOtz3yCOP\nqDvvvFPZbDbV1tamJk2apLKzs5VSSlVUVKhrr71Wtba2KrvdrubOnauOHz+ulFKqqalJzZgxw7Pd\nlYyMDHXo0CGllFIul8uTedGiRerLL79USilltVrVHXfcoT777DNltVpVSkqK+uKLL5RSSn3wwQcq\nKSnJc+xOP96nb586dUrNmTNHVVVVKaWUqqysVFOmTFFNTU2qtLRUJSUleY7p9u3bVWZmplJKqaKi\nIjV58mRVXFyslFLKZrOp5ubms+6v/Wf0c1RaWtphZeYFCxaof/3rX0op98/26NGjHdo/8sgjXa7Q\nvG/fPrVkyRLfhhXnJT0x0cnevXvJzs4G3CsMp6amsm/fvi4nxK2trSU7O5uSkhL0ej2nTp3ixIkT\nXi1I+sknn1BSUsJdd93luc3pdFJXV0dUVBQvvfQSt956K1OmTOGOO+4A3GtHzZs3z7PK8bx589i5\ncyd33HEHmqYxffp0jEYjRqORq666iqlTpwLu5U/Cw8OpqKjA6XRSVFTEQw895Hleu91OUVERCQkJ\nXWa97rrrWL16NWlpaUyZMoVhw4bR2trKF198QX19vadda2srRUVFREVFERQUxIQJEwC4+eabeeKJ\nJ857TA4ePEhZWRm//e1vPbfpdDqKi4sJDw8nKCjI04saO3Yszz33HAD//e9/SU1NJT4+HsBzDHJz\nc7vcX0lJCSNHjjxvnsvNmRP4Llu2jHXr1rFy5Uo2bNiAw+Fg1qxZDB8+nPz8fJYuXUpjY6Onl/v+\n++8DcOedd3LixAlaW1tJTU1l9erVTJ48uZdf3c+TFDFxXkqps146WblyJdOnT2f9+vUAzJgxA6vV\n6vW+b7jhBs8f4jMVFhYSEhJCTU0NTqcTvV7vyXN6ttMFBAR4/q/T6TCZTB22nU4nSikiIyMvaPXm\n7Oxsjh07xt69e1m+fDmLFy/m5ptvRtM0tm7d6snWrqCgoNM+Tj+Ger0el8sF0OF4KaVISkri73//\ne6fHl5WVdXp97TOotz+2K2fb38/Riy++2OXtXQ27HzNmDLm5uV22f+utt7o1l7h4MjpRABAcHExj\nYyMA119/Pf/4xz8AqK6uJi8vj+uuuw6AkJAQTzuApqYmBgwYAMBnn31GSUmJ18+ZkpLCJ598QmFh\noee2/Px8AEpLS1mzZg1vvvkmgwYN8iy/M2nSJLZt24bD4cBut7Nt27ZzvgPu6g97QkICgYGBvPfe\ne57bjh8/7vnMqCtFRUUMGzaMu+++m4yMDL755htCQkJITk7m1Vdf9bQrLy+npqaGhIQELBYL+/fv\nB+DDDz/scNzi4+M9r7X93T3AuHHj+P777/n88887HZNzmTx5Mnl5eRQXFwNgs9loaWm56P0J0VdI\nT0wAsHjxYu655x7MZrNnYEf7WlYrVqwgMTERcA+bz87OJigoiHXr1rFixQqeeuopXnnlFUaPHs3w\n4cO9fs7Bgwezdu1aHn/8cSwWC3a7nfHjxzNixAgeeughVqxYQXx8PE8++STz589n4sSJLFiwgJKS\nEubOnQu4C+Htt99+1ufoqgep1+vZuHEjq1ev5q9//Ssul4vo6Gj+9Kc/nXU/L774IsXFxej1esLC\nwnjmmWcAWLduHWvWrPHMABEcHMyaNWuIjo7mhRde4KmnnkLTNCZMmOAp9gCPPvooTz75JCEhIZ4e\nHUB4eDgbNmzg+eefZ/Xq1djtduLj49m4cWOXr6d9e8iQIaxatYoHH3zQ02t97rnnGDZsWJf727Bh\ngwxMEJcFWU9MiB5y4403ekYnCiG6h1xOFEII0WdJT0z43JNPPsnXX3/d4baeWEr+QtXW1pKVldXp\n9rS0NO6///5eSCSEOB8pYkIIIfosuZwohBCiz5IiJoQQos+SIiaEEKLPkiImhBCiz/r/TXKZbFof\nV6cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dee55e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot('total_exome_sequence', 'total_lc_sequence', hue='super_population', data=df, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take the same plot as above, by facet our results based upon the genomic sequencing center that produced it to look for inter-center variability in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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13n33Xf3whz+UpPAhWvv27dOgQYOa3bbX64kkelyQrWUSOZuU2PnI1jKJnC1e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\nfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dee5a7510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot('total_exome_sequence', 'total_lc_sequence', hue='super_population', col='Main_Project_E_Centers', col_wrap=2, data=df, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The WUSGC (the Genome Institute at Washington University) shows a small outlier cluster that is distinct relative to the other centers. The BCM (Baylor College of Medicine) facet appears the least variable within the exome sequencing dimension.\n",
    "\n",
    "Are there any super population trends here? We can facet our data a second time, this time by super population to dig deeper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gT0RkmHl8ZQRKq0maHZhdYVKBn+E8brh8AJhd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ZPitnW7LAkK0F9m/lXBG5f/fEfriBS7e7Jla1e2F3uy4HtPZNSIIZl6dGtm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\nRztkAIgZh/Z/IG/+y6os3nDcu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wAY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\nuO+++7jxxhu5/fbbyc7OdgugtbW15OfnM3f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evh8/lCSZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dedf33350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot('total_exome_sequence', 'total_lc_sequence', hue='super_population', col='super_population', row='Main_Project_E_Centers', data=df, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the data now faceted by super population as well as genomic center, the variability of the Broad institute data becomes more apparent. Furthermore, the outlier cluster noted earlier in the WUGSC facet appears to be primarily constituted by the EAS and AMR super populations, with no representation from the SAS super population."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}