{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# READ ME\n", "\n", "1/13/2017\n", "\n", "Notebooks were made by Matt Olm (mattolm@berkeley.edu) using Jupyter notebook version 4.0.6, and Python version 3.5.0.\n", "\n", "Near the beginning of the notebook is a variable named \"folder\". Pointing that variable to the folder containing the tables downloaded from the SOM should allow all analysis to run without fail through-out all notebooks.\n", "\n", "Notebooks are included in the spirit of openness and in order to provide details on how the analysis was actually done. It is only minimally commented, the code was not optimized in any way, and it follows no stylistic guidelines what-so-ever. Enjoy!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "from matplotlib import pyplot as plt\n", "from matplotlib.backends.backend_pdf import PdfPages\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "from scipy import stats\n", "import scipy\n", "from matplotlib.backends.backend_pdf import PdfPages\n", "from datetime import date\n", "from datetime import datetime\n", "\n", "plt.rcParams['pdf.fonttype'] = 42\n", "%matplotlib inline\n", "\n", "sns.set('notebook')\n", "sns.set(style='whitegrid')\n", "sns.set_palette('deep')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "folder = ''" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load raw polymorphism frequencies\n", "Pdb = pd.read_csv(folder + 'TableS6_PolymorphismsFrequencies.csv')\n", "Pdb['coverage'] = Pdb['reference_base_count'] + Pdb['variant_base_count']\n", "\n", "# Load run date information\n", "Sdb = pd.read_excel(folder + 'TableS3_JGIProjectInformation.xlsx', sheetname= 'JGI_project_info')\n", "p2d = Sdb.set_index('library Name')['Run Date'].to_dict()\n", "Pdb['date'] = Pdb['project'].map(p2d)\n", "Pdb['date'] = pd.to_datetime(Pdb['date'])\n", "\n", "# Load project coverage information\n", "Xdb = pd.read_excel(folder + 'TableS3_JGIProjectInformation.xlsx', sheetname= 'ANG1_mapping_info')\n", "\n", "# Add a column enumerating days since the first project (GOAU)\n", "d0 = date(2012,11,2)\n", "p2d = {}\n", "for d in Pdb['date'].unique():\n", " j = datetime.utcfromtimestamp(d.tolist()/1e9).date()\n", " days = (j-d0).days\n", " p = Pdb['project'][Pdb['date'] == d].tolist()[0]\n", " p2d[p] = days\n", "Pdb['days'] = Pdb['project'].map(p2d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Display the average ANG1 coverage of each project, as well as the SNP coverage\n", "\n", "This gives an idea of the fact that many of these SNPs are called in error due to read mapping from other organisms in the sample" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GOAU 2012-11-02 08:21:02 3.07x snp avg cov 2x snp median cov 3x total median cov 3.25x total avg cov\n", "UAXT 2013-12-10 18:00:08 7.96x snp avg cov 8x snp median cov 8x total median cov 7.91x total avg cov\n", "UCHP 2014-01-15 00:00:21 39.8x snp avg cov 37x snp median cov 42x total median cov 42.8x total avg cov\n", "TGWG 2014-03-02 20:00:41 23.9x snp avg cov 23x snp median cov 23x total median cov 24.6x total avg cov\n", "TGUU 2014-03-16 06:00:16 6.99x snp avg cov 6x snp median cov 7x total median cov 7.15x total avg cov\n", "SNNY 2014-05-24 15:39:01 24.8x snp avg cov 25x snp median cov 25x total median cov 24.9x total avg cov\n" ] } ], "source": [ "Pdb = Pdb.sort('date')\n", "for p in Pdb['project'].unique():\n", " d = Pdb[Pdb['project'] == p]\n", " print(\"{0:5}{1} {2:.3}x snp avg cov{3:5}x snp median cov{4:4}x total median cov{5:7.3}x total avg cov\".format(p,d['date'].tolist()[0],\\\n", " np.average(d['coverage']),int(np.median(d['coverage'])),Xdb['median_coverage'][Xdb['sample'] == p].tolist()[0],\\\n", " Xdb['average_coverage'][Xdb['sample'] == p].tolist()[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Filter polymorphisms based on coverage" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Remove SNPs that dont' meet coverage requirement in all projects, and have at least one project with the freq bounds\n", "def filter_coverage(df, min_cov = 10, projects = None):\n", " to_remove = []\n", " for key in df['location'].unique():\n", " if projects != None:\n", " m = min(list(df['coverage'][(df['location'] == key) & (df['project'].isin(projects))]))\n", " else:\n", " m = min(list(df['coverage'][df['location'] == key]))\n", " if min_cov > m:\n", " to_remove.append(key)\n", " return df[~df.location.isin(to_remove)].copy()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "203 SNPs to start\n", "194 SNPs after coverage filter\n", "4.43% of SNPs don't have high enough coverage for analysis\n" ] } ], "source": [ "Fdb = Pdb.copy()\n", "print(\"{0} SNPs to start\".format(len(Fdb['location'].unique())))\n", "\n", "#Fdb = filter_coverage(Fdb, min_cov = 2, projects = ['GOAU'])\n", "#Fdb = filter_coverage(Fdb, min_cov = 2)\n", "Fdb = filter_coverage(Fdb, min_cov = 1)\n", "print(\"{0} SNPs after coverage filter\".format(len(Fdb['location'].unique())))\n", "\n", "start = len(Pdb['location'].unique())\n", "filtered = len(Fdb['location'].unique())\n", "CORRECTION = (start-filtered)/start\n", "print(\"{0:.2f}% of SNPs don't have high enough coverage for analysis\".format(CORRECTION*100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cluster polymorphisms based on location\n", "\n", "Polymorphisms close to each other is because these are likely the result of horizonal gene transfer, as one would expect random mutations to develop all accross the genome on this short of a time-scale\n", "\n", "Clusteres are stored in a dictionary, with each dictionary key pointing to a list of locations which correspond to that cluster " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Determine how far apart SNPs would be based on a 1% statistical threshold" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SNPs 157bp apart have a 0.50% chance of being that close randomly\n" ] } ], "source": [ "def SNP_bps_by_chance(chance=.05,snps=100,genome_length=6400000):\n", " return chance / ((1/genome_length) * snps)\n", "\n", "chance = .005\n", "SPACE = int(SNP_bps_by_chance(chance=chance,snps=len(Pdb['location'].unique())))\n", "print(\"SNPs {0}bp apart have a {1:.2f}% chance of being that close randomly\".format(SPACE,chance*100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Use this threshold to cluster together SNPs likely involved in HGT" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def cluster_locs(locs,space):\n", " locs.sort()\n", " previous_loc = 0\n", " cluster_c = 0\n", " clusters = {}\n", " for loc in locs:\n", " loc = int(loc)\n", " # In current cluster\n", " if ((abs(loc - previous_loc) < space)):\n", " if cluster_c not in clusters:\n", " clusters[cluster_c] = []\n", " clusters[cluster_c].append(loc)\n", " # Not in current cluster\n", " else:\n", " #print(\"{0} is {2} from {1}\".format(loc,previous_loc,abs(loc - previous_loc)))\n", " cluster_c += 1\n", " clusters[cluster_c] = [loc]\n", " previous_loc = loc\n", " return list(clusters.values())\n", "\n", "location_clusters = cluster_locs(Fdb['location'].unique(),space=SPACE)\n", "location_clusters = sorted(location_clusters, key=len, reverse=True)\n", "\n", "# Add cluster information to dataframe\n", "loc_clust_dic = {}\n", "for i,clust in enumerate(location_clusters):\n", " for loc in clust:\n", " loc_clust_dic[loc] = i + 1\n", "Fdb['location_cluster'] = Fdb['location'].map(loc_clust_dic)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Mark those SNPs likely involved in HGT" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "157 filtered SNPs are likely not involved in HGT\n", "37 filtered SNPs are likely involved in HGT\n" ] } ], "source": [ "Fdb['singleton'] = False\n", "singletons = [x[0] for x in location_clusters if len(x) == 1]\n", "non_singletons = []\n", "for x in location_clusters:\n", " if len(x) > 1: non_singletons += x\n", "Fdb['singleton'][Fdb['location'].isin(singletons)] = True\n", "print(\"{0} filtered SNPs are likely not involved in HGT\".format(len(singletons)))\n", "print(\"{0} filtered SNPs are likely involved in HGT\".format(len(non_singletons)))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "166 un-filtered SNPs are likely not involved in HGT\n", "37 un-filtered SNPs are likely involved in HGT\n" ] } ], "source": [ "location_clusters = cluster_locs(Pdb['location'].unique(),space=SPACE)\n", "singletons = [x[0] for x in location_clusters if len(x) == 1]\n", "non_singletons = []\n", "for x in location_clusters:\n", " if len(x) > 1: non_singletons += x\n", "print(\"{0} un-filtered SNPs are likely not involved in HGT\".format(len(singletons)))\n", "print(\"{0} un-filtered SNPs are likely involved in HGT\".format(len(non_singletons)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize HGT clusters" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def cluster_plot(df,title,minsize=0,top=None,save=False):\n", " d = df.copy()\n", " sns.set(style = 'whitegrid')\n", " if minsize > 0:\n", " to_remove = []\n", " for cohort in d['location_cluster'].unique():\n", " if len(d['location'][d['location_cluster'] == cohort].unique()) < minsize:\n", " to_remove.append(cohort)\n", " d = d[~d.location_cluster.isin(to_remove)]\n", " if top != None:\n", " c2l = {}\n", " for cohort in d['location_cluster'].unique():\n", " c2l[cohort] = len(d['location'][d['location_cluster'] == cohort].unique())\n", " #cohorts = [x for c2l[x] in sorted(c2l, key=c2l.get, reverse=True)]\n", " cohorts = sorted(c2l, key=c2l.get, reverse=True)\n", " cohorts = cohorts[0:top]\n", " d = d[d.cohort.isin(cohorts)]\n", " g = sns.FacetGrid(d, col='location_cluster',col_wrap=2, hue='location', size=2, ylim=(0,1))\n", " #g = g.map(sns.pointplot,'days','percent_variant')\n", " g = g.map(plt.plot,'days','percent_variant').set(xticks=[],yticks=[])#,xlabel='Sequencing Date',ylabel='% Variant')\n", " g = g.set_titles(\"Haplotpye {col_name}\")\n", " g = g.set_axis_labels(\"Sequencing Date\", \"Variant Frequency\")\n", " g = g.fig.subplots_adjust(wspace=.1, hspace=.1)\n", " if save != False:\n", " plt.savefig(save + \".pdf\",format=\"pdf\",transparent=True)\n", " plt.show()\n", " return\n", "\n", "cluster_plot(Fdb,'{0}bp Clusters'.format(SPACE), minsize=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cluster polymorphisms into cohorts\n", "\n", "This is done based on heirarchical clustering, with a threshold chosen based on trial and error and manual inspection of the resulting cohorts\n", "\n", "Cohorts are stored in a dictionary, with each dictionary key pointing to a list of locations which correspond to that cohort" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def cluster_frequency(df,thresh):\n", " X = []\n", " names = []\n", " for col in df.columns:\n", " X.append(df[col].tolist())\n", " names.append(col)\n", " X = np.array(X)\n", " hclust = scipy.cluster.hierarchy.linkage(X)\n", " fclust = scipy.cluster.hierarchy.fcluster(hclust,thresh,criterion='distance')\n", " clusters = {}\n", " for i, c in enumerate(fclust):\n", " if c in clusters:\n", " clusters[c].append(names[i])\n", " else:\n", " clusters[c] = [names[i]]\n", " true_clusters = {}\n", " for cluster in clusters:\n", " #print(clusters[cluster])\n", " locs = []\n", " for g in clusters[cluster]:\n", " locs.append(g)\n", " if len(locs) >= 2:\n", " true_clusters[cluster] = clusters[cluster]\n", " return(list(clusters.values()))\n", "\n", "polys_pivot = Fdb.pivot(\"project\",\"location\",\"percent_variant\")\n", "cohorts = cluster_frequency(polys_pivot,0.375)\n", "cohorts = sorted(cohorts, key=len, reverse=True)\n", "\n", "# Add cohort information to dataframe\n", "cohort_dic = {}\n", "for i,cohort in enumerate(cohorts):\n", " for key in cohort:\n", " cohort_dic[key] = i + 1\n", "Fdb['cohort'] = Fdb['location'].map(cohort_dic)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cohort evolution over the time-series" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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7nqe5ZjV50y5HVqwldBbJY6KLBIDiFjgz+xPZO3QCJynI9oEftOQiFRGQ0VeH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7aNt99+mx9//JGMjAyys7NZtGgRW7duZcGCBXz77bfMmTOH0tLSWsfYunUrzzzzDD///DMFBQWs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\nzTE2JyUoiM2ninAaY1GVuci1lOBnHIGiq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def cohort_plot(df,title,minsize=0,top=None,save=False):\n", " d = df.copy()\n", " sns.set(style = 'whitegrid')\n", " \n", " if minsize > 0:\n", " to_remove = []\n", " for cohort in d['cohort'].unique():\n", " if len(d['location'][d['cohort'] == cohort].unique()) < minsize:\n", " to_remove.append(cohort)\n", " d = d[~d.cohort.isin(to_remove)]\n", " \n", " if top != None:\n", " c2l = {}\n", " for cohort in d['cohort'].unique():\n", " c2l[cohort] = len(d['location'][d['cohort'] == cohort].unique())\n", " #cohorts = [x for c2l[x] in sorted(c2l, key=c2l.get, reverse=True)]\n", " cohorts = sorted(c2l, key=c2l.get, reverse=True)\n", " cohorts = cohorts[0:top]\n", " d = d[d.cohort.isin(cohorts)]\n", "\n", " g = sns.FacetGrid(d, col='cohort',col_wrap=2, hue='location', size=3, ylim=(0,1))\n", " #g = g.map(sns.pointplot,'days','percent_variant')\n", " g = g.map(plt.plot,'days','percent_variant').set(xticks=[],yticks=[])#,xlabel='Sequencing Date',ylabel='% Variant')\n", " g = g.set_titles(\"Cohort {col_name}\")\n", " g = g.set_axis_labels(\"Sequencing Date\", \"Variant Frequency\")\n", " g = g.fig.subplots_adjust(wspace=.1, hspace=.1)\n", " \n", " if save != False:\n", " plt.savefig(save + \".pdf\",format=\"pdf\",transparent=True)\n", " plt.show()\n", " return\n", "\n", "def gen_colors(taxa):\n", " t2c = {}\n", " NUM_COLORS = len(taxa)\n", " cm = plt.get_cmap('gist_rainbow')\n", " for i,tax in enumerate(taxa):\n", " t2c[tax] = cm(1.*i/NUM_COLORS)\n", " return t2c\n", "\n", "def cohort_line_plot(df):\n", " for cohort in sorted(df['cohort'].unique()):\n", " sub_df = df[df['cohort'] == cohort].pivot('date','location','percent_variant')\n", " line_plot(sub_df,title=\"Cohort {0}\".format(cohort))\n", "\n", "def line_plot(sub_piv,title=\"\",save=False):\n", " sns.set_style('white')\n", " \n", " '''\n", " # Generate unique colors\n", " t2color = gen_colors([i for i in sub_piv])\n", " colors = [t2color[i] for i in sub_piv]\n", " '''\n", " sub_piv.plot(kind='line',marker='o',markersize=8)#,colors=colors)\n", " plt.legend(bbox_to_anchor = (1.2, 1.0))\n", " plt.xlabel('DOL')\n", " plt.ylabel('Percent varient')\n", " plt.ylim(0,1)\n", " plt.title(title)\n", " if save != False:\n", " plt.savefig(save + \".pdf\",format=\"pdf\",transparent=True)\n", " plt.show()\n", "\n", "cohort_plot(Fdb,\"Cohorts\",minsize=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cohorts of non-HGT SNPs\n", "\n", "In this case it's all SNPs, because vast majority are involved in HGT" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TbKM6aoYViba2Nl5//XVCoRCNjY0UFxezadMmFi5cOOTA8b5QnHky3Qc0uvfq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\n+K9ewyisreapn9ZgMicy+KrFuJx1ZK19G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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Sdb = Fdb[Fdb['singleton'] == True]\n", "\n", "polys_pivot = Sdb.pivot(\"project\",\"location\",\"percent_variant\")\n", "cohorts = cluster_frequency(polys_pivot,.375)\n", "cohorts = sorted(cohorts, key=len, reverse=True)\n", "\n", "# Add cohort information to dataframe\n", "cohort_dic = {}\n", "for i,cohort in enumerate(cohorts):\n", " for key in cohort:\n", " cohort_dic[key] = i + 1\n", "Sdb['cohort'] = Sdb['location'].map(cohort_dic)\n", "\n", "cohort_plot(Sdb,\"Singleton cohorts\",minsize=2)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Evolutionary Analysis\n", "\n", "Figure out the number of variants that are absent in the first sample and present in the last sample.\n", "\n", "This looks for \"evolving\" SNPs. These are SNPs which change from dominant in the popluation to not dominant between samples. Most measures of evolutionary rates are based on isolate genomes, in which a single clonal cell is sequenced from the population. My analysis is based on the fact that the dominant variant is the most likely to be selected if an isolate was retreived from the population, and thus a position which changes dominance between the two time-points would most likely be picked up as an evolving position.\n", "\n", "At the final step I filter out all positions that are likely involved in horizontal gene transfer (due to their close proximity on the genome)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Number of mutations between SPH1 and ANG1" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "166 non-HGT mutations seperate ANG1 and SPH1\n" ] } ], "source": [ "SPH1_clusters = cluster_locs(Pdb['location'].unique(),space=SPACE)\n", "SPH1_singletons = [x[0] for x in SPH1_clusters if len(x) == 1]\n", "print(\"{0} non-HGT mutations seperate ANG1 and SPH1\".format(len(SPH1_singletons)))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def locate_evolving_SNPs(db,start_p = 'GOAU',end_p= 'SNNY'):\n", " evolving = []\n", " \n", " days = db['days'][db['project'] == end_p].tolist()[0]\\\n", " -db['days'][db['project'] == start_p].tolist()[0]\n", " \n", " for location in db['location'].unique():\n", " d = db[db['location'] == location]\n", " start_frac = d['percent_variant'][d['project'] == start_p].tolist()[0]\n", " end_frac = d['percent_variant'][d['project'] == end_p].tolist()[0]\n", "\n", " # This is equivilent to XOR\n", " # This is saying that if if both fractions aren't on the same side of the 50% mark:\n", " if (start_frac >= 0.5) & (end_frac <= 0.1):\n", " evolving.append(location)\n", " return evolving, days\n", "\n", "def contextualize_snps(evolving_snps,days,SPH1_snps,title='evolving snps'):\n", " print(\"{0:.2f} {3}, {1:.3} SNPs/year, {2:.2f} year separation\"\\\n", " .format(evolving_snps, (evolving_snps/days) * 365\\\n", " , SPH1_snps/((evolving_snps/days) * 365),title))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "568 days seperate these projects (1.5561643835616439 years)\n", "15.00 non-HGT snps, 9.64 SNPs/year, 17.22 year separation\n", "15.67 non-HGT snps (corrected with factor of 1.04), 10.1 SNPs/year, 16.49 year separation\n" ] } ], "source": [ "evolving, days = locate_evolving_SNPs(Sdb)\n", "print(\"{0} days seperate these projects ({1} years)\".format(days,days/365))\n", "contextualize_snps(len(evolving),days,len(SPH1_singletons),'non-HGT snps')\n", "contextualize_snps(len(evolving)*(CORRECTION + 1),days,len(SPH1_singletons),'non-HGT snps (corrected with factor of {0:.2f})'.format(CORRECTION + 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot evolving SNPs" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cohort_plot(df,title,minsize=0,top=None,save=False):\n", " d = df.copy()\n", " sns.set(style = 'whitegrid')\n", " \n", " if minsize > 0:\n", " to_remove = []\n", " for cohort in d['cohort'].unique():\n", " if len(d['location'][d['cohort'] == cohort].unique()) < minsize:\n", " to_remove.append(cohort)\n", " d = d[~d.cohort.isin(to_remove)]\n", " \n", " if top != None:\n", " c2l = {}\n", " for cohort in d['cohort'].unique():\n", " c2l[cohort] = len(d['location'][d['cohort'] == cohort].unique())\n", " #cohorts = [x for c2l[x] in sorted(c2l, key=c2l.get, reverse=True)]\n", " cohorts = sorted(c2l, key=c2l.get, reverse=True)\n", " cohorts = cohorts[0:top]\n", " d = d[d.cohort.isin(cohorts)]\n", "\n", " g = sns.FacetGrid(d, col='cohort',col_wrap=2, hue='location', size=3, ylim=(0,1))\n", " #g = g.map(sns.pointplot,'days','percent_variant')\n", " g = g.map(plt.plot,'days','percent_variant').set(yticks=[])#,xlabel='Sequencing Date',ylabel='% Variant')\n", " g = g.set_titles(\"Cohort {col_name}\")\n", " g = g.set_axis_labels(\"Sequencing Date\", \"Variant Frequency\")\n", " g = g.fig.subplots_adjust(wspace=.1, hspace=.1)\n", " \n", " if save != False:\n", " plt.savefig(save + \".pdf\",format=\"pdf\",transparent=True)\n", " plt.show()\n", " return" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5gaWjf0+66AeSUnqKxYmf15KVCgpOqhSR5ORkPvroI2644QZ+/PFHnnjiCfbs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\nrerIkSPVBx54QI2Pj3f6a5uTk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Edb = Sdb[Sdb['location'].isin(evolving)]\n", "\n", "polys_pivot = Edb.pivot(\"project\",\"location\",\"percent_variant\")\n", "cohorts = cluster_frequency(polys_pivot,.5)\n", "cohorts = sorted(cohorts, key=len, reverse=True)\n", "\n", "# Add cohort information to dataframe\n", "cohort_dic = {}\n", "for i,cohort in enumerate(cohorts):\n", " for key in cohort:\n", " cohort_dic[key] = i + 1\n", "Edb['cohort'] = Edb['location'].map(cohort_dic)\n", "\n", "cohort_plot(Edb,\"Evolving SNPs\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }