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Last active December 26, 2015 21:09
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An example analysis of 16S rRNA amplicons using QIIME.
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"metadata": {
"name": "qiime-demo"
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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "# Amplicon Sequencing Excercise"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Using this tutorial"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "A lot of bioinformatic analysis runs on the unix-like platforms such as Ubuntu, using a [command line interface](http://linuxcommand.org/learning_the_shell.php). You can [install it on your own machine](http://www.howtogeek.com/99060/how-to-dual-boot-windows-8-and-linux-mint-on-the-same-pc/) and take it for a spin. This is not going to be nescessary for our demonstration but if you are interested in this type of work then it is worth spending some time becoming familiar with this.\n\nWe are going to use command line to call the software commands we want to run, so we need to understand the anatomy of a command. Each operation os run by calling a script and giving it the parameters or data that it needs to make an analysis.\n\n calculate.py -i input.fasta -o output -p 10\n\nThis call tells the computer to use:\n\n 1. the calculate.py script (software) \n 2. with the parameter `i` indicating the input file `input.fasta` \n 3. with parameter `o` indicating the folder where it should write the output. Creatively called `output` \n 4. to set the parameter `p` to 10. This might be a cut-off, eg. the minimum number of times something must occur to be counted.\n\nSome parameters will be required and some optional. Some will have default setting that are given if they are not given in the command.\n\nYou can usually get help by typing:\n\n calculate.py -h"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Making the sequences"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "### 1. Extract total DNA from the sample"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The cells are destroyed and the DNA within them is chemically purified from the other cell components. This so-called **DNA extract** is a mixture of the 10 000+ different genes from each of 1000's of different strains of 10\u2079 different cells."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "### 2. Amplify the a part of the 16S rRNA gene using PCR"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The PCR primers have an A & B adaptor, a barcode and the 16S primer sequences. THe 16S primer sequences anneal to the DNA from the sample and make a product with the adaptor and barcodes flanking the amplified gene.\n\nThis construct is called an **amplicon**. \n\nThe \"target sequence\" region is the is a mixture consisting of the amplified region of the 16S rRNA gene from 1000's of different cells. This is called an **amplicon library**. The 16S rRNA gene sequence from each strain is characteristic and will be used to identify the organisms from which the sequence originated.\n\nWe have 100+ adaptor-barcode-primer sets each identical except for a unique barcode sequence. One of thise unique adaptor-barcode-primer sets is used for each sample, to give the construct below and a known barcode that labels the sample.\n\nThe amplicon libraries from each sample can now be mixed together and sequenced. The barcodes are then used to again separate the sequences frome each sample after sequencing. "
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"collapsed": false,
"input": "Image(filename='img/example_primer_construct.png')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
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4+ODTrTKtykHIHght/vpP3n//6Cb5skfPWZ7QC9Srw1R8fF2I4oybLxRoP7S/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\nHJF9V/QHwQAAAABJRU5ErkJggg==\n",
"prompt_number": 2,
"text": "<IPython.core.display.Image at 0x34cdb50>"
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": "### 3. Sequencing"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The amplicon libraries are sequenced on a next-generation sequencer.\n\nAt AAU Biotechnology deptment we have an Illumina [HiSeq](http://www.illumina.com/systems/hiseq_2000_1000.ilmn) and [MiSeq](http://www.illumina.com/systems/miseq.ilmn). \n\nThe following video explains the technology, although the library preparation method described here is different to the method of preparing amplicons for sequencing... \n\nskip forward to 0:51!"
},
{
"cell_type": "code",
"collapsed": false,
"input": "from IPython.display import YouTubeVideo\nYouTubeVideo('l99aKKHcxC4')",
"language": "python",
"metadata": {},
"outputs": [
{
"html": "\n <iframe\n width=\"400\"\n height=300\"\n src=\"http://www.youtube.com/embed/l99aKKHcxC4\"\n frameborder=\"0\"\n allowfullscreen\n ></iframe>\n ",
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": "<IPython.lib.display.YouTubeVideo at 0x3593c90>"
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Basically, Each piece of DNA in the amplicon library is annealled to its own little part of the chip and then copied thousands of times to give small clusters of identical sequences (so-called \"cluster generation\"). Thus the mixture of amplicons is separated and sequenced individually.\n\n\nThen the DNA is sequenced base-by-base by a reaction producing light. The light from each cluster is detected individually by the machine and this data is converted to a DNA sequence by interpreting the light information in a process called \"base-calling\".\n\nThis process is prone to mistakes and each base is given a **[quality score](http://www.illumina.com/truseq/quality_101/quality_scores.ilmn)** which is the probability of a wrong base-call at that position. We normally require a quality score of >20, which means a probability of 1:100 for a wrong call. The values in the actual sequence are encoded and are not human readable. \n\nThe sequence are in [fastq](http://en.wikipedia.org/wiki/FASTQ_format#Format) format which contains both the sequence and the quality data."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Processing the data"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The basic steps for processing amplicons are as follows:\n\n 1. **Split** the libraries by samples, using the barcode.\n 2. **Assemble** the forward and reverse reads into a *contig*\n 3. **Cluster** the seqs in OTUs at 97% similarity (at \"species\" level)\n 4. **Classify** the sequences by comparison to a reference database\n 5. **Compile** an OTU table of the abundance of each OTU in each sample\n\nThen you have the data to make further analysis and visualisation/plotting.\n\n 1. OTU Heatmap\n 2. Summary plot\n 3. Comparing Alpha diversity: Rarefaction Curves. How diverse are the samples?\n 4. Comparing Beta diversity: Clustering and ordination. How similar/different are the samples?\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "To analyse the amplicon sequencing data we are going to use the [qiime software](http://qiime.org/tutorials/tutorial.html).\n\nData for the analysis\n\n1. DNA sequences from the sequencer\n2. A file of \"metadata\". Data about each sample"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "In addition to the raw sequence data we also need some data about which samples have which barcode and what each sample came from etc.\n\nThe [QIIME mapping file](http://qiime.org/tutorials/tutorial.html#check-mapping-file) contains the data about each sample, including: \n\n - **technical data**, like which primers and barcodes were used to label each sample\n - **sample data**, like location, date and time\n - **experimental data**, like if the sample is a control or a treatment etc. \n\nThis is the mapping file for the demonstration:"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!cat mapping_file.txt",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "#SampleID\tBarcodeSequence\tLinkerPrimerSequence\tplant\tdate\tpair\tsample_type\tDescription\r\nAMPA640\tACTGGAAAA\tGTGCCAGCMGCCGCGGTAA\tAAE\t2013-01-18\tAAE-1\tAS\tAAE-1-AS\r\nAMPA648\tACCTGAAAA\tGTGCCAGCMGCCGCGGTAA\tAAE\t2013-02-07\tAAE-2\tAS\tAAE-2-AS\r\nAMPA609\tACTACAAAA\tGTGCCAGCMGCCGCGGTAA\tAAW\t2012-10-16\tAAW-1\tAS\tAAW-1-AS\r\nAMPA642\tACCATAAAA\tGTGCCAGCMGCCGCGGTAA\tAAW\t2013-01-18\tAAW-2\tAS\tAAW-2-AS\r\nAMPA636\tACTCGAAAA\tGTGCCAGCMGCCGCGGTAA\tHJO\t2013-01-18\tHJO-1\tAS\tHJO-1-AS\r\nAMPA645\tACCTAAAAA\tGTGCCAGCMGCCGCGGTAA\tHJO\t2013-02-07\tHJO-2\tAS\tHJO-2-AS\r\nAMPA639\tACTGCAAAA\tGTGCCAGCMGCCGCGGTAA\tAAE\t2013-01-18\tAAE-1\tWW\tAAE-1-WW\r\nAMPA647\tACCTCAAAA\tGTGCCAGCMGCCGCGGTAA\tAAE\t2013-02-07\tAAE-2\tWW\tAAE-2-WW\r\nAMPA649\tACCCAAAAA\tGTGCCAGCMGCCGCGGTAA\tAAW\t2012-10-16\tAAW-1\tWW\tAAW-1-WW\r\nAMPA641\tACCAAAAAA\tGTGCCAGCMGCCGCGGTAA\tAAW\t2013-01-18\tAAW-2\tWW\tAAW-2-WW\r\nAMPA635\tACTCCAAAA\tGTGCCAGCMGCCGCGGTAA\tHJO\t2013-01-18\tHJO-1\tWW\tHJO-1-WW\r\nAMPA644\tACCAGAAAA\tGTGCCAGCMGCCGCGGTAA\tHJO\t2013-02-07\tHJO-2\tWW\tHJO-2-WW\r\n"
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Processing 1: Preprocessing"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The fastq headers from the raw illumina data have a complex set of data: [Illumina_sequence_identifiers](http://en.wikipedia.org/wiki/FASTQ_format#Illumina_sequence_identifiers).\n\nWhat you need to note is that the letters after the last colon - : - are the barcode and these are used to sort the samples. \n\nThis example file contains two fastq sequences.\n\nCan you see where they start and end?"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!cat 'seq/example.fastq' | cut -c1-100",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "@HWI-ST1040:49:D0HVDACXX:2:1101:6585:2188 1:N:0:ACTGCAAAA\r\nTACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTATATAAGACAGATGTGAAATCCCCGGNNNNNACCTGGGACCTG\r\n+\r\nBCCFFFFFHDFHHIJIJJIIJJIIJIIJIJJJJJJJJGHJJJJJDHFFFFDDDDD=BDDEEEEDDDDDDDDDDDDDD@CDDDD#####++<@DDDDDBDD\r\n@HWI-ST1040:49:D0HVDACXX:1:1101:7838:2218 2:N:0:ACCCAAAAA\r\nTACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTATATAAGACAGATGTGAAATCCCCGGGCNNAACCTGGGACCTG\r\n+\r\n?@@DDDEDHACFHCG@GFHFGGCHHGFDHGBHIII;A;FGFGDG(@EFD8ACBCC;B6@A@DECCCCCCCCDCDDCCCCCBB>>B##++8<@?A?BBBCC\r\n"
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Copy their barcode sequences into the field below, and the sample from which they came."
},
{
"cell_type": "raw",
"metadata": {},
"source": "\n\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Processing 2: Assemble the forward and reverse reads"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The reads are assembled into contigs by finding the overlapping regions and joining them.\n\nReads that are too long, too short, do not align properly or have low quality are removed.\n\nThe statistics of this process on our samples was:"
},
{
"cell_type": "raw",
"metadata": {},
"source": "STAT\tREADS_IN 480000\nSTAT\tNOALIGN\t106\nSTAT\tLOWQUAL\t26553\nSTAT\tSHORT 206\nSTAT\tLONG 161\nSTAT\tPASSED\t 452974"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "The reads are now reformatted for analysis with qiime, including exchanging the Illumina header for the sampleID_num, and some other info about the barcodes for qiime.\n\nWe have screened out the bad quality reads and do not need the quality scores any more. So we convert to fasta format"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!head all_seqs.fasta | cut -c1-100",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": ">AMPA609_1 orig_bc=AACGATGT new_bc=AAAAAAAAA bc_diffs=0\r\nTACGGAGGGGGCTAGCGTTATTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGATCAGAAAGTTGGAGGTGAAATCCCAGGGCTCAACCTTGGAACTG\r\n>AMPA609_2 orig_bc=AACGATGT new_bc=AAAAAAAAA bc_diffs=0\r\nTACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTTGTAAGACGGATGTGAAATCCCCGGGCTTAACCTGGGAACTG\r\n>AMPA609_3 orig_bc=AACGATGT new_bc=AAAAAAAAA bc_diffs=0\r\nTACGGAGGGTGCAAGCGTTATCCGGAATCACTGGGTTTAAAGGGTGCGTAGGCGGTTGTATAAGTCAGTGGTGAAAGGCCGTAGCTTAACTATGGGATTG\r\n>AMPA609_4 orig_bc=AACGATGT new_bc=AAAAAAAAA bc_diffs=0\r\nTACGGGGGGGGCAAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCGCGTAGGCGGCTCTTCAAGTCGGACGTGAAATCCCAAGGCTTAACCTTGGAACTG\r\n>AMPA609_5 orig_bc=AACGATGT new_bc=AAAAAAAAA bc_diffs=0\r\nTACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTCCTTAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAAGGT\r\n"
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Thus labelled we merge all of the sequences to be analysed into one file : `all_seqs.fasta`"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!count_seqs.py -i all_seqs.fasta",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "\r\n120000 : all_seqs.fasta (Sequence lengths (mean +/- std): 252.9610 +/- 0.3405)\r\n120000 : Total\r\n"
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Processing 2: OTU clustering"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!pick_otus.py -i all_seqs.fasta -s 0.97 -o clusters_97\n!tail clusters_97/all_seqs_otus.log",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "output_dir:clusters_97\r\nprefilter_identical_sequences:True\r\npresort_by_abundance:True\r\nsave_uc_files:True\r\nstable_sort:True\r\nstepwords:20\r\nsuppress_sort:True\r\nword_length:12\r\nNum OTUs:1862\r\nResult path: clusters_97/all_seqs_otus.txt"
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Excercise: \n\nI have repeated the analysis with a cluster threshold of 95%, writing the output to a folder called:\n\n clusters_95\n"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!pick_otus.py -i all_seqs.fasta -s 0.95 -o clusters_95\n!tail clusters_95/all_seqs_otus.log",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "output_dir:clusters_95\r\nprefilter_identical_sequences:True\r\npresort_by_abundance:True\r\nsave_uc_files:True\r\nstable_sort:True\r\nstepwords:20\r\nsuppress_sort:True\r\nword_length:12\r\nNum OTUs:1407\r\nResult path: clusters_95/all_seqs_otus.txt"
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": "How many OTUs are present when each level of clustering is used?\n"
},
{
"cell_type": "raw",
"metadata": {},
"source": "97%:\n95%:"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Taxonomic assignment"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!pick_rep_set.py -i clusters_97/all_seqs_otus.txt -f all_seqs.fasta -o clusters_97/represent_seqs.fasta -m most_abundant",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": "!assign_taxonomy.py -i clusters_97/represent_seqs.fasta -o clusters_97/ -m rdp",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": "!make_otu_table.py -i clusters_97/all_seqs_otus.txt -t clusters_97/represent_seqs_tax_assignments.txt -o otu_table.biom",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": "!convert_biom.py -i clusters_97/otu_table.biom -b -o clusters_97/otu_table.txt --header_key taxonomy --output_metadata_id \"ConsensusLineage\"",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Visualization and Analysis"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "There are a lot of low abundance OTUs in the libraries so for the purpose of this excercise we start by removing the OTUs that are not seen at least 250 times. This makes them a bit easier to visualize. "
},
{
"cell_type": "code",
"collapsed": false,
"input": "!sort_otu_table.py -i otu_table.biom -o otu_table_sorted.biom -l sort_order.txt\n\n!filter_otus_from_otu_table.py -i otu_table_sorted.biom -o otu_table_abundant.biom -n 250",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "!make_otu_heatmap_html.py -i otu_table_abundant.biom -o OTU_Heatmap",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": "Image(filename='img/OTU_heatmap.png')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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HbFXU4OxDuZVQgws/Tb8MNSzFirerf2Tf/moOHj27sBQ4Pp7BlW/Q3fmQ3dnpCDa2AG3v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\nqYr4yO/NS0OvLtsYoAC9+s8mAAYgqt/lUAU1rIVeiOpyVh7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5UnnSqEdWeRkvssjN8TR7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S79Cdj80TJOuQY0jh2mzc+lXq6Yx9sJBk11YpEGq1DoeYep8MVabVsDk9SVH23ox3o+tZlrjC3N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"prompt_number": 17,
"text": "<IPython.core.display.Image at 0x371bdd0>"
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": "!summarize_taxa_through_plots.py -i otu_table_abundant.biom -o taxa_summary",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": "!alpha_rarefaction.py -i otu_table.biom -m mapping_file.txt -o alpha",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": "*"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!single_rarefaction.py -i otu_table.biom -o otu_table_even1000.biom -d 1000\n\n!beta_diversity.py -i otu_table_even1000.biom -m bray_curtis -o beta_div\n\n!principal_coordinates.py -i beta_div/bray_curtis_otu_table_even1000.txt -o beta_div/bray_curtis_pcoa.txt\n\n!make_2d_plots.py -i beta_div/bray_curtis_pcoa.txt -m mapping_file.txt -o PCAplot -p beta_params.txt",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": "Image(filename='img/PCA_PC12_sampletype_plot.png')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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x9qPT6ZCVlSUtq9VqqNXqcvdHRMrl7m44469WWxLaLi6Pt0+tVgttmeeL6nS6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\niRMnMGLECOzZs0dvJK1Wq3H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IEydOAPj3apcuXbrA3d0dvr6+eOutt/D+++8DAObMmYOl\nS5dKA4W+fftKtT799FO8+eab0t9ht27d0LBhwwqdipX40FiicvHw8MCZM2fM3QZVUxxBE5VDZU2m\nTwRwBE1EpFgcQRMRKRQDmohIoRjQREQKxYAmIlIoBjQRkUIxoImIFIoBTUSkUAxoIiKFYkATESnU\n/wHyMkBwYB06IAAAAABJRU5ErkJggg==\n",
"prompt_number": 24,
"text": "<IPython.core.display.Image at 0x371b990>"
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Legend: PCA colored by sample type. Activated sludge = red, wastewater = blue."
},
{
"cell_type": "code",
"collapsed": false,
"input": "Image(filename='img/PCA_PC12_plant_plot.png')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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bW9ja2pqsHhHVPBoNoNUC9vbm7qTqGQ3owMBAnD9/HllZWRBCmCSoCwsLpQB2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\n0ERECsWAJiJSKAY0EZFCMaCJiBTq/wGiNlucdXd8YgAAAABJRU5ErkJggg==\n",
"prompt_number": 25,
"text": "<IPython.core.display.Image at 0x371bd50>"
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Legend: PCA colored by plant. AAE = red, AAW = green, HJO = blue"
},
{
"cell_type": "code",
"collapsed": false,
"input": "!upgma_cluster.py -i beta_div/bray_curtis_otu_table_even1000.txt -o beta_div/bray_curtis_cluster.txt",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": "I exported the tree `bray_curtis_cluster.txt`into [Figtree](http://tree.bio.ed.ac.uk/software/figtree/) for the annotations."
},
{
"cell_type": "code",
"collapsed": false,
"input": "Image(filename='img/UPMG.png')",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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AvQwpKoovKoocqa6u1jStpqYmPz/fGElJSXG5XKmpqQ0NDeEyXdc3bdqUnZ1d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\ngHnRD3tlLxExpU1V373MKNC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"prompt_number": 27,
"text": "<IPython.core.display.Image at 0x371bc50>"
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
}
],
"metadata": {}
}
]
}
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