Metagenomics Tutorial (Humann1)
This tutorial is set-up to walk you through the process of determining the taxonomic and functional composition of several metagenomic samples. It covers the use of Metaphlan2 (for taxonomy), HUMAnN (for functional) and STAMP (for visualization and statistical evaluation).
Throughout the tutorial, there are several questions to ensure that you are understanding the process (and not just copy and pasting). You can check the answer sheet after you have answered them yourself.
This lab component will use samples collected and sequenced through the Human Microbiome Project (HMP). We will be analyzing samples collected from three oral sites including subgingival, supragingival, and the tongue. These samples have been downloaded for you already from the HMP-DACC (http://hmpdacc.org/HMIWGS/all/) and have been randomly subsampled to a much smaller number of reads to allow faster computation time. Real data will require more waiting time between each step!
Author: Morgan Langille
Contributions by: Gavin Douglas
First Created: Summer 2015 for CBW Metagenomics
Last Edited: 7 April 2017
- Basic unix skills (This is a good introductory tutorial: http://korflab.ucdavis.edu/bootcamp.html)
- Microbiome Helper Virtual Box (install it and ensure it is working)
- Tutorial Data
- Open this tutorial within the Microbiome Helper Virtual Box. Note that this tutorial will only work with versions 1.1.0 and greater.
- Download the tutorial data, save it to the Desktop (within Ubuntu), unzip the folder, and enter this folder. You can do this using the below commands.
Open a terminal/console and change to the directory containing the tutorial data:
(paste the below command with your mouse, you probably wont be able to copy/paste text in the VBox with keyboard commands)
cd ~/Desktop/
wget https://www.dropbox.com/s/ofulbuie6kuc1w2/hmp_metagenomics_downsampled.zip?dl=1 -O hmp_metagenomics_downsampled.zip
unzip hmp_metagenomics_downsampled.zip
cd hmp_metagenomics_downsampled
The tutorial data consists of a "map file" containing information about each of the samples called hmp_map.txt, along with the sequence data for each sample being contained within a separate fastq file within the directory fastq.
Lets take a look at the hmp_map.txt using less (type q to quit out of the program when done)
less hmp_map.txt
You will have noticed that this map file contains three columns with the first being sample ids, the middle column being the body site, and the third being the sex of the person.
Remember you can count the lines of a file using wc -l. For example,
wc -l hmp_map.txt
Q1) How many samples are there in total (you can either look at the hmp_map.txt file or count the fastq files)?
Q2) How many samples are from each of the different sample sites?
Q3) How many sequences are there in each of the samples? Remember there are 4 lines in a fastq file for each sequence.
We will use Metaphlan2 to determine the taxonomic composition of each sample. As with all other tools, Metaphlan2 has already been installed within the Microbiome Helper Virtual Box.
Similar to other bioinformatic programs you can get the full help for the program by typing the ‘-h’ option after the program name:
metaphlan2.py -h
As you can see there are quite a few options. We won’t explore all of these in this tutorial, but it may be worth reading on your own time.
Find the option which tells you the version of Metaphlan2 you are using.
Q4) What version of Metaphlan2 are you using?
Now we are going to run Metaphlan2 on the sample "SRS015044".
First change back to your working directory:
cd ~/Desktop/hmp_metagenomics_downsampled
Now we are going to run Metaphlan2 on a single example with the following (long) command:
metaphlan2.py --mpa_pkl /usr/local/prg/metaphlan2/db_v20/mpa_v20_m200.pkl --input_type fastq --bowtie2db /usr/local/prg/metaphlan2/db_v20/mpa_v20_m200 --no_map --nproc 2 -o SRS015044.txt fastq/SRS015044.fastq
The command will run for 1-2 minutes on this single sample (it'll be faster if you have more CPUs enabled, see the nproc option described below).
The command line parameters are:
-
--mpa_pkl /usr/local/prg/metaphlan2/db_v20/mpa_v20_m200.pkl: Indicates where the Metaphlan2 marker database is located which contains metadata information about each of the markers -
--input_type fastq: Indicates that our input files are in fastq format. (If we wanted to use fasta files as input then we would change this to 'fasta'). -
--bowtie2db /usr/local/prg/metaphlan2/db_v20/mpa_v20_m200: This indicates the location of the marker database formatted for use with bowtie2 -
--no_mapWe use this flag to prevent Metaphlan2 from storing the intermediate bowtie output files. - '--nproc 2' Indicates that 2 cores should be used to run this program, but will only work if you have enabled at least 2 cores already, otherwise only 1 core will be used.
-
-o SRS015044.txt: Indicates the name of output file that Metaphlan2 will use to write results to. -
fastq/SRS015044.fastq: Metaphlan2 takes the input file containing our metagenomic reads as the last argument
Now run Metaphlan2 for sample SRS015893 as well (swap out the sample names for the above command).
You can inspect the output of these two samples by using the less command (or your favourite editor):
less SRS015044.txt
less SRS015893.txt
Your output should looks something like this:
#SampleID Metaphlan2_Analysis
k__Bacteria 100.0
k__Bacteria|p__Actinobacteria 33.07678
k__Bacteria|p__Firmicutes 24.51694
k__Bacteria|p__Proteobacteria 18.5776
k__Bacteria|p__Bacteroidetes 11.65762
k__Bacteria|p__Candidatus_Saccharibacteria 7.73074
k__Bacteria|p__Fusobacteria 4.44031
k__Bacteria|p__Actinobacteria|c__Actinobacteria 33.07678
k__Bacteria|p__Firmicutes|c__Bacilli 13.6641
k__Bacteria|p__Proteobacteria|c__Betaproteobacteria 12.55122
k__Bacteria|p__Bacteroidetes|c__Flavobacteriia 11.23596
You can see that the output contains two columns, with the first column being the taxonomy name and the second column representing the relative abundance of that taxa (out of 100 total).
This output file provides summaries at each taxonomic level (e.g. phylum, class, family, genus, species, and sometimes strain level). At each taxonomic level the relative abundances will sum to 100.
If reads can only be assigned to a certain taxonomic level (e.g. say the family level), then metaphlan will put those unassigned reads into a lower level taxonomic rank with the name "unclassified" appended.
For example lets pull out all the reads assigned to the genus Veillonella using the grep command:
grep g__Veillonella SRS015044.txt
You should get output like this:
k__Bacteria|p__Firmicutes|c__Negativicutes|o__Selenomonadales|f__Veillonellaceae|g__Veillonella 10.85284
k__Bacteria|p__Firmicutes|c__Negativicutes|o__Selenomonadales|f__Veillonellaceae|g__Veillonella|s__Veillonella_parvula 9.97732
k__Bacteria|p__Firmicutes|c__Negativicutes|o__Selenomonadales|f__Veillonellaceae|g__Veillonella|s__Veillonella_unclassified 0.87552
k__Bacteria|p__Firmicutes|c__Negativicutes|o__Selenomonadales|f__Veillonellaceae|g__Veillonella|s__Veillonella_parvula|t__Veillonella_parvula_unclassified 9.97732
You can see that 10.85% of the metagenome is predicted from organisms in the genus Veillonella. Of those ~9.98% are assigned to the species Veillonella parvula, while ~0.88% are assigned to s__Veillonella_unclassified which means Metaphlan2 doesn't know what species to actually call them.
Q5) What is the relative abundance of organisms unclassified at the species level for the genus Neisseria in sample SRS015044? (Remember to use the grep command)
Q6) What phylum has the highest relative abundance in sample SRS015044? And SRS015893?
Run Metaphlan2 on sample SRS097871 as well, just like for the other 2 samples.
Now, lets combine all of the Metaphlan2 output files into a single merged output file:
/usr/local/prg/metaphlan2/utils/merge_metaphlan_tables.py SRS015044.txt SRS015893.txt SRS097871.txt > metaphlan_merged.txt
Note that this script 'merge_metaphlan_tables.py' takes one or more Metaphlan2 output files as input and combines them into a single output file. The output file is indicated using the stdout redirect symbol '>' and is written in this case to metaphlan_merged.txt
The merged output file should look like this:
ID SRS015044 SRS015893 SRS097871
#SampleID Metaphlan2_Analysis Metaphlan2_Analysis Metaphlan2_Analysis
k__Bacteria 100.0 100.0 100.0
k__Bacteria|p__Actinobacteria 33.07678 7.41456 100.0
k__Bacteria|p__Actinobacteria|c__Actinobacteria 33.07678 7.41456 100.0
k__Bacteria|p__Actinobacteria|c__Actinobacteria|o__Actinomycetales 33.07678 7.41456 100.0
k__Bacteria|p__Actinobacteria|c__Actinobacteria|o__Actinomycetales|f__Actinomycetaceae 1.75917 7.41456 2.12537
k__Bacteria|p__Actinobacteria|c__Actinobacteria|o__Actinomycetales|f__Actinomycetaceae|g__Actinomyces 1.75917 7.41456 2.12537
k__Bacteria|p__Actinobacteria|c__Actinobacteria|o__Actinomycetales|f__Actinomycetaceae|g__Actinomyces|s__Actinomyces_graevenitzii
0.0 7.41456 0.0
Now each sample is listed as a different column within this output file. You can view this file again using 'less' or you can import it into your favourite spreadsheet program.
Q7) What phylum is present in one of the samples but is absent in the others? What sample is this phylum in?
Reminder: to get information about the individual samples you can look in the hmp_map.txt file using 'less':
less hmp_map.txt
Q8) What body site is the sample taken from that contains the unique phylum from question 7?
Running Metaphlan2 on more than a few samples can be tedious. I wrote a script to make this easier as part of the package “Microbiome Helper” (https://github.com/mlangill/microbiome_helper). Note you do not have to do this today as we have already pre-computed the combined Metaphlan2 output, but this is how you would do it.
To run Metaphlan2 on all of the samples using 'run_metaphlan2.pl' (this would take ~30 min):
run_metaphlan2.pl -p 2 -o metaphlan_merged_all.txt ./fastq/*
This command uses the following options:
- '-p 2' runs metaphlan in parallel using 2 processors.
- '-o' is the name for the merged output file.
- ' ./fastq/*' indicates that all of the files within this directory will be used as input.
On your screen you would see the commands that the microbiome helper script is running automatically for you:
cat ./fastq/SRS014477.fastq | /usr/local/metaphlan2/metaphlan2.py --input_type multifastq --mpa_pkl /usr/local/metaphlan2/db_v20/mpa_v20_m200.pkl --bt2_ps sensitive-local --min_alignment_len 50 --bowtie2db /usr/local/metaphlan2/db_v20/mpa_v20_m200 --no_map > ./metaphlan_out/SRS014477
cat ./fastq/SRS011343.fastq | /usr/local/metaphlan2/metaphlan2.py --input_type multifastq --mpa_pkl /usr/local/metaphlan2/db_v20/mpa_v20_m200.pkl --bt2_ps sensitive-local --min_alignment_len 50 --bowtie2db /usr/local/metaphlan2/db_v20/mpa_v20_m200 --no_map > ./metaphlan_out/SRS011343
cat ./fastq/SRS019129.fastq | /usr/local/metaphlan2/metaphlan2.py --input_type multifastq --mpa_pkl /usr/local/metaphlan2/db_v20/mpa_v20_m200.pkl --bt2_ps sensitive-local --min_alignment_len 50 --bowtie2db /usr/local/metaphlan2/db_v20/mpa_v20_m200 --no_map > ./metaphlan_out/SRS019129
Q9) Based on the above example commands being written to the screen, what directory would the Metaphlan2 individual output files be stored in?
STAMP takes two main files as input the profile data which is a table that contains the abundance of features (i.e. taxonomic or functions) and a group metadata file which provides more information about each of the samples in the profile data file.
The metadata file is the hmp_map.txt file. This file is present already in the "hmp_metagenomics_downsampled" directory. You will also need the profile data file which is in a different format than what Metaphlan2 outputs. Therefore we first need to convert it using a script from the microbiome_helper package. You can convert the pre-computed Metaphlan2 output for all samples like this:
metaphlan_to_stamp.pl pre-computed_results/metaphlan2_output/metaphlan_merged_all.txt > metaphlan_merged_all.spf
As you can see this file takes the Metaphlan2 output file (from all the merged samples), which we already made for you, as the only parameter and then writes the output to a new file called "metaphlan_merged_all.spf".
Now load the profile file (metaphlan_merged_all.spf and the metadata file (hmp_map.txt) files by going to "File->load data" within STAMP:
Change the “Profile level” (top left) to “Genus”, ensure that the Group legend (top right) has been set to “sex”, and that “PCA plot” has been set below the large middle window.
You should now be looking at a PCA plot that is coloured by the samples being male or female like this:
As you can see there is no obvious separation in the data points when coloured by sex of the person giving the sample.
Now change the group field (top right) to “body_site” and the PCA will be coloured according to that grouping instead.
Q10) Do you see any separation in the samples when coloured by body site? If so, describe the body sites that separate and which PC axis differentiates these samples.
Now lets test what is significantly different between the groups at the Genus rank.
Under the “Multiple groups” dialog on the left, check that ANOVA is being used as the statistical test, and select “Benjamini-Hochberg FDR” for the multiple test correction. The box at bottom will say how the “Number of active features” using these set of statistics and should indicate that there are 3 organisms at the genus level that are statistically different in their means across the three body sites.
Now lets look at visualizations of these different taxonomic groups.
Change the drop-down box that says "PCA plot" to "Bar plot".
A new window will show a list of all the Genera. You can filter this list to just the 3 Genera passing the statistical test by clicking the "Show only active features" box below the list of Genera.
You can also order the list of Genera by the most significant by clicking on the "corrected p-value" header. Now if you click on the feature "g_Granulicatella" you should see a bar chart representing the relative abundance for each sample for this Genera along with the mean value for the group indicated by a horizontal line. The plot should look like this:
Explore each of the different visualizations by changing "Bar plot" to each of the other options available (e.g. Heatmap plot, Post-hoc plot, and Box plot).
Q11) Create a box plot for the family Streptococcaceae (Change "Profile Level" to "Family" in the top left). Save the box plot as a .png image by using the File->Save plot feature in STAMP.
You can exit STAMP once you're done with this section.
We will now functionally annotate the metagenomes using HUMAnN. HUMAnN analyzes metagenomic data in the context of biochemical pathways, elucidating the distribution of roles among microbial community members.
If you are running this tutorial on our virtual box with low RAM and a low number of CPUs then you should close everything else except for Terminal. Also, if you don't have at least 2GB of memory available for the virtual box (you can change this before running the virtual box, but of course this is limited by the specs of your actual computer) the following commands could be quite slow (especially the "scons" command). If you notice your commands seem to be frozen you can quit them with "CTRL-C" and skip that step, you will be able to analyze the data with STAMP in either case.
If you are short on time you can skip to interpreting the functional results.
Before running HUMAnN, it is first necessary run a similarity search against the KEGG database. This database is a collection of pathways for metabolic and other functional molecular and cellular processes, and is located at “/home/shared/kegg/diamond_db” on the virtual box. This is a reduced KEGG database that the authors of HUMAnN created and which they make available through personal correspondence. This similarity search can be done using various tools such as BLAST, USEARCH, RapSearch, etc. In this tutorial we will use a relatively new similarity search tool that is much faster and works well for large metagenomic datasets called DIAMOND.
First we will make a directory to store our sequence search outputs:
mkdir pre_humann
Now we will run DIAMOND on our metagenomic sequences for the sample SRS015044:
diamond blastx -p 2 -d /home/shared/kegg/diamond_db/kegg.reduced -q ./fastq/SRS015044.fastq -o pre_humann/SRS015044.txt -b 0.4
The options used in this command are:
- 'blastx': Tells DIAMOND to run in “blastx” mode meaning that we will search a nucleotide query against a protein database in all 6 frame translations (3 forward and 3 reverse).
- '-p 2' indicates that DIAMOND should use 2 threads to do the search.
- '-d kegg/kegg.reduced' points at the KEGG database which has already been formatted for use with DIAMOND.
- '-q ./fastq/SRS015044.fastq' is the input metagenomic sample.
- '-o pre_humann/SRS015044.txt' is the name of the output file.
- '-b 0.4' indicates that the sequence block-size in billions of letters should be 5 times lower than the default, which will mean the program will run slower, but use less memory.
- Note: The default e-value cutoff used by DIAMOND is 0.001.
A lot of information is printed to screen while this command is running. You can silence this information by using the "--quiet" option.
The previous command is comparing 60k sequences vs. the KEGG database (~1.3 million sequences) and takes about 10 minutes with 2 CPUs. If we had used the NCBI BLASTX it would have taken a couple of days!
You can see the output using less:
less pre_humann/SRS015044.txt
Each column tells us different information about the match:
- Query
- Subject
- % id
- alignment length
- Mistmatches
- gap openings
- q.start
- q.end
- s.start
- s.end
- e-value
- bit score
Now run DIAMOND for samples SRS015893 and SRS097871 against the KEGG database as above.
Now we are going to run HUMAnN on the three output files. To use HUMAnN you are going to make a symbolic link between the BLAST tabular format files and the HUMAnN "input" directory (HUMAnN reads in input files from this directory).
rm /home/shared/humann-0.99/input/*txt
rm /home/shared/humann-0.99/output/*
ln -s $PWD/pre_humann/*txt /home/shared/humann-0.99/input
(It's ok if the 'rm' commands give the error "No such file or directory", I just added that in to make sure no previous files will mess up HUMAnN).
Then move into the HUMAnN directory:
cd /home/shared/humann-0.99
To being running HUMAnN on all the samples you pre-processed with DIAMOND, you use the “scons” command. Note this will take ~20 minutes.
scons
A bunch of messages will pass on your screen as this command runs. All of the output is contained in the “/home/shared/humann-0.99/output” directory. To see a list of them, once the job is done, type:
ls output
There are MANY output files from HUMAnN. The ones we care about are called:
- 01b-hit-keg-cat.txt –> KEGG KOs
- 04b-hit-keg-mpm-cop-nul-nve-nve.txt -> KEGG Modules
- 04b-hit-keg-mpt-cop-nul-nve-nve.txt -> KEGG Pathways
These files contain relative abundances for each of these different functional classifications. You can look at the format of these using less. For example to look at the KEGG Pathways:
less output/04b-hit-keg-mpt-cop-nul-nve-nve.txt
The output should look like this:
ID NAME SRS015044-hit-keg-mpt-cop-nul-nve-nve SRS015893-hit-keg-mpt-cop-nul-nve-nve SRS097871-hit-keg-mpt-cop-nul-nve-nve
RANDSID RANDSID 763901136 764305738
START START Q1_2009 Q2_2009
GENDER GENDER female male
VISNO VISNO 1 1
STSite STSite Supragingival_plaque Tongue_dorsum
Parent_Specimen Parent_Specimen 700023699 700024548
Run ID Run ID 620DH 61NTL
Lane Lane 7 7
SRS SRS 700023699 700024548
Mean Quality Mean Quality 32.57
Number of Quality Bases Number of Quality Bases 4175865122
Percent of Human Reads Percent of Human Reads 0.0348
Unique Non-Human Bases Unique Non-Human Bases 4359898255
InverseSimpson InverseSimpson 63.1705 59.9415 52.4014
Shannon Shannon 4.29471 4.24922 4.15983
Pielou Pielou 0.878201 0.8689 0.85062
Richness Richness 1 1 1
ko00564 ko00564: Glycerophospholipid metabolism 0.00852428 0.00633934 0.00662779
ko00680 ko00680: Methane metabolism 0.00515712 0.0052314 0.00512276
ko00562 ko00562: Inositol phosphate metabolism 0.00293604 0.00345021 0.0054698
ko00563 ko00563: Glycosylphosphatidylinositol(GPI)-anchor biosynthesis 0.000189354 0 0
ko00561 ko00561: Glycerolipid metabolism 0.00498252 0.00491085 0.00503682
ko00440 ko00440: Phosphonate and phosphinate metabolism 0.00311794 0.00103415 0.00611991
ko00250 ko00250: Alanine, aspartate and glutamate metabolism 0.020349 0.0223569 0.0203559
ko00565 ko00565: Ether lipid metabolism 0.000426284 0.000316276 0
ko00072 ko00072: Synthesis and degradation of ketone bodies 0.00366787 0 0
ko04111 ko04111: Cell cycle - yeast 0 4.32745e-06 0
ko00010 ko00010: Glycolysis / Gluconeogenesis 0.016986 0.0146625 0.0163483
- The first line indicates that the first column is a short id for the KEGG Pathway, the second column is a longer description of the KEGG Pathway, and the remaining columns represent the sample identifiers.
- The next 13 lines contain metadata for each sample.
- The following 4 lines (starting at "InverseSimpson") calculate different alpha-diversity metrics for each sample, but in general these are usually not that useful and can be ignored.
- From line 19 onward, each row indicates the different relative abundance for each KEGG Pathway. Note that these values are small because they have been normalized so that each sample will sum to 1.
- It should be noted that whether metadata rows are included or not depends on your settings.
Now, use less to look at the KO predictions and the KEGG Module predictions:
less output/01b-hit-keg-cat.txt
less output/04b-hit-keg-mpm-cop-nul-nve-nve.txt
Q1) Which of the three samples has the lowest relative abundance of the KEGG Module: “M00319: Manganese/zinc/iron transport system”?
So what about all of our samples? To do the DIAMOND searches for all 30 you could use a wrapper script provided by the microbiome_helper package called run_pre_humann.pl. All 30 samples could be processed with a single command like:
ls fastq/*fastq | parallel --eta -j 2 --load 90% diamond blastx -p 1 -d /home/shared/kegg/diamond_db/kegg.reduced -b 0.4 -q {} -o pre_humann/{/.}.txt --quiet
However, this would take several hours to complete (this is much faster when more CPUs are used). The HUMAnN step would take at least 10 minutes to complete.
To make things easier the output for all 30 samples has been pre-computed and is located in “./pre-computed_results/humann_output”.
If you browse the output using less you can see that they are in the same format but with 30 columns representing the 30 samples (below "~" specifies your home directory):
cd ~/Desktop/hmp_metagenomics_downsampled
less ./pre-computed_results/humann_output/04b-hit-keg-mpt-cop-nul-nve-nve.txt
You will notice that the output looks fairly messy because the terminal will automatically line wrap and it becomes hard to see where one line ends and the next begins. I often find the "cut" command useful to browse data like this. "cut" allows you to just look at particular columns from the data. For example:
cut -f 1-5 ./pre-computed_results/humann_output/04b-hit-keg-mpt-cop-nul-nve-nve.txt | less
- '-f 1-5' indicates that we want to look at the first 5 columns. We could just have easily used '-f 1,3,10' (to view columns 1, 3 and 10) or '-f 1-3,20-' (to view columns 1 to 3 and columns 20 onwards).
- '| less' "pipes" the output from the "cut" command and lets us view the output one screen at a time using our 'less' tool.
Now, we are going to use STAMP to visualize the humann output just like we did with the taxonomic data.
First, we need to convert the HUMAnN output files file into STAMP format:
humann_to_stamp.pl ./pre-computed_results/humann_output/04b-hit-keg-mpt-cop-nul-nve-nve.txt > pathways.spf
humann_to_stamp.pl ./pre-computed_results/humann_output/04b-hit-keg-mpm-cop-nul-nve-nve.txt > modules.spf
humann_to_stamp.pl ./pre-computed_results/humann_output/01b-hit-keg-cat.txt > kos.spf
You should now have the 3 files 'pathways.spf', 'modules.spf', and 'kos.spf' in the current directory. You can check to make sure they are there with 'ls'
ls
Your output should look like this:
mh_user@MicrobiomeHelper:~/Desktop/hmp_metagenomics_downsampled$ ls
fastq kos.spf metaphlan_merged.txt pathways.spf pre_humann SRS015893.txt
hmp_map.txt metaphlan_merged_all.spf modules.spf pre-computed_results SRS015044.txt SRS097871.txt
Load the kos.spf file along with the original hmp_map.txt file into STAMP. (File->Load)
Click on the "Two Groups" tab and choose "Benjamini-Hochberg FDR" for Multiple Test Correction. Look at the "Number of active features" to determine if there are any significant different features.
Q2) Using a two group test with multiple test correction applied are there any significant differences between male and female samples?
Change to a Multiple Group Test using the same multiple test correction and change the Group field (top right) to "body_site".
Q3) Are there any significant differences in KOs across the body sites? If so, how many?
Now load the "pathways.spf" file into STAMP using the same "hmp_map.txt" file.
(Note: STAMP has a bug that will not load a new dataset on top of another, so you need to completely close and restart STAMP first before loading in a different dataset)
Q4) When comparing samples by body site, how many KEGG Pathways are significantly different when using a group test (ANOVA) with Benjamini-Hochberg FDR?
Choose "Box plot" instead of PCA plot, and then expand the list of KEGG Pathways so you can see the corrected p-value. Order the list by corrected p-value by clicking on that column header.
Q5) What is the most significantly different KEGG Pathway? What is the corrected p-value for this KEGG Pathway?
Compare the tongue samples to all other samples using a Two Group test. First select “tongue_dorsum” for Group 1 (on the left hand side) and then select “All other samples” as Group 2. Use the default Welch’s t-test with BH FDR.
Your PCA plot should look like this:
Q6) How many KEGG Pathways are significantly different between the tongue and the plaque samples combined?
Create a "Bar plot" for "ko00230: Purine metabolism".
Q7) Is the relative abundance of “Purine metabolism” higher or lower in the tongue compared to the plaque samples?
Q8) Create an “Extended error bar” plot and save the image as a .png using the File->Save Plot option.