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The unifrac.unweighted comand implements the unweighted UniFrac algorithm. The unifrac.weighted command implements the weighted version of the command. Both of these methods are available through the UniFrac website. The UniFrac methods are generic tests that describes whether two or more communities have the same structure. The significance of the test statistic can only indicate the probability that the communities have the same structure by chance. The value does not indicate a level of similarity. The files that we discuss in this tutorial can be obtained by downloading the AbRecovery.zip file and decompressing it.
By default, the unifrac.unweighted() command will carry out the unweighted UniFrac test on each tree in the tree file. This algorithm can compare more than two treatments at a time. Therefore, this test will determine whether any of the groups within the group file have a significantly different structure than the other groups. Execute the command with default settings:
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups)
This will produce:
Tree# Groups UWScore 1 A-B-C 0.703848 It took 0 secs to run unifrac.unweighted.
or with a count file:
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, count=abrecovery.count_table)
If you would like the significance score, you must set random=t.
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups, random=t)
Tree# Groups UWScore UWSig 1 A-B-C 0.703848 <0.001 It took 8 secs to run unifrac.unweighted.
This means that the tree had a score of 0.703848 and that the significance of the score (i.e. p-value) was less than 1 in 1,000. These data are also in the abrecovery.paup.nj.uwsummary file. Looking at the file abrecovery.paup.nj1.unweighted you will see a table with the score of your tree with the different possible pairwise comparisons and the distribution information for the 1,000 randomly labeled trees that were constructed:
A-B-CScore A-B-CRandFreq A-B-CRandCumul 0.456599 0.0010 1.0000 0.462172 0.0010 0.9990 0.462416 0.0010 0.9980 ... 0.500612 0.0010 0.4820 0.500666 0.0010 0.4810 0.500733 0.0010 0.4800 ... 0.564491 0.0010 0.0030 0.566979 0.0010 0.0020 0.569729 0.0010 0.0010
As the output to the screen indicated, this file tells you that if your comparison between A-B-C had a score 0.703848 then there were no randomly-labelled trees with a score greater than or equal to 0.703848; therefore, your p-value would be less than 0.001 (one divided by the number of randomizations). Alternatively, if your comparison between A-B-C had a score of 0.500733, this table would tell you that 1 of the 1,000 random trees had a score of 0.500733 and that 480 of the 1,000 random trees (i.e. P=0.4800) had a score of 0.500733 or larger.
If instead of loading abrecovery.paup.nj you had instead loaded abrecovery.paup.bnj and run unifrac.unweighted():
mothur > unifrac.unweighted(tree=abrecovery.paup.bnj, group=abrecovery.groups)
This will generate the abrecovery.paup.nj.uwsummary file, but it will also generate 1,000 *.unweighted files (one for each tree you supplied) with contents similar to that observed in abrecovery.paup.nj1.unweighted.
Having demonstrated that the community structure for at least one of the three groups in the abrecovery.groups file were significantly different from the other two, you would now like to do pairwise comparisons. Note: You should not do pairwise comparisons if there is not a significant difference at the global level. A conservative method to determine the significance of your pairwise p-values you could divide the overall significance threshold (e.g. typically 0.05) by the number of comparisons that you will carry out. To do all of the possible pairwise comparisons you will set the groups option:
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups, groups=all) Tree# Groups UWScore 1 A-B 0.715765 1 A-C 0.726746 1 B-C 0.75528 It took 0 secs to run unifrac.unweighted.
or you could enter the following to get the same output:
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups, groups=A-B-C)
Alternatively, to only compare two of the three groups you would enter:
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups, groups=A-B) Tree# Groups UWScore 1 A-B 0.715765
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups, groups=A-C) Tree# Groups UWScore 1 A-C 0.726746
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups, groups=B-C) Tree# Groups UWScore 1 B-C 0.75528
If you run the unifrac.weighted() command multiple times, you will notice that while the score for your user tree doesn't change, it's significance may change some. This is because the testing procedure is based on a randomization process that becomes more accurate as you increase the number of randomizations. By default, unifrac.weighted() will do 1,000 randomizations. You can change the number of iterations with the iters option as follows:
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups, iters=10000)
The random parameter allows you to shut off the comparison to random trees. The default is false, meaning do not compare your trees with randomly generated trees.
The distance parameter allows you to create a distance file from the results generated. By default no distance file is created, options are column, lt or square.
mothur > unifrac.unweighted(tree=abrecovery.paup.nj, group=abrecovery.groups, distance=lt)
The name parameter allows you to enter a namesfile with your tree.
mothur > unifrac.unweighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, name=abrecovery.names)
The count file is similar to the name file in that it is used to represent the number of duplicate sequences for a given representative sequence. It can also contain group information.
mothur > unifrac.unweighted(tree=abrecovery.paup.bnj, count=abrecovery.count_table)
The subsample parameter allows you to enter the size pergroup of the sample or you can set subsample=T and mothur will use the size of your smallest group.
The consensus parameter allows you to indicate you would like trees built from distance matrices created with the subsampling results, as well as a consensus tree built from these trees. Default=F.
The root parameter allows you to include the entire root in your calculations. The default is false, meaning stop at the root for this comparison instead of the root of the entire tree.
The processors parameter allows you to specify the number of processors to use. Default processors=Autodetect number of available processors and use all available.
The withreplacement parameter allows you to indicate you want to subsample your data allowing for the same read to be included multiple times. Default=f.
If you are missing a name from your tree or groups file mothur will warn you and return to the mothur prompt. Be sure that you don't have spaces in your sequence or group names.
- 1.28.0 Added count parameter.
- 1.29.0 Added subsampling parameters.
- 1.31.0 Added multiple processors for Windows.
- 1.33.0 Improved work balance load between processors.
- 1.40.0 Rewrite of threaded code. Default processors=Autodetect number of available processors and use all available.
- 1.40.0 Improves randomization.
- 1.40.0 Fixes segfault error for commands that use subsampling. #357 , #347
- 1.41.0 Fixes crashes with subsample option.
- 1.42.0 - Adds withreplacement parameter to sub.sample command. #262