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# Unifrac.weighted

The **unifrac.weighted** comand implements the weighted UniFrac algorithm. The unifrac.unweighted command implements the unweighted 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.

## Contents

## unifrac.weighted()

By default, the unifrac.weighted() command will carry out the weighted UniFrac test on each tree in the tree file. Since this version of the algorithm can only compare two treatments at a time there is no global test as there is for the parsimony and unweighted UniFrac algorithms. 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.weighted(tree=abrecovery.paup.nj, group=abrecovery.groups)

or with a count file:

mothur > unifrac.weighted(tree=abrecovery.paup.nj, count=abrecovery.count_table)

This will produce:

Tree# Groups WScore 1 A-B 0.3660 1 A-C 0.3817 1 B-C 0.4184

If you would like to see the significance scores, run:

mothur > unifrac.weighted(tree=abrecovery.paup.nj, group=abrecovery.groups, random=t)

Tree# Groups WScore WSig 1 A-B 0.366001 <0.001 1 A-C 0.381741 <0.001 1 B-C 0.418381 <0.001 It took 54 secs to run unifrac.weighted.

This means that the tree had scores between 0.3630 and 0.4184 for each of the pairwise comparisons and that the significance of the score (i.e. p-value) was less than 1 in 1,000. If any of the p-values had been greater than 0.01667, then that comparison would not be considered significant. Instead, here all three comparisons are significant. These data are also in the abrecovery.paup.nj.wsummary file. Looking at the file abrecovery.paup.nj1.weighted 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 labelled trees that were constructed:

A-BScore A-BRandFreq A-BRandCumul A-CRandFreq A-CRandCumul B-CRandFreq B-CRandCumul 0.067772 0.000 1.000 0.001 1.000 0.000 1.000 0.071141 0.000 1.000 0.001 0.999 0.000 1.000 0.071259 0.000 1.000 0.001 0.998 0.000 1.000 ... 0.128733 0.000 0.597 0.001 0.404 0.000 0.618 0.128744 0.000 0.597 0.001 0.403 0.000 0.618 0.128749 0.001 0.597 0.000 0.402 0.000 0.618 0.128816 0.000 0.596 0.001 0.402 0.000 0.618 0.128875 0.001 0.596 0.000 0.401 0.000 0.618 ... 0.240668 0.000 0.002 0.000 0.000 0.001 0.001 0.244803 0.001 0.002 0.000 0.000 0.000 0.000 0.265212 0.001 0.001 0.000 0.000 0.000 0.000

As the output to the screen indicated, this file tells you that if your comparison between A and B had a score 0.3630 then there were no randomly-labelled trees with a score greater than or equal to 0.3630; therefore, your p-value would be less than 0.001 (one divided by the number of randomizations). Alternatively, if your comparison between A and B had a score of 0.128749, this table would tell you that 1 of the 1,000 random trees had a score of 0.128749 and that 597 of the 1,000 random trees (i.e. P=0.597) had a score of 0.128749 or larger.

If instead of loading abrecovery.paup.nj you had instead loaded abrecovery.paup.bnj and run unifrac.weighted():

mothur > unifrac.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups)

This will generate the abrecovery.paup.nj.wsummary file, but it will also generate 1,000 *.weighted files (one for each tree you supplied) with contents similar to that observed in abrecovery.paup.nj1.weighted.

### The **groups** option

**groups**

Although the weighted.unifrac() command will do all of the pairwise comparisons for you by default, you can specify those comparisons that you are most interested in. For example,

mothur > unifrac.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, groups=A-B) Tree# Groups WScore WSig 1 A-B 0.366 <0.001

or

mothur > unifrac.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, groups=A-C) Tree# Groups WScore WSig 1 A-C 0.3817 <0.001

or

mothur > unifrac.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, groups=B-C) Tree# Groups WScore WSig 1 B-C 0.4184 <0.001

You should notice that these scores and significance levels are the same as what you would get by running the basic unifrac.weighted() command. For the sake of keeping command options parallel between unifrac.weighted(), unifrac.unweighted(), and parsimony(), these two commands will yield the same output as the default unifrac.weighted():

mothur > unifrac.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, groups=A-B-C) Tree# Groups WScore WSig 1 A-B 0.3660 <0.001 1 A-C 0.3817 <0.001 1 B-C 0.4184 <0.001

or

mothur > unifrac.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, groups=all) Tree# Groups WScore WSig 1 A-B 0.3660 <0.001 1 A-C 0.3817 <0.001 1 B-C 0.4184 <0.001

### The *iters* option

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.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, iters=10000)

### The *random* option

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* option

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.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, distance=lt)

### The *name* option

The name parameter allows you to enter a namesfile with your tree.

mothur > unifrac.weighted(tree=abrecovery.paup.bnj, group=abrecovery.groups, name=abrecovery.names)

### The *count* option

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. For the unifrac.weighted command it must also contain group information.

mothur > make.table(group=abrecovery.groups, name=abrecovery.names) mothur > unifrac.weighted(tree=abrecovery.paup.bnj, count=abrecovery.count_table)

### subsample

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.

### iters

The iters parameter allows you to choose the number of times you would like to run the subsample. Default=1000.

### consensus

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.

### root

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.

### processors

The processors parameter allows you to specify the number of processors to use. Default processors=Autodetect number of available processors and use all available.

## Finer points

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.

## Revisions

- 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