cluster.classic

The cluster.classic command can be used to assign sequences to OTUs. It is the dotur implementation of cluster. Presently, mothur implements three clustering methods:

• Nearest_neighbor: Each of the sequences within an OTU are at most X% distant from the most similar sequence in the OTU.
• Furthest_neighbor: All of the sequences within an OTU are at most X% distant from all of the other sequences within the OTU.
• Average_neighbor: This method is a middle ground between the other two algorithms.
• Weighted_neighbor: Kind of like average neighbor but OTUs are weighted by the number of sequences in the cluster

If there is an algorithm that you would like to see implemented, please consider either contributing to the mothur project or contacting the developers and we’ll see what we can do. The furthest neighbor algorithm is the default option. For this tutorial you should download the amazondata.zip file and decompress it.

Default settings

By default cluster.classic executes the furthest neighbor clustering algorithm. In order for the cluster.classic command to work, a distance matrix needs to be provided.

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist)


This command will generate the following output:

unique 2   94  2
0.00   2   92  3
0.01   2   88  5
0.02   4   84  2   2   1
0.03   4   75  6   1   2
0.04   4   69  9   1   2
0.05   4   55  13  3   2
0.06   4   48  14  2   4
0.07   4   44  16  2   4
0.08   7   36  15  4   2   1   0   1
0.09   7   36  12  4   3   0   0   2
0.10   7   35  12  2   3   0   0   3
0.11   14  30  9   3   5   0   0   1   0   0   0   0   0   0   1
...


Outputted to the screen is a label describing the distance cutoff used to form OTUs, the number of sequences in the largest OTU, the number of OTUs with only one sequence, with two, etc. Running the cluster.classic() command generates three output files whose names end in sabund, rabund, and list. The data outputted to the screen is the same as that in the sabund file. You will notice that the sample rabund, sabund, and list files each have a “.fn.” tag inserted after the name of the distance matrix. fn corresponds to the algorithm that was used to perform the clustering. In this case furthest neighbor (fn) was used. Other possibilities include “an” for average neighbor and “nn” for nearest neighbor.

Options

name

A name file contains two columns. The first column contains the name of a reference sequence that is in a distance matrix and the second column contains the names of the sequences (separated by commas) that the reference sequence represents. The list of names in the second column should always contain at least the reference sequence name.

There are several reasons to be interested in providing a name file with your distance matrix. First, as sequencing collections increase in size, the number of duplicate sequences is increasing. This is especially the case with sequences generated via pyrosequencing. Sogin and colleagues 1 found that less than 50% of their sequences were unique. Because the alignments and distances for the duplicate sequences are the same, re-processing each duplicate sequence takes a considerable amount of computing time and memory.

Example from amazon.names:

...
U68616 U68616
U68617 U68617
U68618 U68618,U68620
U68619 U68619
U68621 U68621
...


Second, if you pre-screen a clone library using ARDRA then you may only have a sequence for a handful of clones, but you know the number of times that you have seen a sequence like it. In such a case the second column of the name file would contain the sequence name as well as dummy sequence names

...
AA1234 AA1234,AA1234.1,AA1234.2
AA1235 AA1235
AA1236 AA1236,AA1236.1
AA1237 AA1237,AA1237.1,AA1237.2,AA1237.3
AA1238 AA1238,AA1238.1
...


A name file is not required, but depending on the data set to be analyzed, could significantly accelerate the processing time of downstream calculations. Although this is a simple example, the 98 sequence amazon data set has two pairs of duplicate sequences (U68618 and U68620) and (U68667 and U68641). The distance matrix in the file 96_lt_phylip_amazon.dist is a lower triangle matrix for the 96 unique sequences. While you could just read the matrix in and analyze the set of 96 unqiue sequences, this would give a considerably different analysis than if you used the entire 98 sequence data set. Considering the frequency of sequences is critical for pretty much every analysis in mothur, we want to use the name file to artificially inflate the matrix to its full size. In this case we use the namefile option:

mothur > cluster.classic(phylip=96_lt_phylip_amazon.dist, name=amazon.names)


mothur remembers that the distances for the reference sequence also apply to all of the sequences listed in the second column. Using a name file can considerably accelerate the amount of processing time required to analyze some data sets.

count

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. mothur will use this information to form the correct OTU’s. Unlike, when you use a name file the list file generated will contain only the unique names, so be sure to include the count file in downstream analysis with the list file.

mothur > make.table(name=amazon.names)

Example from amazon.count_table:
...
U68616    1
U68617    1
U68618    2
U68619    1
U68621    1
...

mothur > cluster.classic(phylip=96_lt_phylip_amazon.dist, count=amazon.count_table)


method

By default cluster.classic uses the furthest neighbor algorithm; this can be changed with the method option. By running the following command you will get the same output as just running cluster():

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist, method=furthest)


To obtain a nearest neighbor clustering of the data use the method option to produce the subsequent output:

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist, method=nearest)
********************#****#****#****#****#****#****#****#****#****#****#
***********************************************************************
unique 2   94  2
0.00   2   92  3
0.01   4   86  4   0   1
0.02   4   83  2   1   2
0.03   4   75  6   1   2
0.04   4   68  8   2   2
0.05   5   53  13  2   2   1
0.06   13  47  12  2   2   0   0   0   0   0   0   0   0   1
0.07   16  41  10  2   2   0   0   1   0   0   0   0   0   0   0   0   1
...


To obtain an average neighbor clustering of the data again use the method option to produce the subsequent output:

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist, method=average)
********************#****#****#****#****#****#****#****#****#****#****#
***********************************************************************
unique 2   94  2
0.00   2   92  3
0.01   3   87  4   1
0.02   4   83  2   1   2
0.03   4   75  6   1   2
0.04   4   69  9   1   2
0.05   4   55  13  3   2
0.06   4   48  14  2   4
0.07   7   42  15  2   2   1   0   1
...


To obtain an weighted neighbor clustering of the data again use the method option to produce the subsequent output:

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist, method=weighted)
********************#****#****#****#****#****#****#****#****#****#****#
***********************************************************************
unique 2   94  2
0.00   2   92  3
0.01   3   87  4   1
0.02   4   83  2   1   2
0.03   4   75  6   1   2
0.04   4   69  9   1   2
0.05   4   55  13  3   2
0.06   7   48  14  1   3   0   0   1
0.07   7   43  15  2   3   0   0   1
...


cutoff

You can set a cutoff value for performing the clustering operation. The cutoff can be set for the cluster command as follows:

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist, cutoff=0.05)
********************#****#****#****#****#****#****#****#****#****#****#
***********************************************************************
unique 2   94  2
0.00   2   92  3
0.01   2   88  5
0.02   4   84  2   2   1
0.03   4   75  6   1   2
0.04   4   69  9   1   2
0.05   4   55  13  3   2


precision

Perhaps the most commonly asked question is why the cluster.classic command produces data for both the “unique” and “0.00” lines. Aren’t they the same? No. The “unique” line represents data for the situation where all of the sequences in an OTU are identical; the “0.00” line represents data for the situation where all of the sequences in an OTU have pairwise distances less than 0.0049. We made the decision that because there is error in everything, we should round these distances as well and not apply a hard cutoff at 0.01, 0.02, etc. If you want greater precision, there is a precision option in the cluster.classic command:

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist, cutoff=0.02, precision=1000)
********************#****#****#****#****#****#****#****#****#****#****#
***********************************************************************
unique 2   94  2
0.003  2   92  3
0.006  2   90  4
0.008  2   88  5
0.017  3   87  4   1
0.018  3   86  3   2
0.020  4   84  2   2   1


Remember that the 16S rRNA gene is roughly 1,500 bp long. So it would seem silly to have a precision greater than 1,000. Just because you can calculate a number to 20 digits, doesn’t mean they’re all significant.

Finer points

Missing distances

Perhaps the second most commonly asked question is why there isn’t a line for distance 0.XX. If you notice the previous example the distances jump from 0.003 to 0.006. Where are 0.004 and 0.005? mothur only outputs data if the clustering has been updated for a distance. So if you don’t have data at your favorite distance, that means that nothing changed between the previous distance and the next one. Therefore if you want OTU data for a distance of 0.005 in this case, you would use the data from 0.003.

Variability

You may notice that if you run the same command multiple times for the same dataset you might get slightly different out for some distances:

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist)
********************#****#****#****#****#****#****#****#****#****#****#
***********************************************************************
unique 2   94  2
0.00   2   92  3
0.01   2   88  5
0.02   4   84  2   2   1
0.03   4   75  6   1   2
0.04   4   69  9   1   2
0.05   4   55  13  3   2
0.06   4   48  14  2   4
0.07   4   44  16  2   4
0.08   7   35  17  3   2   1   0   1
...

mothur > cluster.classic(phylip=98_sq_phylip_amazon.dist)
********************#****#****#****#****#****#****#****#****#****#****#
***********************************************************************
unique 2   94  2
0.00   2   92  3
0.01   2   88  5
0.02   4   84  2   2   1
0.03   4   75  6   1   2
0.04   4   69  9   1   2
0.05   4   55  13  3   2
0.06   4   48  14  2   4
0.07   4   44  16  2   4
0.08   7   36  15  4   2   1   0   1
...


At a distance of 0.08 these two executions diverge from one another. This is because there was a tie. A sequence could have joined more than one pre-existing OTU. mothur is programmed to randomly select the OTU that it should join. Because of this, it is possible to get differences between runs. This is just a byproduct of using an algorithm-based approach to clustering.

Revisions

• 1.27.0 - reduced memory by 50% and increased speed by 55%.
• 1.28.0 - added count parameter
• 1.38.1 - Removes hard parameter.
• 1.40.4 - Bug Fix: Cluster commands printing of list file. #454