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Cluster.split

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The cluster.split command can be used to assign sequences to OTUs and outputs a .list, .rabund, .sabund files. It splits large distance matrices into smaller peices ...

  • 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.


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 average neighbor algorithm is the default option. For this tutorial you should download the AmazonData.zip file and decompress it.


There are two part to the cluster.split command the splitting of your files into distinct groupings and the clustering of these groupings.

Splitting your files

The cluster.split command can split your files in 3 ways. Splitting by distance file, by classification, or by classification also using a fasta file. The splitmethod parameter allows you to specify how you want to split your files before you cluster, default=distance, options distance, classify or fasta.


Splitting by distance

For the distance file method, you need only provide your distance file and mothur will split the file into distinct groups.

mothur > cluster.split(phylip=98_lt_phylip_amazon.dist)

or

mothur > cluster.split(column=96_lt_column_amazon.dist, name=amazon.names)

large

The large parameter allows you to indicate that your distance matrix is too large to fit in RAM. The default value is false.

mothur > cluster.split(phylip=98_lt_phylip_amazon.dist, large=T)

Splitting by classification

For the classification method, you need to provide your distance file and taxonomy file, and set the splitmethod to classify. You will also need to set the taxlevel you want to split by. mothur will split the sequences into distinct taxonomy groups, and split the distance file based on those groups.

First you need to classify your sequences using the classify.seqs command.

mothur > classify.seqs(fasta=amazon.fasta, template=silva.nogap.fasta, taxonomy=silva.bacteria.silva.tax, probs=f)
mothur > cluster.split(column=96_lt_column_amazon.dist, name=amazon.names, taxonomy=amazon.silva.taxonomy, splitmethod=classify)

Splitting by classification using fasta

For the classification method using a fasta file, you need to provide your fasta file, names file and taxonomy file. You will also need to set the taxlevel you want to split by. mothur will split the sequence into distinct taxonomy groups, and create distance files for each grouping.

First you need to classify your sequences using the classify.seqs command.

mothur > classify.seqs(fasta=amazon.fasta, template=silva.nogap.fasta, taxonomy=silva.bacteria.silva.tax, probs=f)
mothur > cluster.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta)


taxonomy

The taxonomy parameter allows you to enter the taxonomy file for your sequences, this is only valid if you are using splitmethod=classify or fasta. Be sure your taxonomy file does not include the probability scores.

taxlevel

The taxlevel parameter allows you to specify the taxonomy level you want to use to split the distance file, default=3, meaning use the third taxon in each list.

mothur > cluster.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4)


cluster

The cluster parameter allows you to indicate whether you want to run the clustering or just split the distance matrix, default=T. The cluster=f option is used with the file option, see below.

mothur > cluster.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4, cluster=f)

Clustering

file

The file option which allows you to enter your file containing your list of column and names/count files as well as the singleton file. This file is mothur generated, when you run cluster.split() with the cluster=f parameter, see above. This can be helpful when you have a large dataset that you may be able to use all your processors for the splitting step, but have to reduce them for the cluster step due to RAM constraints. For example:

cluster.split(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.fasta, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.uchime.pick.pick.pick.count_table, taxonomy=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pds.wang.pick.pick.taxonomy, taxlevel=4, cluster=f, processors=8, cutoff=0.15) 
cluster.split(file=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.pick.file, processors=4)

This allows your to maximize your processors during the splitting step. Also, if you are unsure if the cluster step will have RAM issue with multiple processors, you can avoid running the first part of the command multiple times.


name

A names 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 amazon1.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 names 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 names file is not required (unless you are using the column= option), 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. 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 names 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=amazon1.names)

Example from amazon1.count_table:
 ...
 U68616	1
 U68617	1
 U68618	2
 U68619	1
 U68621	1
 ...

mothur > cluster.split(fasta=amazon.fasta, count=amazon1.count_table, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4)


method

By default cluster() uses the average 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.split(phylip=98_lt_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.split(phylip=98_lt_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.split(phylip=98_lt_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	
...

cutoff

Similar to reading in the distance matrix, you can set a cutoff value for performing the clustering operation. This will provide a similar boost in speed if you didn't set the cutoff for reading in the matrix. If you already set the cutoff value when reading in the matrix, then don't worry about it for clustering unless you want an even smaller distance. The default value is 0.025. The cutoff can be set for the cluster command as follows:

mothur > cluster.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4, 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	

hard

By default the cutoff parameter is set to cutoff + (5 / (precision * 10.0)). So if you set the cutoff to 0.03 with precision of 100, the cutoff would be set to 0.035. If you want a hard cutoff of 0.03, then you need to set the hard parameter to true.

mothur > cluster.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4, cutoff=0.03, hard=t) 


precision

Perhaps the most commonly asked question is why the cluster 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 read.dist() and cluster() commands:

mothur > cluster.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4, 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.

showabund

timing

processors

If you have a Windows computer, move on, this feature doesn't apply to you. If you're one of the cool kids, you get to use the processors option, which enables you to reduce the processing time by using multiple processors. You are able to use as many processors as your computer has with the following option:

mothur> cluster.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4, processors=2)


Clustering with Vsearch

The vsearch program is written by the vsearch team. You can now use vsearch clustering methods through mothur. NOTE: vsearch is not available for Windows.

fasta

Vsearch requires a fasta file to cluster.

mothur > cluster(fasta=amazon.fasta, method=agc)

name

The name parameter allows you to enter the name file associated with your fasta file.

mothur > cluster(fasta=amazon.fasta, name=amazon.names, method=agc)

count

The count parameter allows you to enter the count file associated with your fasta file.

mothur > cluster(fasta=amazon.fasta, count=amazon.count_table, method=agc)

method

The available clustering methods are agc and dgc.

mothur > cluster(fasta=amazon.fasta, method=dgc)

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.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4)
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.split(fasta=amazon.fasta, name=amazon1.names, taxonomy=amazon.silva.taxonomy, splitmethod=fasta, taxlevel=4)
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
  • 1.29.0 - added cluster parameter
  • 1.30.0 - no longer creates concatenated distance matrix for splitmethod=fasta.
  • 1.31.0 - Bug Fix: - when splitting by taxonomy mothur was saving temp fasta files as the "current" fasta file.
  • 1.34.0 - added the file option which allows you to enter your file containing your list of column and names/count files as well as the singleton file. This file is mothur generated, when you run cluster.split() with the cluster=f parameter. This can be helpful when you have a large dataset that you may be able to use all your processors for the splitting step, but have to reduce them for the cluster step due to RAM constraints. For example: cluster.split(fasta=yourFasta, taxonomy=yourTax, count=yourCount, taxlevel=3, cluster=f, processors=8) then cluster.split(file=yourFile, processors=4). This allows your to maximize your processors during the splitting step. Also, if you are unsure if the cluster step will have RAM issue with multiple processors, you can avoid running the first part of the command multiple times.
  • 1.35.0 - Clustering commands did not include the count file info. when printing list file OTU order. Only effects clustering commands. *.pick commands must preserve otuLabels order. - http://www.mothur.org/forum/viewtopic.php?f=3&t=3460&p=10483#p10483
  • 1.35.0 - Bug Fix: MPI version compile issue, http://www.mothur.org/forum/viewtopic.php?f=4&t=3453&p=10073#p10073. fixed in 1.34.1.
  • 1.36.0 - Bug Fix: did not allow you to use the classic option with the file option.
  • 1.37.0 - Adds vsearch clustering methods: agc and dgc. #169