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collect.single generates collector's curves using calculators, that describe the richness, diversity, and other features of individual samples. Collector's curves describe how richness or diversity change as you sample additional individuals. If a collector's curve becomes parallel to the x-axis, you can be reasonably confident that you have done a good job of sampling and can trust the last value in the curve. Otherwise, you need to keep sampling. For this tutorial you should download and decompress AmazonData.zip
To execute the collect.single() command you first need to have either run the cluster() or read.otu() commands. Enter either of the following commands:
mothur > read.dist(phylip=98_lt_phylip_amazon.dist) mothur > cluster(cutoff=0.10)
mothur > read.otu(list=98_lt_phylip_amazon.fn.list)
or to run the single analysis with multiple samples:
mothur > read.otu(list=98_lt_phylip_amazon.fn.list, group=amazon.groups)
mothur > read.otu(shared=98_lt_phylip_amazon.fn.shared)
By default, the collect.single() command will randomize the order in which the individuals are sampled. So if you run collect.single() multiple times, you will get slightly different results. The collector's curve data for all of the single sample calculators is generated by default with the following command:
mothur > collect.single()
This will result in output to the screen looking like:
unique 1 0.00 2 0.01 3 0.02 4 0.03 5 0.04 6 0.05 7 0.06 8 0.07 9 0.08 10 0.09 11 0.10 12
The left column indicates the label for each line in the data set and the right column indicates the row number in the data set. Execution of collect.single() will generate 8 files, one for each of the richness and diversity calculators. If you look at 98_sq_phylip_amazon.fn.sobs you will see something like:
numsequences unique 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 1 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 98 96.0000 95.0000 93.0000 89.0000 84.0000 81.0000 73.0000 68.0000 66.0000 59.0000 57.0000 55.0000
In this file the first column tells you how many sequences have been sampled; this would typically be plotted on the x-axis of a graph. Each subsequent column has a heading, which indicates the label for the line being analyzed from your OTU data file. The data in the column tells you how many OTUs were observed for that label and number of sequences. By default, collect.single() prints output every 100 sequences.
For some calculators there are formulae that enable us to determine the 95% confidence intervals. For example, if you look at 98_sq_phylip_amazon.fn.chao, you will see something like:
numsampled unique lci hci 0.00 lci hci 0.01 lci hci 0.02 lci hci 1 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 1.5000 98 1558.2 347.61 8593.4 1144.3 350.29 4408.4 732.22 308.00 1993.5 1255.7 288.40 6914.9
Again the first column tells you how many sequences have been sampled. You then need to look at the columns in groups of three. The first column of the set of three has a heading, which indicates the label for the line being analyzed from your OTU data file. The data in the column tells you how many OTUs were predicted, using the Chao1 estimator, to be in that sample for that label and number of sequences. The following two columns contain the bounds on the 95% confidence interval.
If you are not interested in producing collector's curves for all of the calculators, it is possible to select the calculators you want using the calc option:
mothur > collect.single(calc=sobs-chao)
This command would only generate the files 98_lt_phylip_amazon.fn.sobs and 98_lt_phylip_amazon.fn.chao. Note that the sobs file does not have 95% confidence intervals where as the chao file does.
By default the ACE estimator uses 10 as the cutoff between OTUs that are rare and abundant. So if an OTU has more than 10 individuals in it, then it is considered abundant. This is really just an empirical decision and we are merely following the lead of Anne Chao and others who implement 10 in their software. If you would like to use a different cutoff, you can use the abund option:
mothur > collect.single(calc=ace, abund=5)
Looking at the file, 98_lt_phylip_amazon.fn.collect, you'll see that when the distance is 0.10, the final ACE estimate value is 101.1 (95% CI=75.5-158.8) compared to 161.4 (95% CI=120.3-228.4) when abund was 10. You will not see a difference when the maximum abundance is below the threshold.
Within the suite of calculators available in mothur are a set that will predict the number of additional OTUs that will be observed for a given sample size. By default these calculators will base the prediction on a sample that is the same size as the initial sampling. If you would like to use a different sample size, use the size option:
mothur > summary.single(calc=boneh, size=50)
The value of size should be between 1 and the size of the initial sampling. If you go beyond those limits, the default sample size will be used.
There may only be a couple of lines in your OTU data that you are interested in generating collector's curves for. There are two options. You could: (i) manually delete the lines you aren't interested in from you rabund, sabund, or list file; (ii) or use the label option. To use the label option with the collect.single() command you need to know the labels you are interested in. If you want the collector's curve data for the lines labeled unique, 0.03, 0.05 and 0.10 you would enter:
mothur > collect.single(label=unique-0.03-0.05-0.10)
In the file 98_sq_phylip_amazon.fn.sobs you would see something like:
numsequences unique 0.03 0.05 0.10 1 1.0000 1.0000 1.0000 1.0000 98 96.0000 84.0000 73.0000 55.0000
For larger datasets you might not be interested in obtaining all of the data for the number of sequences sampled. For instance, if you have 100,000 sequences, you may only want to output the data every 100 sequences. Alternatively, if you only have 100 sequences, you may only want to output all of the data. The default setting is to output data every 100 sequences. By altering the freq option you can set the frequency that the analysis is performed:
mothur > collect.single(freq=1)
mothur > collect.single(freq=10)
The second command would generate data such as this:
numsequences unique 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 1 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 10 10.0000 10.0000 10.0000 10.0000 10.0000 9.0000 9.0000 10.0000 9.0000 8.0000 10.0000 9.0000 20 20.0000 19.0000 20.0000 19.0000 20.0000 19.0000 17.0000 20.0000 19.0000 15.0000 18.0000 19.0000 30 30.0000 29.0000 29.0000 29.0000 29.0000 29.0000 25.0000 28.0000 27.0000 21.0000 25.0000 25.0000 ...