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The summary.single command will produce a summary file that has the calculator value for each line in the OTU data and for all possible comparisons between the different groups in the group file. This can be useful if you aren't interested in generating collector's or rarefaction curves for your multi-sample data analysis. It would be worth your while, however, to look at the collector's curves for the calculators you are interested in to determine how sensitive the values are to sampling. If the values are not sensitive to sampling, then you can trust the values. Otherwise, you need to keep sampling. For this tutorial you should download and decompress AmazonData.zip
To execute the summary.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)
The summary data for all of the single sample calculators is generated by default with the following command:
mothur > summary.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. In dotur, the summary data was provided in separate files ending in "ltt" and was only generated after the collector's curves were generated. Now, in mothur, all of this data is contained within a single "summary" file. In this case data was written to the file 98_lt_phylip_amazon.fn.summary, which looks like:
label Sobs Chao Chao_lci Chao_hci unique 96.000000 1558.222222 347.616318 8593.437067 0.00 95.000000 1144.375000 350.293575 4408.417964 0.01 93.000000 732.222222 307.998499 1993.501867 0.02 89.000000 1255.666667 288.403803 6914.903478 0.03 84.000000 481.193878 227.569853 1182.858662 0.04 81.000000 315.945000 179.197207 643.125486 0.05 73.000000 179.211735 119.908132 313.489912 0.06 68.000000 143.306667 100.656145 241.660852 0.07 66.000000 121.723183 90.116038 194.755526 0.08 59.000000 92.109568 72.389087 140.875896 0.09 57.000000 96.744444 72.675322 157.771188 0.10 55.000000 95.158163 70.499679 159.045903
Again, the first column contains the label for the row in the data set you are analyzing. Next, the first row of each column is labeled to indicate the calculator that was used to generate the data. For instance, here the data in the column labeled Sobs contains the number of OTUs that were observed in each row of the data set. Next in this file are the data for the Chao1 richness estimator. Because there are formulae for the 95% confidence intervals the first column contains the actual estimator and the next two columns contain the value for the lower and upper bound on the interval.
If you don't want to see all of the default calculators, you can tell mothur which ones to use in the summary file:
mothur > summary.single(calc=sobs-chao-npshannon)
This would generate the 98_lt_phylip_amazon.fn.summary file:
label Sobs Chao Chao_lci Chao_hci NPShannon unique 96.000000 1558.22222 347.61631 8593.43706 7.768419 0.00 95.000000 1144.37500 350.29357 4408.41796 7.355786 0.01 93.000000 732.222222 307.99849 1993.50186 6.831284 0.02 89.000000 1255.66666 288.40380 6914.90347 6.344819 0.03 84.000000 481.193878 227.56985 1182.85866 5.800593 0.04 81.000000 315.945000 179.19720 643.125486 5.559488 0.05 73.000000 179.211735 119.90813 313.489912 5.090494 0.06 68.000000 143.306667 100.65614 241.660852 4.853388 0.07 66.000000 121.723183 90.116038 194.755526 4.776910 0.08 59.000000 98.4453120 74.865828 157.068166 4.483528 0.09 57.000000 105.568047 75.830083 182.270567 4.377893 0.10 55.000000 100.872781 72.625547 174.389885 4.286385
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 > summary.single(calc=ace, abund=5)
Looking at the file, 98_lt_phylip_amazon.fn.summary, you'll see that when the distance is 0.10, the 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.
label & line
There may only be a couple of lines in your OTU data that you are interested in summarizing. There are three options. You could: (i) manually delete the lines you aren't interested in from you rabund, sabund, or list file; (ii) use the label option; and (iii) use the line option. To use the label option with either the summary.single() command you need to know the labels you are interested in. If you want the summary data for the lines labeled unique, 0.03, 0.05 and 0.10 you would enter:
mothur > summary.single(label=unique-0.03-0.05-0.10, calc=sobs-chao)
Opening 98_lt_phylip_amazon.fn.summary you would see the output as:
label Sobs Chao Chao_lci Chao_hci unique 96.000000 1558.22222 347.616318 8593.437067 0.03 84.000000 481.193878 227.569853 1182.858662 0.05 73.000000 179.211735 119.908132 313.4899120 0.10 55.000000 100.872781 72.6255470 174.3898850
To use the line option you need to know the line numbers that you are interested in. To generate the same output with the line option you would enter:
mothur > summary.single(line=1-5-7-12, calc=sobs-chao)