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This tutorial explains how to use a few different taxa summary comparison techniques that are available in compare_taxa_summaries.py. Taxa summary files are compared using either Pearson or Spearman correlation coefficients, which are computed between pairs of samples. The script provides two different comparison modes. Paired comparison mode compares samples that match between the two taxa summary files, while expected comparison mode compares all samples in one taxa summary file to an “expected” sample in the other file.
One potential use for this script is to compare two taxa summary files that have been constructed using different taxonomic classification techniques. For example, you might want to see whether the taxonomic composition of samples assigned using the RDP classifier are correlated with the taxonomic composition of samples derived from BLAST or RTAX. Another example is to see whether using different confidence levels or E-values (for RDP and BLAST, respectively) yield taxonomic compositions that are similar/correlated.
Another example use-case is to compare the taxonomic composition of several mock community replicate samples to a single expected, or known, sample community.
This script is also useful for sorting and filling taxa summary files so that each sample has the same taxa listed in the same order (with missing taxa reporting an abundance of zero). The sorted and filled taxa summary files can then be passed to a script, such as plot_taxa_summary.py, to visually compare the differences using the same taxa coloring scheme.
Please note that this tutorial does not attempt to cover every possible option that can be used in the taxa summary comparison script. Instead, it attempts to provide useful examples to give you an idea of how to use this script in your own study, as well as customize some of the output to your liking. For a complete listing of the available options, please refer to the compare_taxa_summaries.py script documentation.
You can obtain the files used in this tutorial here. The files are derived from the overview tutorial. The unzipped directory will contain two taxa summary files named ts_rdp_0.60.txt and ts_rdp_0.80.txt. These taxa summaries were constructed from the overview tutorial representative sequence set by assigning taxonomy to it using the RDP classifier with two different confidence levels (0.60 and 0.80). The gg_otus_4feb2011 97% representative set was used as training input to the RDP classifier.
Depending on the options that you invoke on the script, either three or four output files will be generated. They will all be placed in the directory specified by the required -o option. Most of the output files will be tab-separated text files containing information about the test that was performed and its results. These can easily be viewed in a spreadsheet program such as Excel.
Two of the output files will be the sorted and filled versions of the input taxa summary files, which can then be used in plot_taxa_summary.py to visualize the differences in taxonomic composition. These files will be named based on the basename of the input taxa summary files. If the input files’ basenames are the same, the output files will have ‘0’ and ‘1’ appended to their names to keep the filenames unique. The first input taxa summary file will have ‘0’ in its filename and the second input taxa summary file will have ‘1’ in its filename.
The third output file will contain the results of the overall comparison of the input taxa summary files using the specified comparison mode. The file will be named overall_comparison.txt and will be in tab-delimited format for easy viewing in a program such as Excel.
If –perform_detailed_comparisons is specified, a fourth output file named detailed_comparisons.txt will be created. This file will also be in tab-delimited format and will contain the results of comparing individual pairs of samples (whereas overall_comparison.txt contains results from comparing all pairs of samples at once).
In order to see whether the results of using the RDP classifier at a confidence level of 0.60 versus 0.80 are correlated, we need to compute the correlation between each of the paired samples in the two taxa summary files. To do this, run the following command:
compare_taxa_summaries.py -i ts_rdp_0.60.txt,ts_rdp_0.80.txt -m paired -o taxa_comp
This command will create a new output directory named taxa_comp, which will contain three files named ts_rdp_0.60_sorted_and_filled.txt, ts_rdp_0.80_sorted_and_filled.txt, and overall_comparison.txt. The first two files are sorted and filled versions of the input taxa summary files, meaning that the taxa (rows) have been sorted and any missing taxa have been added with zero abundance. This results in taxa summary files that have the same taxa listed in the same order. Open up overall_comparison.txt to see the results of the test:
The comment at the top of the file tells us that Pearson’s correlation coefficient was used to compute the correlation between samples. The value of 0.9993 is very close to +1, which indicates strong positive correlation between the paired samples in the two input taxa summary files. Both parametric and nonparametric p-values indicate that the correlation is statistically significant. The two-sided 95% confidence interval also supports the same conclusion. Thus, it appears that using the RDP classifier at a confidence level of 0.60 or 0.80 yields highly correlated results (i.e. you might draw the same biological conclusions from using either taxa summary file).
Note the fifth line of the file, which tells us that 9 samples matched between the two taxa summary files. All 9 samples matched between the two files because both files have the same sample IDs. The script determines which samples to compute the correlation between based on matching sample IDs between the two files (the order of sample IDs does not matter between the two files, but the names must match exactly). Any sample IDs that do not match will not be considered when calculating the results.
If you need to compare taxa summaries that do not have matching sample IDs, you can use a sample ID map to provide a mapping between sample IDs. For a description of this file format, see Sample id map. To illustrate this, run the following command:
compare_taxa_summaries.py -i ts_rdp_0.80.txt,ts_rdp_0.60_renamed.txt -m paired -o taxa_comp_using_sample_id_map -s sample_id_map.txt -c spearman
The second input taxa summary file (ts_rdp_0.60_renamed.txt) is simply the original ts_rdp_0.60.txt file with all sample IDs starting with ‘PC.’ renamed to ‘S.’. The sample ID map (sample_id_map.txt) specifies how to pair up samples between the two taxa summary files now that the sample IDs do not exactly match.
This command will create a new output directory named taxa_comp_using_sample_id_map, which will contain three files like before. Open up overall_comparison.txt to see the results of the test:
Notice that all 9 samples were still included because we provided a sample ID map. If we had not provided a sample ID map, the script would have raised an error stating that the two taxa summary files were incompatible because no matches could be found.
Also notice that we used the -c option to use the Spearman correlation coefficient instead of the Pearson correlation coefficient. This is why we obtained a different value for the correlation coefficient. Regardless, we still reach essentially the same conclusion regarding the correlation of the two taxa summary files.
If you’d like to see the correlation tests applied to each pair of samples (instead of to all pairs of samples at one time), use the –perform_detailed_comparisons option:
compare_taxa_summaries.py -i ts_rdp_0.60.txt,ts_rdp_0.80.txt -m paired -o taxa_comp_detailed --perform_detailed_comparisons
In addition to the three previous file, you will find the file detailed_comparisons.txt in the newly created directory taxa_comp_detailed:
We see that nearly all of the comparisons indicate strong, statistically-significant correlation. The p-values are provided that have been Bonferroni-corrected for multiple comparisons (though are still zero for the parametric test because the uncorrected p-values are zero). The minor differences in confidence intervals between correlation coefficients that are the same is attributed to rounding error.
To perform a one-tailed test of negative association instead of a two-tailed test, run the following command:
compare_taxa_summaries.py -i ts_rdp_0.60.txt,ts_rdp_0.80.txt -m paired -o taxa_comp_one_tailed -t low -l 0.90
This command will create a new output directory named taxa_comp_one_tailed, which will contain three files like before. Open up overall_comparison.txt to see the results of the test:
We performed a one-tailed test of negative association, i.e. the null hypothesis was that Pearson’s product-moment coefficient was equal to 0 (no association) and the alternative hypothesis was that the correlation coefficient was less than 0. We see that both parametric and nonparametric p-values are now insignificant, indicating that we fail to reject the null hypothesis of no association (keep in mind that the observed correlation coefficient of 0.9993 indicates strong positive correlation, not negative correlation).
Also note that we specified a 90% confidence interval instead of the default 95% confidence interval. If you compare these results to the results in the first command of the tutorial, you’ll see that the 90% confidence interval is a little tighter than the 95% confidence interval, which is what we would expect.
By specifying -m expected to the script, all samples in the first input taxa summary file will be compared to a single “expected” sample in the second input taxa summary file. This comparison mode is especially useful if you need to compare the taxonomic composition of various samples to a sample that has a known taxonomic composition. The second input taxa summary file must contain only a single sample or you must tell the script which one to use (if there are multiple samples) using the –expected_sample_id option. The output files will be in the same format as those seen previously when using paired comparison mode.