microbio_me_qiime()returned an error. What’s wrong?
This vignette includes answers and supporting materials that address frequently asked questions (FAQs), especially those posted on the phyloseq issues tracker.
For most issues the phyloseq issues tracker should suffice; but occasionally there are questions that are asked repeatedly enough that it becomes appropriate to canonize the answer here in this vignette. This is both (1) to help users find solutions more quickly, and (2) to mitigate redundancy on the issues tracker.
All users are encouraged to perform a google search and review other questions/responses to both open and closed issues on the phyloseq issues tracker before seeking an active response by posting a new issue.
##  '1.19.1'
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The most common cause for this errors is derived from a massive change to the way biom files are stored on disk. There are currently two “versions” of the biom-format, each of which stores data very differently. The original format – and original support in phyloseq – was for biom-format version 1 based on JSON.
The latest version – version 2 – is based on the HDF5 file format, and this new biom format version recently become the default file output format for popular workflows like QIIME.
The biomformat package is the Bioconductor incarnation of R package support for the biom file format, written by Paul McMurdie (phyloseq author) and Joseph Paulson (metagenomeSeq author). Although it has been available on GitHub and BioC-devel for many months now, the first release version of biomformat on Bioconductor will be in April 2016. In that same release, phyloseq will switch over from the JSON-only biom package hosted on CRAN to this new package, biomformat, which simultaneously supports biom files based on either HDF5 or JSON.
This difference will be largely opaque to users, and phyloseq will “just work” after the next release in April.
import_biom function to read your recent QIIME or other biom-format data.
Additional back details are described in Issue 443.
As just described, HDF5 biom format is currently supported in the development version of phyloseq, via the new beta/development package called biomformat on BioC-devel and GitHub:
If you need to use HDF5-based biom format files immediately and cannot wait for the upcoming release, then you should install the development version of the biomformat package by following the instructions at the link above.
Even though the biom-format supports the self-annotated inclusion of major components like that taxonomy table and sample data table, many tools that generate biom-format files (like QIIME, MG-RAST, mothur, etc.) do not export this data, even if you provided the information in your data input files. The reason for this boggles me, and I’ve shared my views on this with QIIME developers, but there nevertheless seems to be no plan to include your sample data in the ouput biom file.
Furthermore, even though I have proposed it to the biom-format team, there is currently no support (or timeline for support) for inclusion of a phylogenetic tree within a “.biom” file.
A number of tutorials are available demonstrating how one can add components to a phyloseq object after it has been created/imported. The following tutorial is especially relevant
Which makes use of the following functions:
microbio_me_qiime()returned an error. What’s wrong?
The QIIME-DB server is permanently down.
Users are suggested to migrate their queries over to Qiita.
Indeed, the previous link to microbio.me/qiime now sends users to the new Qiita website.
Stay tuned. The Qiita API needs to be released by the Qiita developers first. The phyloseq developers have no control over this, as we are not affiliated directly with the QIIME developers. Once there is an official Qiita API with documentation, an interface for phyloseq will be added.
We found the
microbio_me_qiime() function to be very convenient while the QIIME-DB server lasted. Hopefully an equivalent is hosted soon.
Every plot function in phyloseq returns a ggplot2 object. When these objects are “printed” to standard output in an R session, for instance,
then the graphic is rendered in the current graphic device.
Alternatively, if you save the object output from a phyloseq
plot_ function as a variable in your session, then you can further modify it, interactively, at your leisure. For instance,
p1 = plot_tree(esophagus, color = "Sample") p1
p1 + ggtitle("This is my title.") + annotate("text", 0.25, 3, color = "orange", label = "my annotation")
There are lots of ways for you to generate custom graphics with phyloseq as a starting point.
The following sections list some of my favorites.
For example, the plot_ordination() examples tutorial provides several examples of using additional ggplot2 commands to modify/customize the graphic encoded in the ggplot2 object returned by
The ggplo2 documentation is the current and canonical online reference for creating, modifying, and developing with ggplot2 objects.
For simple changes to aesthetics and aesthetic mapping, the aesthetic specifications vignette is a useful resource.
psmelt function converts your phyloseq object into a table (
data.frame) that is very friendly for defining a custom ggplot2 graphic. This function was originally created as an internal (not user-exposed) tool within phyloseq to enable a DRY approach to building ggplot2 graphics from microbiome data represented as phyloseq objects.
When applicable, the phyloseq
plot_ family of functions use
psmelt. This function is now a documented and user-accessible function in phyloseq – for the main purpose of enabling users to create their own ggplot2 graphics as needed.
There are lots of great documentation examples for ggplot2 at
The following are two very simple examples of using
psmelt to define your own ggplot2 object “from scratch”. It should be evident that you could include further ggplot2 commands to modify each plot further, as you see fit.
data("esophagus") mdf = psmelt(esophagus) # Simple bar plot. See plot_bar() for more. ggplot(mdf, aes(x = Sample, y = Abundance)) + geom_bar(stat = "identity", position = "stack", color = "black")
# Simple heat map. See plot_heatmap() for more. ggplot(mdf, aes(x = Sample, y = OTU, fill = Abundance)) + geom_raster()
If your new custom plot function is awesome and you think others might use it, add it to the
"plot-methods.R" source file and submit a pull request on GitHub.
Please include example and test code in the code included in your pull request.
I’ll try and add it to the package by the next release. I will also give you authorship credit in the function doc. See the “typo fix” section below for further details about GitHub pull requests…
Development of new R functions/commands for creating/modifying new geometric objects is now formally documented in the ggplot2 extension vignette.
This may be related to the previous section, in that your ggplot2 extension for phyloseq could be contributed to the phyloseq project as a pull request.
This is something that is actually faster and less work for you to solve yourself and contribute back to the phyloseq package. For trivial typo fixes, I will quickly include your fixes into the package code. Sometimes I accept them on my cell phone while I’m still in bed. No wasted time on either end! :-)
The point is that this should be simple, and is simple if you follow one of the following suggestions.
GitHub now provides the option to make changes to code/text of a repository directly from your web browser through an in-page editor. This handles all the Git details for you. If you have a GitHub account and you’re logged in, all you’d have to do is locate the file with the offending typo, then use the “edit” button to make the changes and send the to me as a pull request.
(The following instructions are borrowed from Yihui Xie’s site about fixing typos)
Alternatively, for those who want to try GIT and Github pull requests, which make it possible for you to contribute to open source and fix obvious problems with no questions being asked – just do it yourself, and send the changes to the original author(s) through Github.
The official documentation for Github pull requests is a little bit verbose for beginners. Basically what you need to do for simple tasks are:
Before getting to more specific issues, let’s start by keeping appropriately separate the concept of
These two concepts have been often-conflated – mostly by purveyors of methods that use rarefying – wrongly insisting that rarefying is somehow addressing both problems and the matter is settled. Unfortunately rarefying is a very inefficient, noise-introducing method that poorly addresses the data analysis challenges that motivate either concept.
DESeq2 and related solutions can help you address the need for standardization (e.g. differing library sizes) at a particular step in your analysis while still making efficient inferences from your data.
The denoising problem is best addressed at the sequence-processing level, and the best general-purpose option currently available is:
The answer to a question of this category depends a lot on your experiment, and what you want to learn from your data. The following are some resources that may help.
That is an expected artifact of current sequencing technologies, and not a “problem” on its own. In most cases, differences in total counts are uncorrelated with any variable in your experimental design. You should check that this is the case. It remains possible that there are structural/procedural artifacts in your experiment that have influenced the total counts. If library sizes are correlated with one of your design variables, then this might represent an artifact that you need to address more carefully. This is a decision that you will have to make and defend. No software package or workflow can address this for you, but phyloseq/R can certainly help you check for correlation. See the
sample_data() accessor functions.
Other than the portent of structural biases in your experiment, you should recall that comparisons between observation classes that have uneven sample sizes is not a new nor unsolved problem in statistics.
The most useful analytical methods you can use in this context are therefore methods that expect and account for differences in total number of reads between samples.
How you account for these library size differences should depend on the type of analysis in which you are engaged, and which methods you plan to use. For instance, for a beta-diversity measure like Bray-Curtis Dissimilarity, you might simply use the relative abundance of each taxa in each sample, as the absolute counts are not appropriate to use directly in the context where count differences are not meaningful.
For further information, see
No. Generally speaking, the answer is no. Most alpha diversity methods will be most effective when provided with the originally-observed count values.
The misleading notion – that normalization is necessary prior to alpha-diversity analysis – seems to be derived from various “one size fits all” pipeline tools like QIIME, in which it is often encouraged to rarefy counts as a normalizing transformation prior to any/all analysis. While this may simplify certain aspects of pipeline software development, it is analytical and statistical folly. Rarefying microbiome data is statistically inadmissible.
For further information, I suggest reviewing literature such as
This sort of question usually appears after someone used a log-like transformation / variance stabilizing transformation on their data, in preparation for an exploratory analysis via ordination. Negative values in this context probably correspond to “less than one count” after rescaling. For many ordination methods, like PCA, negative numbers are not a problem.
Instead, the problem is often posed because a user also wants to use a particular distance measure that is undefined or unstable in the presence of negative entries. In this context, however, the more negative a value is, the more likely that it was zero, or very small, in the original “raw” count matrix. For most distances and hypotheses, these values are probably not very important, or even negligible. Given this, it is probably quite reasonable to do one of the following:
Xis your matrix, you can accomplish this with
X[X < 0.0] <- 0.0
Please also note that taxa entries that are all negative after transformation, or equivalently are very small or almost always zero, should probably be filtered from your data prior to analysis. There are many different reasons for this.
See my SO post on alternative geometric mean functions in R There are several examples for alternative calculations of geometric mean, and some of these might solve the problem of having an error.
See also the discussion on Issue 445 regarding geometric means.
Alternative library size estimators may be appropriate for your data, and it remains your responsibility to determine if any specific approach is valid.
Mike Love (a developer for DESeq2), suggested the following consideration:
“On the other hand, very sparse count datasets, with large counts for single samples per row and the rest at 0, don’t fit well to the negative binomial distribution. Here, the VST or simply shifted log,
log(count+k), might be a safer choice than the
rlog. A way that I test for sparsity is looking at a plot of the row sum of counts and the proportion of count which is in a single sample.”
Also, think carefully about what you mean here. I suspect this statement could be more accurately stated as, pseudocounts are not appropriate for my experiment, data, and the analysis step I was about to perform. Your position in this case is thus based on a combination of how the data appears to behave, and your knowledge of how pseudocounts would affect the analysis you were going to use. Consider the following.
There is now lots of documentation on this topic.
For starters, please see the phyloseq vignette devoted to this topic.
A Google search for “phyloseq differential abundance” will also likely turn up a number of useful, related resources.
I am currently a biostatistician at Second Genome, Inc., which offers complete end-to-end microbiome experiment solutions as a fee-for-service. In some cases Second Genome clients already have their microbiome data and want to make use of our team of trained microbiome analysts to get the most information from their expeirment. I recommend contacting one of the sales associates at the link above.
My day-to-day efforts are in understanding the role of the microbiome in human health and disease. If you’re looking for a collaboration on your microbiome data collection or data analysis, please contact Second Genome Solutions.