# phyloseq Section 01 - Import data into R and organize as phyloseq object

## Goals for this section

This is the first section of the phyloseq lab. In this section you will learn how to import data from a common format and how to manipulate, investigate, and merge data together into a single “experiment-level” object. You will also learn how to save this object into a compressed and self-describing file, that you can easily and quickly load into a new R session without repeating the entire parsing and organizing process.

### Create and save a phyloseq object

The phyloseq object that you create and save in this section will be used in the next section; but don’t worry! If you have trouble or don’t finish a copy of the object we are trying to make in this tutorial is included with these materials, the file named: mp-phyloseq-lab-00.RDS

### Practice reading R package documentation

A secondary goal of this section is for you to become familiar or even comfortable reading and using the documentation of R functions that accompanies every good R package. Don’t give up too quickly! Every time you get stuck and start searching for an example you are practicing how to get around R on your own. Each of the functions we will use are well-documented, and the tutorial text provides each main function that you will use.

### Answer Key

In programming there are many ways to accomplish a task. I have provided an answer key in the form of a working example of code that accomplishes the task being asked of you. If your version of the code does not match exactly, that is okay, yours is still correct as long as it works! Please try not to use the answer key until you have exhausted your other options, including an earnest attempt at exploring the documentation of the functions mentioned in each prompt, as well as your own trial-and-error attempts to accomplish the task.

# Download QIIME tutorial data (Moving Pictures)

## System command, `wget`, `tar`, etc.

1. Locate the Moving Pictures tutorial compressed tarball (`.tgz`), or uncompressed file. Note this location or save the path as an object. If you do not have the relative path immediately available from the earlier QIIME lab, you can download the lab to the current directory from the following link: `ftp://ftp.microbio.me/qiime/tutorial_files/moving_pictures_tutorial-1.9.0.tgz` Hint, you do not need to leave your R session to do this. R can initiate system commands (bash) using the `system()` function. So in this case: `system("wget ftp://ftp.microbio.me/qiime/tutorial_files/moving_pictures_tutorial-1.9.0.tgz")`
2. Unzip this copressed directory archive with `system("tar -xvzf moving_pictures_tutorial-1.9.0.tgz")`

## Find the biom file in the QIIME tutorial data

There is a specific biom file we want to work with. The QIIME tutorial might have many.

1. Locate the biom file `otu_table_mc2_w_tax_no_pynast_failures.biom`. Hint: use the autocomplete feature of system paths by typing the tab button when the cursor is within a pair of quotation marks. The path within the tutorial is: `moving_pictures_tutorial-1.9.0/illumina/precomputed-output/otus`
2. Save this full or relative path as `fullInputPath`
3. Check that the system recognizes existence of the file with `file.exists()`
4. Check that file is the expected size with `file.info()`

# Load Libraries, Import Data

## Load phyloseq

1. Load the phyloseq, biomformat, and ggplot2 packages using the `library()` command.
2. Check the version number of each package using the `packageVersion()` command.

## Import Data

### Import data from BIOM object

We want to use a special import function that is not yet fully integrated into phyloseq. This is to accomodate the new version of the biom format that is based on HDF5 and has become the default output from QIIME.

1. Import the biom file you located above using the `biomformat::read_biom()`, and save the result as an object named `biom1`. Make sure to specify the package with the `::` notation, because the older biom package also includes a `read_biom()` function. Save this data, the output of `biomformat::read_biom()` as `biom1`. (The phyloseq package will be migrating to biomformat in the next release.)
2. phyloseq’s current `import_biom()` function can already properly interpret `biom1` and convert it to proper phyloseq format. Save this, the output of `import_biom()` as `mp0`.
3. Use the following command to keep only the first seven taxonomic ranks: `tax_table(mp0) <- tax_table(mp0)[, 1:7]` This is to fix a weird importing artifact. But now you know how to do it :-)
4. Print the object to standard out using the `print()` function, or simply typing the object name and pressing enter. What does it show you?
5. Explore the taxonomic ranks in this data using the `rank_names()` function. What are the taxonomic ranks available in this data?

### Import sample data from mapping file

1. See if you can locate the mapping file you used in the QIIME tutorial.

2. If not, it should be in: moving_pictures_tutorial-1.9.0/illumina/map.tsv

3. Import the mapping file using the `import_qiime_sample_data()` function, and save this as an object named `qsd`.
4. Combine your biom file and your sample data into one phyloseq object using the `merge_phyloseq()` function.

5. Read out the sample variables that are now present in your object.

### Import phylogenetic tree

Read phylogenetic tree. Add it to the data object.

1. Find QIIME-output phylogenetic tree object. Hint: it is at moving_pictures_tutorial-1.9.0/illumina/precomputed-output/otus/rep_set.tre.
2. Define this path as a character string in object, `treeFile1`
3. Use phyloseq’s `read_tree()` function to import the tree. Save it as `tree1`.
4. Add the tree to the previous phyloseq object, and save this new result as `mp2`. Again, to do this you’ll use `merge_phyloseq()` with arguments `mp1` and `tree1`.

### Import representative sequences

The OTU centroids, often called the “representative sequences”, is the set of sequences that represent the center of an OTU in sequence space. There is one such sequence per OTU, and they are all different from one another. In principle they should be at least as dissimilar from one another as the OTU radius that you defined during OTU clustering. This is commonly 3% sequence dissimilarity, but this valuse is arbitrary, and you may select a different, smaller value in practce.

1. Locate the representative sequences for this tutorial. If you’re not sure where they are from the previous tutorial, the following is the relative path where they are in the original tutorial data structure: `moving_pictures_tutorial-1.9.0/illumina/precomputed-output/otus/rep_set.fna`.
2. Import the representative sequences for this data, using the `Biostrings::readDNAStringSet()` function for reading fasta files, and save this DNAStringSet object as `bs1`.
3. The names of each sequence have extra decorations beyond just the OTU ID. We need exact OTU ID matches in order to merge this data with your phyloseq object. The following code will accomplish this for you, by removing everything after the first space from the sequence ID: `names(bs1) <- gsub("\\s.+$", "", names(bs1))`
4. Add `bs1` to your data object using the `merge_phyloseq()` function, and save the result as `mp3`.
5. Check that your number of OTUs did not change using the `ntaxa()` function on the unmerged objects and on `mp3`.
6. You can also check that the OTU names match ahead of attempting the merge by using the following structure: `intersect(taxa_names(object1), taxa_names(object2))`. Note that if there are many taxa, you might want to save the result of `intersect()` as an object and query it in pieces, or its `length()`, rather than fill up your terminal with OTU identifiers.

# Save Data Object(s)

You’ve got an object you want to use for later analysis (or many of them!); AND it is well-organized and self-describing with a tree, sequences, taxonomy, sample data, and of course the counts of each OTU in each sample (OTU table). Before doing further analysis, let’s end this session by saving our hard-earned object as a compressed, serialized R object. These objects

1. Are usually very compact and use less file space than the original input files, and
2. load into R very quickly, so that you don’t need to re-parse the files every time you want to come back to an analysis, or section of an analysis.

Save the file as mp-phyloseq-lab.RDS, using the `saveRDS()` function.

### Advanced

1. Write a single R command that will save each of the intermediate files as separate RDS objects. Use the `mapply()` function to loop over two vectors vectors simultaneously, one with the objects (a list) and the other a character vector of the file names you will store.
2. There is more than one way to do this, but using `mget()` and `paste0()` functions to store a list of the 4 `mp*` objects we created is a helpful fist step.

### Note: save.image()

If you want to store many objects as one file, or you want to store all the objects at the end of your session, then you may find that `save()` or `save.image()` to be easier to use than `saveRDS()`. The files are usually given the extension `.RData` instead of `.RDS`, and the reverse operation (loading the stored data into R session) is accomplished with the `load()` function, rather than `readRDS()`.

Try for yourself!

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