Author: Sandi Yen
Contributors: Nick Ilott, Jethro Johnson
Updated: 11 May 2020

About OCMSlooksy

OCMSlooksy is a visualization tool designed for 16S rRNA gene sequence data. It is a R Shiny app that is distributed as a R package. Even though this tool was created as an extension of the 16S rRNA gene analysis pipeline within OCMS, 16S data generated from other pipelines may be used, given that the input file formats are compatible.

Installing OCMSlooksy

The app is still under development, but it can be installed from the github page. To install OCMSlooksy, use the following in your R console:

devtools::install_local("path/to/OCMSlooksy-30-07-2020.tar.gz")

Launching OCMSlooksy

The app is launched from your R console by:

library(OCMSlooksy)
run_app()

This will open the app in your web browser. If pop-ups have been blocked by your browser, you may have to allow them for the app.

Using OCMSlooksy

The app is laid out such that all major tasks in the analysis process are shown across the top, where each task is given its own tab. Sub-tasks are shown in the sidebar on the left.

Import: Uploading your data

There is an example dataset available in the Import tab (toggle the Example data switch to enable/disable). When enabled, you can browser through all the entire app using this dataset.

As part of the OCMS 16S analysis pipeline, the count and taxonomy tables are exported as a SQLite database file called csvdb. This file is uploaded as the Database file. The upload may take a few minutes if the dataset contains several hundred samples. If your raw 16S data was not processed by the OCMS pipeline, there is a helper function in the OCMSlooksy package that creates a SQLite database file from either R dataframes or from csv or tsv files:

OCMSlooksy::create_db(counts, taxonomy, overwrite = FALSE, outdir, fromfile = FALSE)

# for details on function usage
?OCMSlooksy::create_db

The format of the count table should be: features (ASVs or OTUs) in rows, in a column called featureID, and samples in columns.

+-----------+---------+---------+-----+
| featureID | sample1 | sample2 | ... |
+-----------+---------+---------+-----+
| ASV1      | ...     | ...     | ... |
+-----------+---------+---------+-----+
| ...       | ...     | ...     | ... |

The format of the taxonomy table should have the columns as shown below. The column Taxon is a concatenation of all taxonomy levels (k_kingdom;p_phylum;c_class;o_order;f_family;g_genus;s_species).

+-----------+----------+--------+--------+-------+-------+--------+-------+---------+-------+
| featureID | sequence |Kingdom | Phylum | Class | Order | Family | Genus | Species | Taxon |
+-----------+----------+--------+--------+-------+-------+--------+-------+---------+-------+
| ASV1      | ATG...   | ...    | ...    | ...   | ...   | ...    | ...   | ...     | ...   |
+-----------+----------+--------+--------+-------+-------+--------+-------+---------+-------+

The metedata associated with your data is imported from a csv or tsv file. The only restriction on the format of the metadata file is that samples are in rows, in a column called sampleID. Sample identifiers must be unique and must correspond with the sample columns found in your count table. All columns should have a header.

Once the files have been uploaded, click the Launch Data button to submit. Preview of the uploaded data is available.