GOmixer is an online tool to compare and visualize gut meta-omics data with minimal effort and input. This help section is a brief tutorial on how to use the different features of GOmixer.
In this tutorial you will learn how to navigate GOmixer using the job viewer, how to format data matrices to make them compatible with GOmixer, and what are the different options to perform a comparative analysis
The job viewer
Every user action in GOmixer is submitted as a background job. The job viewer lists and monitor all these jobs. Upon job completion the viewer lists the possible actions that could be performed on the job result. In the example below, the first job in the list is still running and therefore its action list is empty. The second job is finished, the action will view its result online while will download the result. For job number 3, will view the dataset, will start a metabolic modules inference, and will start a comparative analysis using the loaded dataset.
The data matrix should be tab delimited (i.e. in Excel, use export as cvs and select the TAB separator) and should always contain a unique id as a first column. The second column could contain taxonomic information (optional field), the third (second if taxon information is omitted) should contain KO annotation, and the rest is sample abundances
|id(a free field)||taxon(optional)||KEGG ortholog||Sample1||Sample2||..||sampleN|
The matrix could be pre-processed (scaled, rarefied, ...), however GOmixer offers scaling or proportioning in case of raw data.
The metadata matrix should be tab delimited and should have sample names as a header and metadata in rows. Sample names should match the sample names of the data matrix, otherwise the dataset will not load.
The above figure describes the flow of a comparative data analysis in GOmixer. Once a dataset is loaded into GOmixer, for each sample of the dataset, GOmixer first quantifies metabolic modules. The resulting modules are then compared for over/under-representation, between two user-selected groups of samples, using Wicloxon's rank-sum test. After that, the p-values are adjusted using Benjamini-Hochberg’s false discovery rate (FDR). Finally, the modules that are below the user-defined cutoff for FDR are plotted on metabolic maps, and species-function association is displayed on a chord plot, if taxonomic information is provided.
Arrows in blue indicate the possible I/O options for external data analysis.