An interactive web tool for evaluating and comparing associations of clinical variables and microbiome composition


This interactive online tool allows:
(1) Running multiple multivariate linear regression models, on the same features and metadata.
(2) Visualizing the associations between microbial features and clinical metadata found in each model.
(3) Comparing across the various models to identify the critical metadata variables and select the optimal model.

Upload input file

Note: The file should be a separator based file including all relevant data: clinical metadata variables and taxonomic features abundance for each sample (csv, tsv etc.). Acceptable separators are any one of tab, comma and semicolon. The first row is assumed to be the header containing names of the columns. Once you upload your file, 'Submit' will take you to the next step of analysis. If there is any problem with parsing the file, the error will be presented here. Once you fix the problem, try to reload the file. Use ':' to separate the heavy and light chains. The heavy chain should come first!

Run an example

Clicking on the Load example data button will load a case study data as an example. In this case study, we considered six clinical variables that were collected in our cohort, and are also known to have an impact on gut microbiome composition: mode of delivery (vaginal or C-section), age (at time of visit), ethnicity, use of probiotics in the first year of life, infant diet at each time point (breastfed, formula-fed, mixed), and infante allergy status (case/control). In this study we searched for microbial features that are associated with the allergy status, taking into account all other clinical variables. For more details regarding this case study please check our paper.

Link to source code: Available at GitHub
If you use EasyMap please cite: Ehud Dahan, Victoria M. Martin, Moran Yassour. EasyMap - An Interactive Web Tool for Evaluating and Comparing Associations of Clinical Variables and Microbiome Composition Front. Cell. Infect. Microbiol. 12:854164.
Contact: Moranya {at} mail.huji.ac.il
Acknowledgement: We are grateful for the support of the Center for Interdisciplinary Data Science Research (CIDR) at the Hebrew University of Jerusalem. In particular, we would like to thank Haimasree Bhattacharya from CIDR for her contributions.