CorrMapper helps to explore, integrate and visualise the data of complex biological studies.
It requires one or two omics datasets, along with a metadata table, which holds
clinically or biologically relevant information about these samples.
Once all these files are uploaded, the metadata could be mapped onto the low-dimensional representation of the omics datasets in an interactive dashboard. This helps with sample stratification using the various clinical metadata variables.
After this initial exploration, CorrMapper offers a range of feature selection algorithms which will find the most discriminatory variables of the omics dataset(s) with respect to a chosen metadata variable, e.g.: cancer vs healthy.
Finally, the correlation network of these highly discriminatory variables are calculated and visualised in an interactive and novel way.
Have a look at the demos of CorrMapper's visualisation modules:
Get started by uploading your datasets, or an example dataset.