I have been given a dataset containing the following information: Species data was collected from Daphnia magna, the data consists of transcriptomic data (RNA-Seq) and polar metabolomic data (DIMS). This information was collected under 2 conditions, 1 = treatment with an unknown metal ion. 2 = treatment with an unknown metal nanoparticle. Data was collected over three time points; early (hours), intermediate, late (days). The experiment was repeated for multiple biological replicates. The task we have been given is to use statistics, machine learning, and bioinformatics knowledge to look for genes and metabolites that co-vary across conditions and in response to perturbations in an animal system.

The data provided is in tsv format and contains the following: - Information on genes monitored including gene ID, gene family information, and information on functions. - Similar information for metabolites including empirical information, functional information and peaks in measurements. - Two other tsv files describing the change in gene expression over time for each individual and condition and another for metabolite data describing the same changes. This is all the information and instruction we have been given, we have 3 days to analyse the dataset. Could anyone suggest where to start looking for a method to start analysing this data? As we have covered limited machine learning, so our knowledge is limited at this time. Any advice on methods / resources would be greatly appreciated.