Question: Multi omics dataset analyses, where to start? And methods to use?
2
gravatar for petebrotherwood
6 weeks ago by
petebrotherwood30 wrote:

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.

ADD COMMENTlink modified 6 weeks ago by bbmisraccb60 • written 6 weeks ago by petebrotherwood30

Since you mention metabolomics, you can also have a look at this recent paper: https://www.mdpi.com/2218-1989/9/10/200

ADD REPLYlink written 4 weeks ago by Egon Willighagen5.2k
1
gravatar for bbmisraccb
6 weeks ago by
bbmisraccb60
United States
bbmisraccb60 wrote:

Hi,

In our Review entitled: "Integrated omics: tools, advances and future approaches: https://jme.bioscientifica.com/view/journals/jme/62/1/JME-18-0055.xml we have provided the list of ALL multi-omics/ integrated omics tools "Table 1: List of various tools, software, statistical approaches and databases available for integrated –omics approaches." and can be a starting point to find those tools.

For transcripts ad metabolites (dual omics!) you can have something very simple like:

IMPALA:impala.molgen.mpg.de (their paper:https://www.ncbi.nlm.nih.gov/pubmed/21893519) Omics Integrator/ Steiner Net is another web-based platform for transcripts + metabolites integration (if you have LC-MS raw metabolomics data) : http://fraenkel-nsf.csbi.mit.edu/omicsintegrator/ (paper: https://www.ncbi.nlm.nih.gov/pubmed/22638579)

Paintomics (http://www.paintomics.org/) and 3Omics (https://3omics.cmdm.tw/) (in literature these are the 2 most cited multi-omics tools, with 250+ and 80+ citations, also do it fine.

Currently, the leading one and the one that is getting popular is: mixOmics: http://mixomics.org/ But very little citations to back up their popularity, though soon would be many!

WGCNA claims to do it as well- I am not entirely convinced.

Currently, problem is, if you see Table 1 of the first paper, "a lot of tools, but hardly any one being used multiple times/ tested by the community".

Sorry, but nothing else I have comes across that handles "time course and multi-omics/ integration" together, and unfortunately so, and hence there is a window to develop such tools using ML/ DL! Also, even for single-omics the time-course tools are a hopeless story. : )

Hope it helps (or not!).

Thanks, Biswa

ADD COMMENTlink written 6 weeks ago by bbmisraccb60
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