Question: Methods to interrogate RNA-seq
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gravatar for telroyjatter
4 weeks ago by
telroyjatter150
telroyjatter150 wrote:

Let's say you have an RNA-seq counts matrix for two experimental groups, and you have already generated your table of differentially expressed genes. Generally speaking, what are the best ways to interrogate this dataset?

The ones I can think of:

  • Gene ontology enrichment
  • WGCNA and/or MEGENA for co-expression networks: modules; eigengenes, eigengene networks
  • ARACNE for transcription factor-target networks
  • Key driver analysis (i.e., which hub gene in WGCNA/MEGENA/ARACNE has the highest connectivity; what other ways are there to define key drivers?)
  • HOMER for regulatory binding sites

Any other methods, reviews, papers are highly appreciated!

rna-seq • 179 views
ADD COMMENTlink modified 29 days ago by Kevin Blighe66k • written 4 weeks ago by telroyjatter150
1

The first thing is "what is your biological question?" Every analysis depends on that response.

ADD REPLYlink written 4 weeks ago by JC11k

Let's say the tissue is from brain, and we want to understand how the administration of a drug affects gene expression.

ADD REPLYlink written 4 weeks ago by telroyjatter150

already generated ... table of differentially expressed genes

So you have a list of genes and are looking for biological meaning in this list?

ADD REPLYlink written 4 weeks ago by RamRS30k

Or in the entire dataset, for example some methods include an entire counts matrix rather than just a list of DEGs or the counts of only DEGs.

ADD REPLYlink written 4 weeks ago by telroyjatter150

Echoing JC here: if you're only thinking of a question after having generated the data, things have been done backwards.

Why is the list of methods you've listed not sufficient to get you started?

ADD REPLYlink written 4 weeks ago by Friederike6.4k

It is enough to get started. I don't even have data. I just want to create a list of all of the methods, with their respective applications, that can be used to mine, interrogate, and extract knowledge from an RNA-seq dataset (or *omics datasets more generally).

ADD REPLYlink written 4 weeks ago by telroyjatter150
0
gravatar for Kevin Blighe
29 days ago by
Kevin Blighe66k
Kevin Blighe66k wrote:

Perhaps you can obtain some insight from my answer, here: What is the best way to combine machine learning algorithms for feature selection such as Variable importance in Random Forest with differential expression analysis?

Kevin

ADD COMMENTlink written 29 days ago by Kevin Blighe66k
1

Yes this is helpful. Thanks, Kevin!

ADD REPLYlink written 28 days ago by telroyjatter150
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