Coder Social home page Coder Social logo

Comments (3)

Eisuan avatar Eisuan commented on June 24, 2024

Dear user,
in order to help you with your requests, I'll require additional information:

  1. How did you generate your list of target genes? Are those putative target genes of ligands (based on NicheNet predictions)?
  2. Is your data bulk-RNA seq or sc?

Unfortunately, we do not provide enrichment procedures based on target genes or predicted ligands in our package yet.

from nichenetr.

pariaaliour avatar pariaaliour commented on June 24, 2024

Thanks for your reply,
My data is single nucleus dataset. I followed the sender-focused approach. And the code I used to generate the target genes was:

active_ligand_target_links_df = best_upstream_ligands %>% lapply(get_weighted_ligand_target_links, geneset = geneset_oi, ligand_target_matrix = ligand_target_matrix, n = 200) %>% bind_rows() %>% drop_na()
active_ligand_target_links = prepare_ligand_target_visualization(ligand_target_df = active_ligand_target_links_df, ligand_target_matrix = ligand_target_matrix, cutoff = 0.33)

Moreover, if there is no function for enrichment analysis do you recommend any way to do so out of nichenetr (as you had one case in tutorial that target genes are the genes of a specific pathway)?
I appreciate your help on this!
Paria

from nichenetr.

Eisuan avatar Eisuan commented on June 24, 2024

Dear Paria,
In this case, I suggest utilising an over-representation analysis approach (enrichment procedures based on Fisher's exact test / hypergeometric distribution). This kind of approach requires the definition of a gene universe, which is the set of all the possible genes that might have been chosen as "interesting ones" or "not interesting ones" (e.g. in differential expression analysis, the former genes are those "differentially expressed" and the latter ones are "not differentially expressed").
In your case, your universe set is composed of all the target genes described in the ligand-target matrix.

To perform the analysis, you can use TopGO since it conveniently allows you to adjust for the gene universe. This package also supports enrichment procedures that leverage limitations that could arise if you use hierarchical structured annotations, such as GO-terms (e.g. parent-child terms relationships and information redundancy. Please, refer to Alexa 2006 for more info). I can not help suggesting specific sources for pathway annotations, since their quality might be subjective.

Best regards,
Daniele

from nichenetr.

Related Issues (20)

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.