Question: Threshold for centrality score during prediction of Hub genes in co expression network
0
gravatar for archie
13 months ago by
archie70
India
archie70 wrote:

Dear all,

I am working on analysis of weighted co-expression network. My next step is to measure network topology in terms of betweeness , closeness , degree . For this I am using the CytoNCA app within cytoscape. But I am just curious to know if I need to apply threshold value for betweeness , closeness , degree scores in order to call top ranked genes as hub genes of network.

waiting for reply

Archana

ADD COMMENTlink modified 13 months ago • written 13 months ago by archie70
1
gravatar for Kevin Blighe
13 months ago by
Kevin Blighe41k
Guy's Hospital, London
Kevin Blighe41k wrote:

Hi Archana,

I don't believe there are any pre-defined or standardised cut-offs due to the fact that network metrics can vary tremendously based on numerous other parameters, such as:

  • distance metric used for network construction (e.g. Euclidean distance, correlation, etc)
  • total number of nodes / vertices
  • any pre-filtering on edge values (e.g. removing weak edges)
  • the nature of the data, e.g., a network constructed from differentially expressed genes using 1 group of samples in which they were found to be differentially expressed will likely produce a 'stronger' network than one produced from randomly selected genes
  • whether you're plotting just a graph or a minimum spanning tree of the graph
  • et cetera

Also, these scores are presented differently in different studies. For example hub scores, closeness centrality, and betweenness centrality can either be presented as scaled to 0-1 or as 'raw' scores. Degree is obviously just degree..

Thus, your choice of threshold should be based on the rank of the scores. Higher scores obviously indicate a more important vertex. Once you take a look over the results, you'll get a feeling of where you should be setting thresholds.

Just my take.

Kevin

ADD COMMENTlink written 13 months ago by Kevin Blighe41k
1
gravatar for archie
13 months ago by
archie70
India
archie70 wrote:

Dear Kevin,

Thank you so much for detail explanation. I also looked into various articles where they have mentioned "TOP RANKED" . I will also select top ranked genes and perform further analysis.

ADD COMMENTlink written 13 months ago by archie70
1

I read somewhere; selecting hubs or bottleneck genes in range of 10%-40% of genes dose not have a much impact on the results.

ADD REPLYlink modified 13 months ago • written 13 months ago by F3.4k
1

Yes, that's a good general figure. I have been using >0.4 (>40%) in a recent experiment.

ADD REPLYlink written 13 months ago by Kevin Blighe41k
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