Answers.Body: <p>This question can be addressed by gene set enrichment analysis. You should use Gene Ontology and KEGG Pathways to find out which genes from your gene set are enriched in GO terms and KEGG pathway terms. You can use <a href="https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html" rel="nofollow noreferrer">clusterProfiler</a> package in R for this anlysis and for visualization also. Here is the link to the guide:
  <a href="https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html" rel="nofollow noreferrer">https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-08-13T12:37:19.343Z
Answers.LastActivityDate: 2022-08-14T19:15:13.763Z
Answers.LastEditDate: 2022-08-14T19:15:13.763Z
Answers.UserDisplayName: Nitesh Shriwash
Body: <p>I have a list of genes (n = 120) that are involved in breast cancer. And I have a list of differentially expressed genes (n = 70) that are present in the breast cancer gene list. My question is: Are differentially expressed genes significantly more likely to be breast cancer related vs non-differentially expressed genes?
  which statistical test will be suitable to address this question and how I can visualize its results in <code>R</code>.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-06-14T15:47:04.373Z
Comments.Text: I would suggest using an existing tool to evaluate this issue, such as the GO enrichment analysis toolkit: http://geneontology.org/docs/go-enrichment-analysis/. Some of the details are finicky, there are a lot of people who have thought very hard about the issue of annotating genes and GO is what people have settled on, rather than a bespoke list of BC genes. If you must visualize this (rather than simply report the numbers or have a table of enriched categories, which I'd prefer), I'd use a Venn diagram, which you can google for R.
Comments.UserDisplayName: Maximilian Press
Tags: <rna-seq><r><visualization>
Title: statistical test and visualization of genes present in an experiment
ThreadText: # statistical test and visualization of genes present in an experiment
  
  (Asked by: username_0 on 2022-06-13)
  
  I have a list of genes (n = 120) that are involved in breast cancer. And I have a list of differentially expressed genes (n = 70) that are present in the breast cancer gene list. My question is: Are differentially expressed genes significantly more likely to be breast cancer related vs non-differentially expressed genes?
  which statistical test will be suitable to address this question and how I can visualize its results in `R`.
  
  **Tags:** rna-seq, r, visualization
  
  > **Comments:**
  > **username_1**: I would suggest using an existing tool to evaluate this issue, such as the GO enrichment analysis toolkit: http://geneontology.org/docs/go-enrichment-analysis/. Some of the details are finicky, there are a lot of people who have thought very hard about the issue of annotating genes and GO is what people have settled on, rather than a bespoke list of BC genes. If you must visualize this (rather than simply report the numbers or have a table of enriched categories, which I'd prefer), I'd use a Venn diagram, which you can google for R.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2022-08-13. Score: 1
  
  This question can be addressed by gene set enrichment analysis. You should use Gene Ontology and KEGG Pathways to find out which genes from your gene set are enriched in GO terms and KEGG pathway terms. You can use [clusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) package in R for this anlysis and for visualization also. Here is the link to the guide:
  <https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html>
  
  
  ---
  
  