Skip to content

assignment_3

Jump to bottom
dxjasmine edited this page Apr 3, 2020 · 3 revisions

Objective

Time estimated: 2 days; taken 5 days; date started: 2020-03-29; date completed: 2020-04-02

  • Perform a non-thresholded pathway analysis and compare it with the threshold analysis result.
  • Use the Cytoscape and enrichment map pipeline to visualize the results.

Procedures

Non-thresholded Gene set Enrichment Analysis

  1. get rank file from assignment 2 (lecture 6 reference to generate rank file): gene name and rank, rnk file
  2. get genesets from the baderlab geneset collection from February 1, 2020 containing GO biological process, no IEA and pathways.
  3. load two files into GSEA
  4. go to "RunGSEAPreranked"
  5. Select bader lab genesets and rank file; set max gene set = 200, min genset = 15; set no collapse;
  6. results in output file

Visualize Gene set Enrichment Analysis in Cytoscape

  1. cytoscape app add enrichment map pipeline
  2. create enrichment map with GSEA result: app -> enrichment map;
  3. select the output folder for GSEA result: set FDE q to 0.1
  4. app -> auto-annotate
  5. select the clusters >4 terms: create a new network from selected nodes
  6. manually move the clusters to create a publish-ready figure
  7. collapse all into a theme network

Post Analysis

  1. download wnt pathway WP428
  2. annotate it with rank file

Results

Non-thresholded Gene set Enrichment Analysis

  • the top genesets are D loop structure, DNA damage and repair, carcinoma mutations, trascription factor in wnt pathways, etc. Most of them are highly related to DNA damage, repair and wnt pathway. This is consistant with the original result in the paper that is interested in the impact of G9a in wnt pathway and if this is a tumor-related factor.

Visualize Gene set Enrichment Analysis in Cytoscape

  • The biggest clusters

    • proteasome degradation mediated

    • aerobic electron cytochrome

    • trna ribonucleoprotein nucleus

  • These themes are consistant with Ga9 function and hypothesis mentioned in the original paper.

  • Most results from enrichment map are consistant with the result from assignment2. Both of the result indicates the cell cycle control, gene damage and repair. However, the reuslts from enrichment map is more detailed compared to results from Gprofile. We can see viral budding viron, protein membrane processing and other special stage in cell cycle while the these are omited in gprofile results.

Conclusion

The result is partially consistant with thresholded analysis using Gprofile. Both of the result indicates the cell cycle control, gene damage and repair. Most of GSEA results are more detailed and specific to metabolic process. Thresholded analysis is more arbitary considering which thresholded to be used, while GSEA results gives a more specific result based on significance.

Note and Reference

Zhang, K., Wang, J., Yang, L., Yuan, Y.-C., Tong, T. R., Wu, J., … Raz, D. J. (2018). Targeting histone methyltransferase G9a inhibits growth and Wnt signaling pathway by epigenetically regulating HP1α and APC2 gene expression in non-small cell lung cancer. Molecular Cancer, 17(1), 153. https://doi.org/10.1186/s12943-018-0896-8

Kucera, M., Isserlin, R., Arkhangorodsky, A., & Bader, G. D. (2016). AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations [version 1; referees: 2 approved]. F1000Research, 5. https://doi.org/10.12688/F1000RESEARCH.9090.1

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., … Ideker, T. (2003). Cytoscape: A software Environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303

Rao, V. K., Ow, J. R., Shankar, S. R., Bharathy, N., Manikandan, J., Wang, Y., & Taneja, R. (2016). G9a promotes proliferation and inhibits cell cycle exit during myogenic differentiation. Nucleic Acids Research, 44(17), 8129–8143. https://doi.org/10.1093/nar/gkw483

Clone this wiki locally