GSA Time-series STEM GSEA SEA timeClip TcGSA phantom FUNNEL Java R

Gene set analysis for time-series

Posted by Xiaowei Huang on March 15, 2022

There are many tools to perform gene set analysis on time-series data. I have reviewed seven of them and here I share some quick tutorials that I have written as interactive jupyter notebooks.

Jupyter tutorials:

The tutorials can be found here:

  1. STEM

  2. GSEA for time series

  3. SEA –maSigFun, PCA-maSigFun, and ASCA-functional–

  4. timeClip

  5. TcGSA

  6. PHANTOM

  7. FUNNEL-GSEA

References:

[1] ERNST, J., et al. (2006), STEM: a tool for the analysis of short time series gene expression data, BMC Bioinformatics 7, 191, https://doi.org/10.1186/1471-2105-7-191

[2] Gene Set Enrichment Analysis (GSEA) User Guide, v.3.0.0, access date: 2020-06-06, https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?_Interpreting_GSEA_Results

[3] NUEDA, M., et al. (2009), Functional assessment of time course microarray data, BMC Bioinformatics 10(Suppl 6):S9, https://doi.org/10.1186/1471-2105-10-S6-S9

[4] MARTINI, P., et al. (2014), timeClip: pathway analysis for time course data without replicates, BMC Bioinformatics 15, S3, https://doi.org/10.1186/1471-2105-15-S5-S3

[5] HEJBLUM, B., et al. (2015), Time-Course Gene Set Analysis for Longitudinal Gene Expression Data, PLoS Comput Biol 11(6): e1004310, https://doi.org/10.1371/journal.pcbi.1004310

[6] GU, J., et al. (2017), Phantom: investigating heterogeneous gene sets in time-course data, Bioinformatics 33, 18, https://doi.org/10.1093/bioinformatics/btx348

[7] ZHANG, Y., et al. (2017), FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis, Bioinformatics 33, 13, https://doi.org/10.1093/bioinformatics/btx104

FRIENDS