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I would like to find out how to combine gene set enrichment analysis with hierarchical clustering. The motivation for this combination is that potentially too many gene-set symbols for leukemia may pass the p-value significance threshold. We are using an R-package for fast preranked gene set enrichment analysis (GSEA) with the URL, https://github.com/ctlab/fgsea.

Subsequent to the fast gene set enrichment analysis on a ranked list of gene symbols with different expression t-statistics, we specifically need to identify gene-set symbols belonging to a common functional group using the R language hclust function intended for supervised hierarchical clustering with a Euclidean distance between features in a timely manner, for example 15 or fewer minutes.

Quoting from Alan Moses 2017 book, "Statistical Modeling and Machine Learning for Molecular Biology" , "Clustering is meant for exploratory data analysis and therefore doesn't really have a strong framework for hypothesis testing".A new R package ClusterProfiler provides enrichment analysis of gene clusters as reported in this URL, http://guangchuangyu.github.io/2015/05/use-clusterprofiler-as-an-universal-enrichment-analysis-tool/

The fact that most quality measures found in the literature have been conceived to evaluate non-overlapping clusterings, even when most real-life problems are better modeled using overlapping clustering algorithms is analyzed in detail in the following paper and University of Texas Ph.D thesis

Academic paper: CICE-BCubed: A New Evaluation Measure for Overlapping Clustering Algorithms. Available from: https://www.researchgate.net/publication/260421976_CICE-BCubed_A_New_Evaluation_Measure_for_Overlapping_Clustering_Algorithms [accessed Apr 16, 2017].

and

https://repositories.lib.utexas.edu/bitstream/handle/2152/ETD-UT-2010-08-2022/KRUMPELMAN-DISSERTATION.pdf?sequence=1&isAllowed=y

As I am a novice to this type of bioinformatics research, please correct any inaccuracies in my problem statement.

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  • $\begingroup$ The article, What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm with the URL, journals.plos.org/plosone/article?id=10.1371/… discusses MAP-DP which is a more flexible clustering technique using a probabilistic model. $\endgroup$
    – Frank
    Commented Apr 17, 2017 at 8:51

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You can try Enrichr for enrichment analysis. On 'Results' page you can use Clustergram view. Or you can upload your results to Clustergrammer itself.

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  • $\begingroup$ Thank you for your nice answer. Is it possible to measure and refine the quality of hierarchical clustering results generated by the R language function hclust() using Euclidean distance by automatically computing p-values for all clusters contained in the clustering of original data. Could we use the paper "Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression", journals.plos.org/ploscompbiol/article?id=10.1371/… to refine clustering analysis? Thank you. $\endgroup$
    – Frank
    Commented Apr 16, 2017 at 19:09
  • $\begingroup$ Is it mathematically correct to use the R function for combining gene set enrichment analysis p-values from github.com/ctlab/fgsea. in this URL, biostars.org/p/211307, to calculate the quality of a hclust() clustering result tree? Thank you. I am awarding reputation points to your nice answer. $\endgroup$
    – Frank
    Commented Apr 16, 2017 at 19:15
  • $\begingroup$ How does your answer address overlapping clustering? Thank you. $\endgroup$
    – Frank
    Commented Apr 17, 2017 at 3:48
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A rising young M.I.T professor told me just now that the volcano plot with 1og(p value) vertical axis can in many cases differentially visualize overlapping clusters where p values come from gene set enrichment analysis,

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