Gene set enrichment analysis made simple

Irizarry, Rafael A.; Wang, Chi; Zhou, Yun; Speed, Terence P.

doi:10.1177/0962280209351908

Cited by 171 publications

(200 citation statements)

References 18 publications

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“…20 Since genes are far from equiprobable in gene sets, the adequate null hypothesis for g versus v gives genes with probabilities proportional to their frequency in the gene set database 55 and requires the KS, T-, and W-tests with frequency-corrected null hypotheses (see refs. [56][57][58] for review). In addition, scoring the enrichment of the »1500 KEGG and GO gene sets in >1,000 samples would mean computing »1.5 million tests with a high accuracy for the most significant results.…”

Section: Sample Enrichment Scores (Ses)mentioning

confidence: 99%

Large-scale microarray profiling reveals four stages of immune escape in non-Hodgkin lymphomas

et al. 2016

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Non-Hodgkin B-cell lymphoma (B-NHL) are aggressive lymphoid malignancies that develop in patients due to oncogenic activation, chemo-resistance, and immune evasion. Tumor biopsies show that B-NHL frequently uses several immune escape strategies, which has hindered the development of checkpoint blockade immunotherapies in these diseases. To gain a better understanding of B-NHL immune editing, we hypothesized that the transcriptional hallmarks of immune escape associated with these diseases could be identified from the meta-analysis of large series of microarrays from B-NHL biopsies. Thus, 1446 transcriptome microarrays from seven types of B-NHL were downloaded and assembled from 33 public Gene Expression Omnibus (GEO) datasets, and a method for scoring the transcriptional hallmarks in single samples was developed. This approach was validated by matching scores to phenotypic hallmarks of B-NHL such as proliferation, signaling, metabolic activity, and leucocyte infiltration. Through this method, we observed a significant enrichment of 33 immune escape genes in most diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) samples, with fewer in mantle cell lymphoma (MCL) and marginal zone lymphoma (MZL) samples. Comparing these gene expression patterns with overall survival data evidenced four stages of cancer immune editing in B-NHL: non-immunogenic tumors (stage 1), immunogenic tumors without immune escape (stage 2), immunogenic tumors with immune escape (stage 3), and fully immuno-edited tumors (stage 4). This model complements the standard international prognostic indices for B-NHL and proposes that immune escape stages 3 and 4 (76% of the FL and DLBCL samples in this data set) identify patients relevant for checkpoint blockade immunotherapies.

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Section: Sample Enrichment Scores (Ses)mentioning

confidence: 99%

Large-scale microarray profiling reveals four stages of immune escape in non-Hodgkin lymphomas

et al. 2016

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“…Further, we applied several Enrichment Analysis methods, such as "reporter features" from Patil et al [7], Wilcoxon rank-sum test [8], and globaltest from Goeman et al [9], and noticed a clear benefit from the increased gene coverage. Additionally, as most of the Gene Set Enrichment methods have a lower threshold on the number of genes in the set based on the underlying statistics [8], a number of sets had to be filtered out when microarray data processed with the original annotation.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, as most of the Gene Set Enrichment methods have a lower threshold on the number of genes in the set based on the underlying statistics [8], a number of sets had to be filtered out when microarray data processed with the original annotation. The improved annotation, on the other hand, allowed to keep a fraction of these genes for the analysis, as in this case they satisfied the gene coverage requirements.…”

Section: Resultsmentioning

confidence: 99%

A Tool to Build Up-To-Date Gene Annotations for Affymetrix Microarrays

Milchevskaya¹,

Tödt²,

Gibson³

2017

Genomics Comput Biol

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Genome-wide expression profiling and genotyping is widely applied in functional genomics research, ranging from stem cell studies to cancer, in drug response studies, and in clinical diagnostics. The Affymetrix GeneChip microarrays represent the most popular platform for such assays. Nevertheless, due to rapid and continuous improvement of the knowledge about the genome, the definition of many of the genes and transcripts change, and new genes are discovered. Thus the original probe information is out-dated for a number of Affymetrix platforms, and needs to be re-defined. It has been demonstrated, that accurate probe set definition improves both coverage of the gene expression analysis and its statistical power.Therefore we developed a method that incorporates the most recent genome annotations into the annotation of the microarray probe sets, using tools from the next generation sequencing. Additionally our method allows to quickly build project specific gene annotation models, as well as for comparison of microarray to RNAseq data.

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“…The GSEA method went a slight revision Subramanian et al [14], where ad-hoc modifications are implemented that are supposed to countervail the well-known lack of sensitivity of the KS test [15,16].…”

Section: Esmentioning

confidence: 99%

Gene-Category Analysis

Bauer¹

2016

Methods in Molecular Biology

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Gene-category analysis is one important knowledge integration approach in biomedical sciences that combines knowledge bases such as Gene Ontology with lists of genes or their products, which are often the result of high-throughput experiments, gained from either wet-lab or synthetic experiments. In this chapter, we will motivate this class of analyses and describe an often used variant that is based on Fisher's exact test. We show that this approach has some problems in the context of Gene Ontology of which users should be aware. We then describe some more recent algorithms that try to address some of the shortcomings of the standard approach.

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Gene set enrichment analysis made simple

Cited by 171 publications

References 18 publications

Large-scale microarray profiling reveals four stages of immune escape in non-Hodgkin lymphomas

Large-scale microarray profiling reveals four stages of immune escape in non-Hodgkin lymphomas

A Tool to Build Up-To-Date Gene Annotations for Affymetrix Microarrays

Gene-Category Analysis

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