Urgent need for consistent standards in functional enrichment analysis

Wijesooriya, Kaumadi; Jadaan, Sameer A.; Perera, Kaushalya L.; Kaur, Tanuveer; Ziemann, Mark

doi:10.1371/journal.pcbi.1009935

Cited by 66 publications

(78 citation statements)

References 43 publications

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“…Other authors reported potential problems of functional enrichment analysis [17][18][19][20][21] and described best practices in the past [22,23], but we believe that our guidelines are easier to follow and to understand by all users, including students and beginners.…”

Section: Introductionmentioning

confidence: 99%

Nine quick tips for pathway enrichment analysis

Chicco

Agapito

2022

PLoS Comput Biol

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Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.

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Section: Introductionmentioning

confidence: 99%

Nine quick tips for pathway enrichment analysis

Chicco

Agapito

2022

PLoS Comput Biol

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“…Combined with similarity measures, which can measure semantic overlap, such approaches can advance the interpretation biclustering results and method evaluations. Biological enrichment analysis can be sensitive to the exact content of biclusters and moreover can be affected by the enrichment test methodology and applied thresholds, for example (71). Therefore, GO enrichment results alone do not suffice to compare and evaluate biclustering methods and in fact may be misleading if not placed into a bicluster coherence context.…”

Section: Discussionmentioning

confidence: 99%

Deep surveys of transcriptional modules with Massive Associative K-biclustering (MAK)

Joachimiak

Tuglus

Salamzade

et al. 2022

Preprint

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Biclustering can reveal functional patterns in common biological data such as gene expression. Biclusters are ordered submatrices of a larger matrix that represent coherent data patterns. A critical requirement for biclusters is high coherence across a subset of columns, where coherence is defined as a fit to a mathematical model of similarity or correlation. Biclustering, though powerful, is NP-hard, and existing biclustering methods implement a wide variety of approximations to achieve tractable solutions for real world datasets. High bicluster coherence becomes more computationally expensive to achieve with high dimensional data, due to the search space size and because the number, size, and overlap of biclusters tends to increase. This complicates an already difficult problem and leads existing methods to find smaller, less coherent biclusters. Our unsupervised Massive Associative K-biclustering (MAK) approach corrects this size bias while preserving high bicluster coherence both on simulated datasets with known ground truth and on real world data without, where we apply a new measure to evaluate biclustering. Moreover, MAK jointly maximizes bicluster coherence with biological enrichment and finds the most enriched biological functions. Another long-standing problem with these methods is the overwhelming data signal related to ribosomal functions and protein production, which can drown out signals for less common but therefore more interesting functions. MAK reports the second-most enriched non-protein production functions, with higher bicluster coherence and arrayed across a large number of biclusters, demonstrating its ability to alleviate this biological bias and thus reflect the mediation of multiple biological processes rather than recruitment of processes to a small number of major cell activities. Finally, compared to the union of results from 11 top biclustering methods, MAK finds 21 novel S. cerevisiae biclusters. MAK can generate high quality biclusters in large biological datasets, including simultaneous integration of up to four distinct biological data types.

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“…Genes whose classi cation as hypo-or hyper-variable con rmed by cross-validation were additionally analysed for over-representation using the ConsensusPathDB (Kamburov, Stelzl et al 2012). According to best practice for pathway/functional analyses (e.g., the over-representation analysis used in this work), a gene set is compared to a given list of total genes measured in an experiment, called background (Wijesooriya, Jadaan et al 2022). As such, gene lists per radiation dose after exclusion of bimodally expressed genes were used (Additional File 3).…”

Section: Gene Ontology Over-representation Analysismentioning

confidence: 99%

Computation and comparison of gene expression variability in fibroblasts of long-term survivors of childhood cancer and cancer-free controls in response to ionizing and sham-irradiation: the KiKme study

Grandt

Brackmann

Foraita

et al. 2022

Preprint

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Background: Differential expression analysis is usually adjusted for variation. However, most studies that examined the expression variability (EV) have used computations affected by low expression levels and did not examine healthy tissue. This study aims to calculate and characterize an unbiased EV in primary fibroblasts of childhood cancer survivors and cancer-free controls (N0) in response to ionizing radiation. Methods: Human skin fibroblasts of 52 donors with a first primary neoplasm in childhood (N1), 52 donors with at least one second primary neoplasm (N2+), as well as 52 N0 were obtained from the KiKme case-control study and exposed to a high (2 Gray) and a low dose (0.05 Gray) of X-rays and sham- irradiation (0 Gray). Genes were then classified as hypo-, non-, or hyper-variable per donor group and radiation treatment, and subsequently examined for enriched functional signatures. Results: We found 22 genes with considerable EV differences between donor groups, of which 11 genes were associated with response to ionizing radiation, stress, and DNA repair. The largest number of genes exclusive to one donor group and variability classification combination were all detected in N0: hypo-variable genes after 0 Gray (n=49), 0.05 Gray (n=41), and 2 Gray (n=38), as well as hyper-variable genes after any dose (n=43). After 2 Gray GO term groups contained the Notch signalling pathway, subsuming cellular response to oxidative stress, cell death in response to hydrogen peroxide, DNA damage response, and detection of DNA damage. In N2+, 30 genes were uniquely classified as hyper-variable after the low dose, and were associated with inflammatory functions. For N1, no exclusive gene sets with functions related to the radiation response were detected in our data. Conclusion: Only in N0, the handling of genotoxic and oxidative stress after a high dose of ionizing radiation may be tightly regulated and thus more efficient, whereas, after a low dose, only N2+ showed high degrees of variability in inflammatory pathways that can be a result of radiation-induced reactive oxygen species, potentially leading to a higher vulnerability towards side effects of exposure to high doses of ionizing radiation.

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Urgent need for consistent standards in functional enrichment analysis

Cited by 66 publications

References 43 publications

Nine quick tips for pathway enrichment analysis

Nine quick tips for pathway enrichment analysis

Deep surveys of transcriptional modules with Massive Associative K-biclustering (MAK)

Computation and comparison of gene expression variability in fibroblasts of long-term survivors of childhood cancer and cancer-free controls in response to ionizing and sham-irradiation: the KiKme study

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