Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies

Nilsson, Björn; Håkansson, Petra; Johansson, Mikael; Nelander, Sven; Fioretos, Thoas

doi:10.1186/gb-2007-8-5-r74

Cited by 21 publications

(28 citation statements)

References 22 publications

(29 reference statements)

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“…This was highlighted by visualizing the score for each combination of signal representing subset size and signal magnitude separately, like in previous work [7]. Each result is averaged over tested class sizes and standard deviations of the signal.…”

Section: Resultsmentioning

confidence: 99%

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Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function

2009

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BackgroundA central task in contemporary biosciences is the identification of biological processes showing response in genome-wide differential gene expression experiments. Two types of analysis are common. Either, one generates an ordered list based on the differential expression values of the probed genes and examines the tail areas of the list for over-representation of various functional classes. Alternatively, one monitors the average differential expression level of genes belonging to a given functional class. So far these two types of method have not been combined.ResultsWe introduce a scoring function, Gene Set Z-score (GSZ), for the analysis of functional class over-representation that combines two previous analysis methods. GSZ encompasses popular functions such as correlation, hypergeometric test, Max-Mean and Random Sets as limiting cases. GSZ is stable against changes in class size as well as across different positions of the analysed gene list in tests with randomized data. GSZ shows the best overall performance in a detailed comparison to popular functions using artificial data. Likewise, GSZ stands out in a cross-validation of methods using split real data. A comparison of empirical p-values further shows a strong difference in favour of GSZ, which clearly reports better p-values for top classes than the other methods. Furthermore, GSZ detects relevant biological themes that are missed by the other methods. These observations also hold when comparing GSZ with popular program packages.ConclusionGSZ and improved versions of earlier methods are a useful contribution to the analysis of differential gene expression. The methods and supplementary material are available from the website http://ekhidna.biocenter.helsinki.fi/users/petri/public/GSZ/GSZscore.html.

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Section: Resultsmentioning

confidence: 99%

“…This has motivated many authors to propose threshold free gene set analysis methods (also called as Gene Set Enrichment Analysis [5] and Threshold free ontological analysis [7]). These methods monitor differential gene expression at the class level.…”

Section: Introductionmentioning

confidence: 99%

Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function

2009

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“…An entirely different viewpoint on enrichment is explored in Breslin et al (2004), Barry et al (2005), Ben-Shaul et al (2005), Jiang and Gentleman (2007), and Nilsson et al (2007). The perspective is that the decisions made in determining which genes are interesting are somewhat arbitrary.…”

Section: Discussionmentioning

confidence: 97%

TreeHugger: A New Test for Enrichment of Gene Ontology Terms

Jupiter¹,

Şahutoğlu

VanBuren³

2010

INFORMS Journal on Computing

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T he Gene Ontology (GO) project provides a structured vocabulary of biological terms used by biological researchers as a tool for standardization of references to biological entities. Genes may be annotated with GO terms to indicate their roles or localizations in the cell. GO has been used in conjunction with high-throughput experimental methods, such as microarrays. In this setting, the interest is to determine whether sets of genes identified by the high-throughput experiment are enriched for GO terms: Do certain terms annotate more genes in the identified set than one might expect? Enriched terms are taken as a potential summary of the cellular function for the identified set of genes and may provide clues leading to new directions for investigation. Current methods for determining whether sets of genes are GO-enriched have certain well-known shortcomings. Many methods do not take the hierarchical structure of the ontology into account in determining enrichment. We address this drawback by introducing a new statistical test (TreeHugger) based on a novel per-gene scoring scheme for GO terms. Given a set of genes and a specified subset of those genes, our method determines enrichment of GO terms in the subset, taking into account the structure of the ontology and ascribing a lower weight to those terms that do not themselves directly annotate the given genes. Tests on simulated and real data indicate that our method is a conservative test for enrichment. Testing TreeHugger on a biological example reveals that it also reduces the redundancy caused by giving high scores to indirect annotations as provided by standard enrichment tests.

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“…The majority of ontological analysis methods rely on threshold-dependent, discrete statistical procedures, such as Pearson's chi-square or Fisher's exact tests, to test for the relative enrichment of gene categories with in lists of significantly differentially expressed genes (reviewed in Khatri and Draghici, 2005). Recently, however, some authors have proposed threshold-free methods based on the Kolmogorov-Smirnov (KS) as an attractive alternative to the discrete approaches (Ben-Shaul et al, 2005;Barry et al, 2005;Lamb et al, 2003;Mootha et al, 2003;Nilsson et al, 2007). In essence, KS-based analysis amounts to computing the empirical distributions of gene-specific differential expression scores (usually t-statistics or varianceregularized variants thereof (Cui et al, 2005;Smyth, 2004;Storey and Tibshirani, 2003) for the gene category being assessed and for a reference gene population (usually all genes on the array or the category complement).…”

Section: Application To the Ontological Analysis Of Microarray Datamentioning

confidence: 99%

“…the Bonferroni or Benjamini-Hochberg corrections (Bejamini and Hochberg, 1995)). One such example is ontological analysis of genome-wide gene expression studies using the Kolmogorov-Smirnov (KS) statistic (Barry et al, 2005;Ben-Shaul et al, 2005;Mootha et al, 2003;Lamb et al, 2003;Nilsson et al, 2007). In fact, this application was the one that originally motivated this study (Section 3).…”

Section: Introductionmentioning

confidence: 96%

Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies

Cited by 21 publications

References 22 publications

Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function

Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function

TreeHugger: A New Test for Enrichment of Gene Ontology Terms

A compression algorithm for pre-simulated Monte Carlo p-value functions: Application to the ontological analysis of microarray studies

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