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October 31, 2024
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ARSyN: a method for the identification and removal of systematic noise in multifactorial time-course microarray experiments

Publicated to: BIOSTATISTICS. 13 (3): 553-566 - 2012-01-01 13(3), DOI: 10.1093/biostatistics/kxr042

Authors:

MARÍA JOSÉ NUEDA; Alberto Ferrer; Ana Conesa
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Affiliations

Ctr Invest Principe Felipe, Bioinformat & Genom Dept - Author
Univ Alicante, Dept Estadist & Invest Operat - Author
Univ Politecn Valencia, Dept Estadist & Invest Operat Aplicadasy Calidad - Author
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Abstract

Transcriptomic profiling experiments that aim to the identification of responsive genes in specific biological conditions are commonly set up under defined experimental designs that try to assess the effects of factors and their interactions on gene expression. Data from these controlled experiments, however, may also contain sources of unwanted noise that can distort the signal under study, affect the residuals of applied statistical models, and hamper data analysis. Commonly, normalization methods are applied to transcriptomics data to remove technical artifacts, but these are normally based on general assumptions of transcript distribution and greatly ignore both the characteristics of the experiment under consideration and the coordinative nature of gene expression. In this paper, we propose a novel methodology, ARSyN, for the preprocessing of microarray data that takes into account these 2 last aspects. By combining analysis of variance (ANOVA) modeling of gene expression values and multivariate analysis of estimated effects, the method identifies the nonstructured part of the signal associated to the experimental factors (the noise within the signal) and the structured variation of the ANOVA errors (the signal of the noise). By removing these noise fractions from the original data, we create a filtered data set that is rich in the information of interest and includes only the random noise required for inferential analysis. In this work, we focus on multifactorial time course microarray (MTCM) experiments with 2 factors: one quantitative such as time or dosage and the other qualitative, as tissue, strain, or treatment. However, the method can be used in other situations such as experiments with only one factor or more complex designs with more than 2 factors. The filtered data obtained after applying ARSyN can be further analyzed with the appropriate statistical technique to obtain the biological information required. To evaluate the performance of the filtering strategy, we have applied different statistical approaches for MTCM analysis to several real and simulated data sets, studying also the efficiency of these techniques. By comparing the results obtained with the original and ARSyN filtered data and also with other filtering techniques, we can conclude that the proposed method increases the statistical power to detect biological signals, especially in cases where there are high levels of structural noise. Software for ARSyN is freely available at http://www.ua.es/personal/mj.nueda.
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Keywords

Analysis of varianceAnimalAnimalsArticleAscaBatch effectBromobenzeneBromobenzenesComputer simulationData interpretation, statisticalDna microarrayGene expression profilingGene-expressionGeneticsLiverMetabolismMethodologyMicroarraysModels, statisticalNormalizationOligonucleotide array sequence analysisPhysiological stressPhysiologyPotatoPrincipal component analysisPrincipal components analysisRatRatsSingleSolanum tuberosumStatistical analysisStatistical modelStress, physiologicalSystematic noiseSystematic noise.Tool

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal BIOSTATISTICS due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2012, it was in position 10/47, thus managing to position itself as a Q1 (Primer Cuartil), in the category Mathematical & Computational Biology.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.19. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.09 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-03, the following number of citations:

  • WoS: 55
  • Scopus: 58
  • Europe PMC: 43
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-03:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 106.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 106 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
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Awards linked to the item

Spanish MICINN [BIO2008-04368-E, DPI2008-06880-C03-03/DPI]
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