Making sense of microarray data distributions

Hoyle, David C.; Rattray, Magnus; Jupp, Ray; Brass, Andy

doi:10.1093/bioinformatics/18.4.576

Cited by 145 publications

(123 citation statements)

References 27 publications

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“…6, which is published as supporting information on the PNAS web site). These results are also consistent with a previous report on power-law distribution of gene expression levels (16,17). Collectively, these results indicate that network regulation of genome-wide transcriptional organization is conserved between prokaryotes and eukaryotes.…”

Section: Resultssupporting

confidence: 93%

Universality and flexibility in gene expression from bacteria to human

Ueda

Hayashi

Matsuyama

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

132

108

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Highly parallel experimental biology is offering opportunities to not just accomplish work more easily, but to explore for underlying governing principles. Recent analysis of the large-scale organization of gene expression has revealed its complex and dynamic nature. However, the underlying dynamics that generate complex gene expression and cellular organization are not yet understood. To comprehensively and quantitatively elucidate these underlying gene expression dynamics, we have analyzed genome-wide gene expression in many experimental conditions in Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster, Mus musculus, and Homo sapiens. Here we demonstrate that the gene expression dynamics follows the same and surprisingly simple principle from E. coli to human, where gene expression changes are proportional to their expression levels, and show that this ''proportional'' dynamics or ''rich-travel-more'' mechanism can regenerate the observed complex and dynamic organization of the transcriptome. These findings provide a universal principle in the regulation of gene expression, show how complex and dynamic organization can emerge from simple underlying dynamics, and demonstrate the flexibility of transcription across a wide range of expression levels.systems biology ͉ DNA microarray ͉ gene expression dynamics ͉ transcription ͉ transcriptional organization

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Section: Resultssupporting

confidence: 93%

Universality and flexibility in gene expression from bacteria to human

Ueda

Hayashi

Matsuyama

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

132

108

View full text Add to dashboard Cite

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“…It is well established that the empirical distributions are one-sided distributions, they manifest the power-law asymptotics and character of these distributions is the same for any tissues and organisms from bacteria to mammal [9]. Similar results have been reported by other authors [10,11,12,13,14].…”

Section: Introductionsupporting

confidence: 84%

Дробно-Устойчивая Статистика Экспрессии Генов В Экспериментальных Данных Секвенирования Нового Поколения

Saenko¹

2016

Math.Biol.Bioinf.

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Abstract.As has been shown in the previous article [1] an application of class of the fractional-stable laws to the genes expression results obtained by DNA-microarrays leads to poor agreement between experimental and theoretical distributions. This difference can be explained by the imperfection of the technology of the gene expression determination. In this article the distributions of the gene expression obtained by Next Generation Sequence technology are investigated. In this technology the determination technique of the gene expression differs from the DNA-microarrays technology. This results to more qualitative results of an approximation. In particular, it is established that the probability density function of the gene expression has a form of shift-scale mixture of probability laws, where one of the components of the mixture is the fractional-stable distribution.

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“…A log-normal distribution was observed for the bulk of Affymetrix microarray spot intensities. 35 Underlying the t-test is the gene-specific SD, or rather changeable SD from gene to gene. However, the intensitydependent SD of the FUMI theory (Eq.…”

Section: Resultsmentioning

confidence: 99%

Test of Significant Differences with a priori Probability in Microarray Experiments

et al. 2003

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The advent of high-throughput array technology has now made it possible to collect data on thousands to tens of thousands of genes simultaneously. However, methods for detecting the genuine changes in the gene expression levels in cells or tissues are still evolving. [1][2][3][4][5][6][7][8][9][10][11] A straightforward method for comparing two expression levels of genes is the traditional two-sample t-test. The basic problem with the t-test in microarray experiments, however, is that the repetition is restricted within a small number in most cases, because experiments are costly or tedious to repeat. Although the importance in replication has been illustrated, 1,3,6,9 situations often arise where only single or duplicate experiments for each condition are allowed.The purpose of this paper is to put forward a method for testing the significant differences of the gene expression levels under a single pair of experiments (a control experiment and treatment experiment). In order to take into account the stochastic aspects of gene expressions, we model our algorithm on the t-test. In our approach and the t-test, the SD estimates of the gene expression levels are a criterion for the statistical judgment, but one of the key differences is how to estimate the SD for each gene.In the t-test, the SD estimates are derived from the same data set as those to be judged by the t-test, itself. This fact can be closely connected with the above-mentioned problem of replication. In our approach, the SD estimates, referred to here as a priori SD, are obtained from experimental results which are different from the target data set of the judgment.Statistics tells that the variability in the estimates of SD obeys the chi-squares distribution and is much larger than the variability in the estimates of averages, as long as the estimates are obtained by repetition. That is, the estimates of averages are more reliable. The t-test uses the SD estimates directly, but in this paper, the a prior SD is given as a function of the average of the gene expression levels. Then, we can easily expect that our approach can provide more stable judgment, but needs a sound model for the a prior SD.The idea of the a priori SD is not novel in the area of analytical chemistry. Since more than three decades ago, there have been published many theories and methods for estimating SD with no recourse to repetition, especially in instrumental analyses. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] In spite of varied symbols and terminology in the literature, the largest part of uncertainty equations proposed can take a universal form:where RSD denotes the relative standard deviation of measurements, sB denotes blank SD, A measurements (e.g., area), and I independent error. To our knowledge, Huber et al. A traditional method for comparing two expression levels of genes in microarray experiments is the two-sample t-test. Because of the difficulty in using a large number of microarrays, an alternative method is required which can provide a relia...

show abstract

Making sense of microarray data distributions

Cited by 145 publications

References 27 publications

Universality and flexibility in gene expression from bacteria to human

Universality and flexibility in gene expression from bacteria to human

Дробно-Устойчивая Статистика Экспрессии Генов В Экспериментальных Данных Секвенирования Нового Поколения

Test of Significant Differences with a priori Probability in Microarray Experiments

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