Diverse correlation structures in gene expression data and their utility in improving statistical inference

It is well known that correlations in microarray data represent a serious nuisance deteriorating the performance of gene selection procedures. This paper is intended to demonstrate that the correlation structure of microarray data provides a rich source of useful information. We discuss distinct correlation substructures revealed in microarray gene expression data by an appropriate ordering of genes. These substructures include stochastic proportionality of expression signals in a large percentage of all gene pairs, negative correlations hidden in ordered gene triples, and a long sequence of weakly dependent random variables associated with ordered pairs of genes. The reported striking regularities are of general biological interest and they also have far-reaching implications for theory and practice of statistical methods of microarray data analysis. We illustrate the latter point with a method for testing differential expression of nonoverlapping gene pairs. While designed for testing a different null hypothesis, this method provides an order of magnitude more accurate control of type 1 error rate compared to conventional methods of individual gene expression profiling. In addition, this method is robust to the technical noise. Quantitative inference of the correlation structure has the potential to extend the analysis of microarray data far beyond currently practiced methods.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS120 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

How high is the level of technical noise in microarray data?

BACKGROUND: Microarray gene expression data are commonly perceived as being extremely noisy because of many imperfections inherent in the current technology. A recent study conducted by the MicroArray Quality Control (MAQC) Consortium and published in Nature Biotechnology provides a unique opportunity to probe into the true level of technical noise in such data. RESULTS: In the present report, the MAQC study is reanalyzed in order to quantitatively assess measurement errors inherent in high-density oligonucleotide array technology (Affymetrix platform). The level of noise is directly estimated from technical replicates of gene expression measurements in the absence of biological variability. For each probe set, the magnitude of random fluctuations across technical replicates is characterized by the standard deviation of the corresponding log-expression signal. The resultant standard deviations appear to be uniformly small and symmetrically distributed across probe sets. The observed noise level does not cause any tangible bias in estimated pair-wise correlation coefficients, the latter being particularly prone to its presence in microarray data. CONCLUSION: The reported analysis strongly suggests that, contrary to popular belief, the random fluctuations of gene expression signals caused by technical noise are quite low and the effect of such fluctuations on the results of statistical inference from Affymetrix GeneChip microarray data is negligibly small. REVIEWERS: The paper was reviewed by A. Mushegian, K. Jordan, and E. Koonin

A nitty-gritty aspect of correlation and network inference from gene expression data

<p>Abstract</p> <p>Background</p> <p>All currently available methods of network/association inference from microarray gene expression measurements implicitly assume that such measurements represent the actual expression levels of different genes within each cell included in the biological sample under study. Contrary to this common belief, modern microarray technology produces signals aggregated over a random number of individual cells, a "nitty-gritty" aspect of such arrays, thereby causing a random effect that distorts the correlation structure of intra-cellular gene expression levels.</p> <p>Results</p> <p>This paper provides a theoretical consideration of the random effect of signal aggregation and its implications for correlation analysis and network inference. An attempt is made to quantitatively assess the magnitude of this effect from real data. Some preliminary ideas are offered to mitigate the consequences of random signal aggregation in the analysis of gene expression data.</p> <p>Conclusion</p> <p>Resulting from the summation of expression intensities over a random number of individual cells, the observed signals may not adequately reflect the true dependence structure of intra-cellular gene expression levels needed as a source of information for network reconstruction. Whether the reported effect is extrime or not, the important point, is to reconize and incorporate such signal source for proper inference. The usefulness of inference on genetic regulatory structures from microarray data depends critically on the ability of investigators to overcome this obstacle in a scientifically sound way.</p> <p>Reviewers</p> <p>This article was reviewed by Byung Soo KIM, Jeanne Kowalski and Geoff McLachlan</p

Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation analysis?

<p>Abstract</p> <p>Background.</p> <p>This work was undertaken in response to a recently published paper by Okoniewski and Miller (BMC Bioinformatics 2006, <b>7</b>: Article 276). The authors of that paper came to the conclusion that the process of multiple targeting in short oligonucleotide microarrays induces spurious correlations and this effect may deteriorate the inference on correlation coefficients. The design of their study and supporting simulations cast serious doubt upon the validity of this conclusion. The work by Okoniewski and Miller drove us to revisit the issue by means of experimentation with biological data and probabilistic modeling of cross-hybridization effects.</p> <p>Results.</p> <p>We have identified two serious flaws in the study by Okoniewski and Miller: (1) The data used in their paper are not amenable to correlation analysis; (2) The proposed simulation model is inadequate for studying the effects of cross-hybridization. Using two other data sets, we have shown that removing multiply targeted probe sets does not lead to a shift in the histogram of sample correlation coefficients towards smaller values. A more realistic approach to mathematical modeling of cross-hybridization demonstrates that this process is by far more complex than the simplistic model considered by the authors. A diversity of correlation effects (such as the induction of positive or negative correlations) caused by cross-hybridization can be expected in theory but there are natural limitations on the ability to provide quantitative insights into such effects due to the fact that they are not directly observable.</p> <p>Conclusion.</p> <p>The proposed stochastic model is instrumental in studying general regularities in hybridization interaction between probe sets in microarray data. As the problem stands now, there is no compelling reason to believe that multiple targeting causes a large-scale effect on the correlation structure of Affymetrix gene expression data. Our analysis suggests that the observed long-range correlations in microarray data are of a biological nature rather than a technological flaw.</p> <p>Reviewers:</p> <p>The paper was reviewed by I. K. Jordan, D. P. Gaile (nominated by E. Koonin), and W. Huber (nominated by S. Dudoit).</p

What helps improve outcomes of industrial policy? Evidence from Russia

In the context of most developing countries, the implementation of industrial policy faces significant challenges related to capacity, access to information, and governance limitations. This situation accounts for the absence of widely recognised success stories – instances where government agencies and policy instruments have an established track record of effectively pursuing national objectives within the realm of industrial policy. This highlights a significant gap in our understanding about which industrial policy tools can be effective in countries grappling with broader deficiencies in their national accountability systems. In this article, we delve into the state support programmes initiated by Russia’s Industrial Development Fund (IDF) since 2014. These programmes aim to promote import substitution by providing industrial enterprises with lowinterest rate loans. Notably, the IDF programmes differ significantly from most other industrial policy instruments employed by the Russian government in terms of their design, implementation principles, and outcomes. Between 2014 and 2017, the implementation of the IDF’s programmes produced statistically significant results, fostering the growth of sales for the supported enterprises. Within our article, we shed light on the institutional features that contributed to the effectiveness of the programmes and enabled the IDF to maintain the integrity of their procedures for selecting beneficiaries and supporting them throughout project implementation. Our analysis has identified a set of institutional arrangements that can maximise the positive impacts of state support programmes while minimising the respective risks. Consequently, we believe that the successful Russian experience in administering the programmes for direct state support holds substantial value for a wide range of organisations compelled to implement government support programmes under less-than-ideal institutional conditions. Furthermore, the emergence of more effective industrial policy tools in Russia, such as the IDF, in the mid-2010s, may partially explain the increased resilience of the Russian economy in the face of large-scale international sanctions imposed in 2022 due to its war in Ukraine

Multivariate search for differentially expressed gene combinations

BACKGROUND: To identify differentially expressed genes, it is standard practice to test a two-sample hypothesis for each gene with a proper adjustment for multiple testing. Such tests are essentially univariate and disregard the multidimensional structure of microarray data. A more general two-sample hypothesis is formulated in terms of the joint distribution of any sub-vector of expression signals. RESULTS: By building on an earlier proposed multivariate test statistic, we propose a new algorithm for identifying differentially expressed gene combinations. The algorithm includes an improved random search procedure designed to generate candidate gene combinations of a given size. Cross-validation is used to provide replication stability of the search procedure. A permutation two-sample test is used for significance testing. We design a multiple testing procedure to control the family-wise error rate (FWER) when selecting significant combinations of genes that result from a successive selection procedure. A target set of genes is composed of all significant combinations selected via random search. CONCLUSIONS: A new algorithm has been developed to identify differentially expressed gene combinations. The performance of the proposed search-and-testing procedure has been evaluated by computer simulations and analysis of replicated Affymetrix gene array data on age-related changes in gene expression in the inner ear of CBA mice

Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI

Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain–computer interface (BCI) feedback. In this study, we investigated the potential for observation of virtual hand movements within a P300-based BCI as a feedback system to activate the mirror neuron system. We also explored the role of feedback anticipation and estimation mechanisms during movement observation. Twenty healthy subjects participated in the study. We analyzed event-related desynchronization and synchronization (ERD/S) of sensorimotor EEG rhythms and Error-related potentials (ErrPs) during observation of virtual hand finger flexion presented as feedback in the P300-BCI loop and compared the dynamics of ERD/S and ErrPs during observation of correct feedback and errors. We also analyzed these EEG markers during passive AO under two conditions: when subjects anticipated the action demonstration and when the action was unexpected. A pre-action mu-ERD was found both before passive AO and during action anticipation within the BCI loop. Furthermore, a significant increase in beta-ERS was found during AO within incorrect BCI feedback trials. We suggest that the BCI feedback may exaggerate the passive-AO effect, as it engages feedback anticipation and estimation mechanisms as well as movement error monitoring simultaneously. The results of this study provide insights into the potential of P300-BCI with AO-feedback as a tool for neurorehabilitation

Recommended reading list of early publications on atomic layer deposition-Outcome of the "Virtual Project on the History of ALD"

Atomic layer deposition (ALD), a gas-phase thin film deposition technique based on repeated, self-terminating gas-solid reactions, has become the method of choice in semiconductor manufacturing and many other technological areas for depositing thin conformal inorganic material layers for various applications. ALD has been discovered and developed independently, at least twice, under different names: atomic layer epitaxy (ALE) and molecular layering. ALE, dating back to 1974 in Finland, has been commonly known as the origin of ALD, while work done since the 1960s in the Soviet Union under the name "molecular layering" (and sometimes other names) has remained much less known. The virtual project on the history of ALD (VPHA) is a volunteer-based effort with open participation, set up to make the early days of ALD more transparent. In VPHA, started in July 2013, the target is to list, read and comment on all early ALD academic and patent literature up to 1986. VPHA has resulted in two essays and several presentations at international conferences. This paper, based on a poster presentation at the 16th International Conference on Atomic Layer Deposition in Dublin, Ireland, 2016, presents a recommended reading list of early ALD publications, created collectively by the VPHA participants through voting. The list contains 22 publications from Finland, Japan, Soviet Union, United Kingdom, and United States. Up to now, a balanced overview regarding the early history of ALD has been missing; the current list is an attempt to remedy this deficiency. (C) 2016 Author(s).Peer reviewe