RMA with quantile normalization mixes biological signals between different sample groups in microarray data analysis

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.

On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.

Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

BACKGROUND: Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers.

METHODS: In this study, we have undertaken a meta-analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets.

RESULTS: We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer.

CONCLUSION: In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated.

The use of patient experience survey data by out-of-hours primary care services: a qualitative interview study

Background English National Quality Requirements mandate out-of-hours primary care services to routinely audit patient experience, but do not state how it should be done.

Objectives We explored how providers collect patient feedback data and use it to inform service provision. We also explored staff views on the utility of out-of-hours questions from the English General Practice Patient Survey (GPPS).

Methods A qualitative study was conducted with 31 staff (comprising service managers, general practitioners and administrators) from 11 out-of-hours primary care providers in England, UK. Staff responsible for patient experience audits within their service were sampled and data collected via face-to-face semistructured interviews.

Results Although most providers regularly audited their patients’ experiences by using patient surveys, many participants expressed a strong preference for additional qualitative feedback. Staff provided examples of small changes to service delivery resulting from patient feedback, but service-wide changes were not instigated. Perceptions that patients lacked sufficient understanding of the urgent care system in which out-of-hours primary care services operate were common and a barrier to using feedback to enable change. Participants recognised the value of using patient experience feedback to benchmark services, but perceived weaknesses in the out-of-hours items from the GPPS led them to question the validity of using these data for benchmarking in its current form.

Conclusions The lack of clarity around how out-of-hours providers should audit patient experience hinders the utility of the National Quality Requirements. Although surveys were common, patient feedback data had only a limited role in service change. Data derived from the GPPS may be used to benchmark service providers, but refinement of the out-of-hours items is needed.

Global surveillance of cancer survival 1995–2009: analysis of individual data for 25 676 887 patients from 279 population-based registries in 67 countries (CONCORD-2)

BACKGROUND: Worldwide data for cancer survival are scarce. We aimed to initiate worldwide surveillance of cancer survival by central analysis of population-based registry data, as a metric of the effectiveness of health systems, and to inform global policy on cancer control.

METHODS: Individual tumour records were submitted by 279 population-based cancer registries in 67 countries for 25·7 million adults (age 15-99 years) and 75,000 children (age 0-14 years) diagnosed with cancer during 1995-2009 and followed up to Dec 31, 2009, or later. We looked at cancers of the stomach, colon, rectum, liver, lung, breast (women), cervix, ovary, and prostate in adults, and adult and childhood leukaemia. Standardised quality control procedures were applied; errors were corrected by the registry concerned. We estimated 5-year net survival, adjusted for background mortality in every country or region by age (single year), sex, and calendar year, and by race or ethnic origin in some countries. Estimates were age-standardised with the International Cancer Survival Standard weights.

FINDINGS: 5-year survival from colon, rectal, and breast cancers has increased steadily in most developed countries. For patients diagnosed during 2005-09, survival for colon and rectal cancer reached 60% or more in 22 countries around the world; for breast cancer, 5-year survival rose to 85% or higher in 17 countries worldwide. Liver and lung cancer remain lethal in all nations: for both cancers, 5-year survival is below 20% everywhere in Europe, in the range 15-19% in North America, and as low as 7-9% in Mongolia and Thailand. Striking rises in 5-year survival from prostate cancer have occurred in many countries: survival rose by 10-20% between 1995-99 and 2005-09 in 22 countries in South America, Asia, and Europe, but survival still varies widely around the world, from less than 60% in Bulgaria and Thailand to 95% or more in Brazil, Puerto Rico, and the USA. For cervical cancer, national estimates of 5-year survival range from less than 50% to more than 70%; regional variations are much wider, and improvements between 1995-99 and 2005-09 have generally been slight. For women diagnosed with ovarian cancer in 2005-09, 5-year survival was 40% or higher only in Ecuador, the USA, and 17 countries in Asia and Europe. 5-year survival for stomach cancer in 2005-09 was high (54-58%) in Japan and South Korea, compared with less than 40% in other countries. By contrast, 5-year survival from adult leukaemia in Japan and South Korea (18-23%) is lower than in most other countries. 5-year survival from childhood acute lymphoblastic leukaemia is less than 60% in several countries, but as high as 90% in Canada and four European countries, which suggests major deficiencies in the management of a largely curable disease.

INTERPRETATION: International comparison of survival trends reveals very wide differences that are likely to be attributable to differences in access to early diagnosis and optimum treatment. Continuous worldwide surveillance of cancer survival should become an indispensable source of information for cancer patients and researchers and a stimulus for politicians to improve health policy and health-care systems.

Identification of Small Signal Oscillation Mode Parameters from Simulated and Actual PMU Ringdown Data

This paper presents the practical use of Prony Analysis to identify small signal oscillation mode parameters from simulated and actual phasor measurement unit (PMU) ringdown data. A well-known two-area four-machine power system was considered as a study case while the latest PMU ringdown data were collected from a double circuit 275 kV main interconnector on the Irish power system. The eigenvalue analysis and power spectral density were also conducted for the purpose of comparison. The capability of Prony Analysis to identify the mode parameters from three different types of simulated PMU ringdown data has been shown successfully. Furthermore, the results indicate that the Irish power system has dominant frequency modes at different frequencies. However, each mode has good system damping.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Current and Turbulence Measurement with Collocated ADP and Turbulence Profiler Data

This paper presents a current and turbulence measurement campaign conducted at a test site in an energetic tidal channel known as Strangford Narrows, Northern Ireland. The data was collected as part of the MaRINET project funded by the EU under their FP7 framework. It was a collaborative effort between Queen’s University Belfast, SCHOTTEL and Fraunhofer IWES. The site is highly turbulent with a strong shear flow. Longer term measurements of the flow regime were made using a bottom mounted Acoustic Doppler Profiler (ADP). During a specific turbulence measurement campaign, two collocated in- struments were used to measure incoming flow characteristics: an ADP (Aquadopp, Nortek) and a turbulence profiler (MicroRider, Rockland Scientific International). The instruments recorded the same incoming flow, so that direct comparisons between the data can be made. In this study the methodology adopted to deploy the instruments is presented. The resulting turbulence measurements using the different types of instrumentation are compared and the usefulness of each instrument for the relevant range of applications is discussed. The paper shows the ranges of the frequency spectra obtained using the different instruments, with the combined measurements providing insight into the structure of the turbulence across a wide range of scales.

Management of polycythaemia vera: a critical review of current data

Polycythaemia vera (PV) is a chronic blood cancer; its clinical features are dominated by myeloproliferation (erythrocytosis, often leucocytosis and/or thrombocytosis) and a tendency for thrombosis and transformation to myelofibrosis or acute myeloid leukaemia. In the past 10 years the pathophysiology of this condition has been defined as JAK/STAT pathway activation, almost always due to mutations in JAK2 exons 12 or 14 (JAK2 V617F). In the same time period our understanding of the optimal management of PV has expanded, most recently culminating in the approval of JAK inhibitors for the treatment of PV patients who are resistant or intolerant to therapy with hydroxycarbamide. It has also been demonstrated that life expectancy for many patients with PV is not normal, nor is their quality of life. We critically explore these findings and discuss their impact. In addition, we highlight persisting gaps in our current management strategy; for example, what is the optimal first line cytoreductive therapy and, indeed, which patients need cytoreductive drugs.

High-dose therapy and autologous stem cell transplantation in first relapse for diffuse large B cell lymphoma in the rituximab era:an analysis based on data from the European Blood and Marrow Transplantation Registry

Autologous stem cell transplantation (ASCT) consolidation remains the treatment of choice for patients with relapsed diffuse large B cell lymphoma. The impact of rituximab combined with chemotherapy in either first- or second-line therapy on the ultimate results of ASCT remains to be determined, however. This study was designed to evaluate the benefit of ASCT in patients achieving a second complete remission after salvage chemotherapy by retrospectively comparing the disease-free survival (DFS) after ASCT for each patient with the duration of the first complete remission (CR1). Between 1990 and 2005, a total of 470 patients who had undergone ASCT and reported to the European Blood and Bone Transplantation Registry with Medical Essential Data Form B information were evaluated. Of these 470 patients, 351 (74%) had not received rituximab before ASCT, and 119 (25%) had received rituximab before ASCT. The median duration of CR1 was 11 months. The median time from diagnosis to ASCT was 24 months. The BEAM protocol was the most frequently used conditioning regimen (67%). After ASCT, the 5-year overall survival was 63% (95% confidence interval, 58%-67%) and 5-year DFS was 48% (95% confidence interval, 43%-53%) for the entire patient population. Statistical analysis showed a significant increase in DFS after ASCT compared with duration of CR1 (median, 51 months versus 11 months; P < .001). This difference was also highly significant for patients with previous exposure to rituximab (median, 10 months versus not reached; P < .001) and for patients who had experienced relapse before 1 year (median, 6 months versus 47 months; P < .001). Our data indicate that ASCT can significantly increase DFS compared with the duration of CR1 in relapsed diffuse large B cell lymphoma and can alter the disease course even in patients with high-risk disease previously treated with rituximab.

Long working hours and risk of coronary heart disease and stroke: a systematic review and meta-analysis of published and unpublished data for 603 838 individuals

Background: Long working hours might increase the risk of cardiovascular disease, but prospective evidence is scarce, imprecise, and mostly limited to coronary heart disease. We aimed to assess long working hours as a risk factor for incident coronary heart disease and stroke. 

Methods We identified published studies through a systematic review of PubMed and Embase from inception to Aug 20, 2014. We obtained unpublished data for 20 cohort studies from the Individual-Participant-Data Meta-analysis in Working Populations (IPD-Work) Consortium and open-access data archives. We used cumulative random-effects meta-analysis to combine effect estimates from published and unpublished data. 

Findings We included 25 studies from 24 cohorts in Europe, the USA, and Australia. The meta-analysis of coronary heart disease comprised data for 603 838 men and women who were free from coronary heart disease at baseline; the meta-analysis of stroke comprised data for 528 908 men and women who were free from stroke at baseline. Follow-up for coronary heart disease was 5·1 million person-years (mean 8·5 years), in which 4768 events were recorded, and for stroke was 3·8 million person-years (mean 7·2 years), in which 1722 events were recorded. In cumulative meta-analysis adjusted for age, sex, and socioeconomic status, compared with standard hours (35-40 h per week), working long hours (≥55 h per week) was associated with an increase in risk of incident coronary heart disease (relative risk [RR] 1·13, 95% CI 1·02-1·26; p=0·02) and incident stroke (1·33, 1·11-1·61; p=0·002). The excess risk of stroke remained unchanged in analyses that addressed reverse causation, multivariable adjustments for other risk factors, and different methods of stroke ascertainment (range of RR estimates 1·30-1·42). We recorded a dose-response association for stroke, with RR estimates of 1·10 (95% CI 0·94-1·28; p=0·24) for 41-48 working hours, 1·27 (1·03-1·56; p=0·03) for 49-54 working hours, and 1·33 (1·11-1·61; p=0·002) for 55 working hours or more per week compared with standard working hours (p_trend<0·0001).

Interpretation Employees who work long hours have a higher risk of stroke than those working standard hours; the association with coronary heart disease is weaker. These findings suggest that more attention should be paid to the management of vascular risk factors in individuals who work long hours. 

Energy versus data integrity trade-offs in embedded high-density logic compatible dynamic memories

Current variation aware design methodologies, tuned for worst-case scenarios, are becoming increasingly pessimistic from the perspective of power and performance. A good example of such pessimism is setting the refresh rate of DRAMs according to the worst-case access statistics, thereby resulting in very frequent refresh cycles, which are responsible for the majority of the standby power consumption of these memories. However, such a high refresh rate may not be required, either due to extremely low probability of the actual occurrence of such a worst-case, or due to the inherent error resilient nature of many applications that can tolerate a certain number of potential failures. In this paper, we exploit and quantify the possibilities that exist in dynamic memory design by shifting to the so-called approximate computing paradigm in order to save power and enhance yield at no cost. The statistical characteristics of the retention time in dynamic memories were revealed by studying a fabricated 2kb CMOS compatible embedded DRAM (eDRAM) memory array based on gain-cells. Measurements show that up to 73% of the retention power can be saved by altering the refresh time and setting it such that a small number of failures is allowed. We show that these savings can be further increased by utilizing known circuit techniques, such as body biasing, which can help, not only in extending, but also in preferably shaping the retention time distribution. Our approach is one of the first attempts to access the data integrity and energy tradeoffs achieved in eDRAMs for utilizing them in error resilient applications and can prove helpful in the anticipated shift to approximate computing.

Statistical data correction for unreliable memories

In this paper, we introduce a statistical data-correction framework that aims at improving the DSP system performance in presence of unreliable memories. The proposed signal processing framework implements best-effort error mitigation for signals that are corrupted by defects in unreliable storage arrays using a statistical correction function extracted from the signal statistics, a data-corruption model, and an application-specific cost function. An application example to communication systems demonstrates the efficacy of the proposed approach.