A Regression-Based Differential Expression Detection Algorithm for Microarray Studies with Ultra-Low Sample Size

Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.     

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.     

有序差异显示:一种基因表达谱系统比较法

系统研究具有同一基因组的各种细胞群之间基因的差异表达谱十分重要。目前,研究基因差异表达的技术大致有ｍＲＮＡ差异显示［１］、ＲＤＡ［３］、ＳＳＨ［４、５］和ｃＤＮＡ阵列［６］等。近几年,还发展了一些研究基因差异表达谱系统的技术,如ＲＬＣＳ（ｒｅｓｔｒｉｃｔｉｏｎｌａｎｄ－ｍａｒｋｃＤＮＡｓｃａｎｎｉｎｇ）［８］、ＧＥＦ（ｇｅｎｅｅｘｐｒｅｓ－ｓｉｏｎｆｉｎｇｅｒｐｒｉｎｔｉｎｇ）［２］和ＲＮＡ指纹法［９］等。然而,这些技术或较为复杂,或灵敏度偏低。本文拟介绍一种有效的基因表达谱系统比较法——有序差…     

A Comprehensive and Universal Method for Assessing the Performance of Differential Gene Expression Analyses

The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods.     

Robust Modeling of Differential Gene Expression Data Using Normal/Independent Distributions: A Bayesian Approach

In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/independent distributions is considered. These distributions include the Student’s t and normal distributions which have been used previously, but also include extensions such as the slash, the contaminated normal and the Laplace distributions. The purpose of this paper is to identify differentially expressed genes by considering these distributional assumptions instead of the normal distribution. A Bayesian approach using the Markov Chain Monte Carlo method is adopted for parameter estimation. Two publicly available gene expression data sets are analyzed using the proposed approach. The use of the robust models for detecting differentially expressed genes is investigated. This investigation shows that the choice of model for differentiating gene expression data is very important. This is due to the small number of replicates for each gene and the existence of outlying data. Comparison of the performance of these models is made using different statistical criteria and the ROC curve. The method is illustrated using some simulation studies. We demonstrate the flexibility of these robust models in identifying differentially expressed genes.     

cDNA-SCOT基因差异表达两种电泳方法的比较研究

应用cDNA-SCOT研究了低温胁迫下不同抗寒性甘蔗品种的基因差异表达,比较了两种PCR产物电泳方法。结果表明,采用琼脂糖凝胶电泳成本较低,操作简单,省时,其凝胶图像条带亮白,背景为黑色,主带明显,副带较模糊;采用聚丙烯酰胺凝胶电泳成本较高,操作复杂,耗时,其凝胶电泳图像条带呈黑褐色,背景为浅黄色,主带和弱带清晰易读,但部分引物泳道背景深,影响差异基因鉴别效果。根据两种电泳方法的优缺点,提出应用cDNA-SCOT进行基因差异表达研究时的相关建议。     

The Detection of Hamster Connexins: A Comparison of Expression Profiles with Wild-Type Mouse and the Cancer-Prone Min Mouse

The open reading frames of 17 connexins from Syrian hamster (using tissues) and 16 connexins from the Chinese hamster cell line V79, were fully (Cx30, Cx31, Cx37, Cx43 and Cx45) or partially sequenced. We have also detected, and partially sequenced, seven rat connexins that previously were unavailable. The expression of connexin genes was examined in some hamster organs and cultured hamster cells, and compared with wild-type mouse and the cancer-prone Min mouse. Although the expression patterns were similar for most organs and connexins in hamster and mouse, there were also some prominent differences (Cx29 and 30.3 in testis; Cx31.1 and 32 in eye; Cx46 in brain, kidney and testis; Cx47 in kidney). This suggests that some connexins have species-specific expression profiles. In contrast, there were minimal differences in expression profiles between wild type and Min mice. Species-specific expression profiles should be considered in attempts to make animal models of human connexin-associated diseases.     

Patient Satisfaction with Methadone Maintenance Treatment in Vietnam: A Comparison of Different Integrative-Service Delivery Models

Background

Patient satisfaction is an important component of quality in healthcare delivery. To inform the expansion of Methadone Maintenance Treatment (MMT) services in Vietnam, we examined the satisfaction of patients with regards to different services delivery models and identified its associated factors.

Methods

We interviewed 1,016 MMT patients at 5 clinics in Hanoi and Nam Dinh province. The modified SATIS instrument, a 10-item scale, was used to measure three dimensions: “Services quality and convenience”, “Health workers’ capacity and responsiveness” and “Inter-professional care”.

Results

The average score was high across three SATIS dimensions. However, only one third of patients completely satisfied with general health services and treatment outcomes. Older age, higher education, having any problem in self-care and anxiety/depression were negatively associated with patient’s satisfaction. Meanwhile, patients receiving MMT at clinics, where more comprehensive HIV and general health care services were available, were more likely to report a complete satisfaction.

Conclusion

Patients were highly satisfied with MMT services in Vietnam. However, treatment for drug users should go beyond methadone maintenance to address complicated health demands of drug users. Integrating MMT with comprehensive HIV and general health services together with improving the capacity of health workers and efficiency of services organisation to provide interconnected health care for drug users are critical for improving the outcomes of the MMT program.     

Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results

Background

Hierarchical Sample clustering (HSC) is widely performed to examine associations within expression data obtained from microarrays and RNA sequencing (RNA-seq). Researchers have investigated the HSC results with several possible criteria for grouping (e.g., sex, age, and disease types). However, the evaluation of arbitrary defined groups still counts in subjective visual inspection.

Results

To objectively evaluate the degree of separation between groups of interest in the HSC dendrogram, we propose to use Silhouette scores. Silhouettes was originally developed as a graphical aid for the validation of data clusters. It provides a measure of how well a sample is classified when it was assigned to a cluster by according to both the tightness of the clusters and the separation between them. It ranges from 1.0 to ??1.0, and a larger value for the average silhouette (AS) over all samples to be analyzed indicates a higher degree of cluster separation. The basic idea to use an AS is to replace the term cluster by group when calculating the scores. We investigated the validity of this score using simulated and real data designed for differential expression (DE) analysis. We found that larger (or smaller) AS values agreed well with both higher (or lower) degrees of separation between different groups and higher percentages of differentially expressed genes (P_DEG). We also found that the AS values were generally independent on the number of replicates (N_rep). Although the P_DEG values depended on N_rep, we confirmed that both AS and P_DEG values were close to zero when samples in the data showed an intermingled nature between the groups in the HSC dendrogram.

Conclusion

Silhouettes is useful for exploring data with predefined group labels. It would help provide both an objective evaluation of HSC dendrograms and insights into the DE results with regard to the compared groups.

     

Assessment of Patient Empowerment - A Systematic Review of Measures

BackgroundPatient empowerment has gained considerable importance but uncertainty remains about the best way to define and measure it. The validity of empirical findings depends on the quality of measures used. This systematic review aims to provide an overview of studies assessing psychometric properties of questionnaires purporting to capture patient empowerment, evaluate the methodological quality of these studies and assess the psychometric properties of measures identified.MethodsElectronic searches in five databases were combined with reference tracking of included articles. Peer-reviewed articles reporting psychometric testing of empowerment measures for adult patients in French, German, English, Portuguese and Spanish were included. Study characteristics, constructs operationalised and psychometric properties were extracted. The quality of study design, methods and reporting was assessed using the COSMIN checklist. The quality of psychometric properties was assessed using Terwee’s 2007 criteria.Findings30 studies on 19 measures were included. Six measures are generic, while 13 were developed for a specific condition (N=4) or specialty (N=9). Most studies tested measures in English (N=17) or Swedish (N=6). Sample sizes of included studies varied from N=35 to N=8261. A range of patient empowerment constructs was operationalised in included measures. These were classified into four domains: patient states, experiences and capacities; patient actions and behaviours; patient self-determination within the healthcare relationship and patient skills development. Quality assessment revealed several flaws in methodological study quality with COSMIN scores mainly fair or poor. The overall quality of psychometric properties of included measures was intermediate to positive. Certain psychometric properties were not tested for most measures.DiscussionFindings provide a basis from which to develop consensus on a core set of patient empowerment constructs and for further work to develop a (set of) appropriately validated measure(s) to capture this. The methodological quality of psychometric studies could be improved by adhering to published quality criteria.