A comprehensive evaluation of SAM,the SAM R-package and a simple modification to improve its performance

Background  

The Significance Analysis of Microarrays (SAM) is a popular method for detecting significantly expressed genes and controlling the false discovery rate (FDR). Recently, it has been reported in the literature that the FDR is not well controlled by SAM. Due to the vast application of SAM in microarray data analysis, it is of great importance to have an extensive evaluation of SAM and its associated R-package (sam2.20).     

SAMMate: a GUI tool for processing short read alignments in SAM/BAM format

Background  

Next Generation Sequencing (NGS) technology generates tens of millions of short reads for each DNA/RNA sample. A key step in NGS data analysis is the short read alignment of the generated sequences to a reference genome. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing this information.     

Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types

Background  

Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples.     

Improving gene set analysis of microarray data by SAM-GS

Background  

Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).     

Assessment of differential gene expression in human peripheral nerve injury

Background  

Microarray technology is a powerful methodology for identifying differentially expressed genes. However, when thousands of genes in a microarray data set are evaluated simultaneously by fold changes and significance tests, the probability of detecting false positives rises sharply. In this first microarray study of brachial plexus injury, we applied and compared the performance of two recently proposed algorithms for tackling this multiple testing problem, Significance Analysis of Microarrays (SAM) and Westfall and Young step down adjusted p values, as well as t-statistics and Welch statistics, in specifying differential gene expression under different biological States.     

DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites

Background  

A typical step in the analysis of gene expression data is the determination of clusters of genes that exhibit similar expression patterns. Researchers are confronted with the seemingly arbitrary choice between numerous algorithms to perform cluster analysis.     

Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data

Background  

A key step in the analysis of microarray expression profiling data is the identification of genes that display statistically significant changes in expression signals between two biological conditions.     

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

Background  

The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.     

Array2BIO: from microarray expression data to functional annotation of co-regulated genes

Background  

There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility.     

Transcriptional profiling of the pea shoot apical meristem reveals processes underlying its function and maintenance

Background  

Despite the importance of the shoot apical meristem (SAM) in plant development and organ formation, our understanding of the molecular mechanisms controlling its function is limited. Genomic tools have the potential to unravel the molecular mysteries of the SAM, and legume systems are increasingly being used in plant-development studies owing to their unique characteristics such as nitrogen fixation, secondary metabolism, and pod development. Garden pea (Pisum sativum) is a well-established classic model species for genetics studies that has been used since the Mendel era. In addition, the availability of a plethora of developmental mutants makes pea an ideal crop legume for genomics studies. This study aims to utilise genomics tools in isolating genes that play potential roles in the regulation of SAM activity.     

The Effect of S-Adenosylmethionine on Cognitive Performance in Mice: An Animal Model Meta-Analysis

Background

Alzheimer''s disease (AD) is the most frequently diagnosed form of dementia resulting in cognitive impairment. Many AD mouse studies, using the methyl donor S-adenosylmethionine (SAM), report improved cognitive ability, but conflicting results between and within studies currently exist. To address this, we conducted a meta-analysis to evaluate the effect of SAM on cognitive ability as measured by Y maze performance. As supporting evidence, we include further discussion of improvements in cognitive ability, by SAM, as measured by the Morris water maze (MWM).

Methods

We conducted a comprehensive literature review up to April 2014 based on searches querying MEDLINE, EMBASE, Web of Science, the Cochrane Library and Proquest Theses and Dissertation databases. We identified three studies containing a total of 12 experiments that met our inclusion criteria and one study for qualitative review. The data from these studies were used to evaluate the effect of SAM on cognitive performance according to two scenarios: 1. SAM supplemented folate deficient (SFD) diet compared to a folate deficient (FD) diet and 2. SFD diet compared to a nutrient complete (NC) diet. Hedge''s g was used to calculate effect sizes and mixed effects model meta-regression was used to evaluate moderating factors.

Results

Our findings showed that the SFD diet was associated with improvements in cognitive performance. SFD diet mice also had superior cognitive performance compared to mice on an NC diet. Further to this, meta-regression analyses indicated a significant positive effect of study quality score and treatment duration on the effect size estimate for both the FD vs SFD analysis and the SFD vs NC analysis.

Conclusion

The findings of this meta-analysis demonstrate efficacy of SAM in acting as a cognitive performance-enhancing agent. As a corollary, SAM may be useful in improving spatial memory in patients suffering from many dementia forms including AD.     

Evaluating the Effectiveness of Self-Administration of Medication (SAM) Schemes in the Hospital Setting: A Systematic Review of the Literature

Background

Self-administration of medicines is believed to increase patients'' understanding about their medication and to promote their independence and autonomy in the hospital setting. The effect of inpatient self-administration of medication (SAM) schemes on patients, staff and institutions is currently unclear.

Objective

To systematically review the literature relating to the effect of SAM schemes on the following outcomes: patient knowledge, patient compliance/medication errors, success in self-administration, patient satisfaction, staff satisfaction, staff workload, and costs.

Design

Keyword and text word searches of online databases were performed between January and March 2013. Included articles described and evaluated inpatient SAM schemes. Case studies and anecdotal studies were excluded.

Results

43 papers were included for final analysis. Due to the heterogeneity of results and unclear findings it was not possible to perform a quantitative synthesis of results. Participation in SAM schemes often led to increased knowledge about drugs and drug regimens, but not side effects. However, the effect of SAM schemes on patient compliance/medication errors was inconclusive. Patients and staff were highly satisfied with their involvement in SAM schemes.

Conclusions

SAM schemes appear to provide some benefits (e.g. increased patient knowledge), but their effect on other outcomes (e.g. compliance) is unclear. Few studies of high methodological quality using validated outcome measures exist. Inconsistencies in both measuring and reporting outcomes across studies make it challenging to compare results and draw substantive conclusions about the effectiveness of SAM schemes.     

Multivariate analysis of microarray data: differential expression and differential connection

Background  

Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression.     

PAGE: Parametric Analysis of Gene Set Enrichment

Background  

Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.     

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

Background  

A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.     

Comment on and reply to "Analysis of variation of amplitudes in cell cycle gene expression" by Liu,Gaido and Wolfinger: On the analysis of gene expression during the normal,eukaryotic, cell cycle

Background  

The paper of Liu, Gaido and Wolfinger on gene expression during the division cycle of HeLa cells using the data of Whitfield et al. are discussed in order to see whether their analysis is related to gene expression during the division cycle.     

A robust two-way semi-linear model for normalization of cDNA microarray data

Background  

Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression values.     

Transcriptomic response to differentiation induction

Background  

Microarrays used for gene expression studies yield large amounts of data. The processing of such data typically leads to lists of differentially-regulated genes. A common terminal data analysis step is to map pathways of potentially interrelated genes.