Statistically invalid classification of high throughput gene expression data.
Ontology highlight
ABSTRACT:
SUBMITTER: Barbash S
PROVIDER: S-EPMC3551228 | biostudies-other | 2013
REPOSITORIES: biostudies-other
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Project description:Extensive methodological research has been conducted to improve gene expression summary methods. However, in addition to quantitative gene expression summaries, most platforms, including all those examined in the MicroArray Quality Control project, provide a qualitative detection call result for each gene on the platform. These detection call algorithms are intended to render an assessment of whether or not each transcript is reliably measured. In this paper, we review uses of these qualitative detection call results in the analysis of microarray data. We also review the detection call algorithms for two widely used gene expression microarray platforms, Affymetrix GeneChips and Illumina BeadArrays, and more clearly formalize the mathematical notation for the Illumina BeadArray detection call algorithm. Both algorithms result in a P-value which is then used for determining the qualitative detection calls. We examined the performance of these detection call algorithms and default parameters by applying the methods to two spike-in datasets. We show that the default parameters for qualitative detection calls yield few absent calls for high spike-in concentrations. When genes of interest are expected to be present at very low concentrations, spike-in datasets can be useful for appropriately adjusting the tuning parameters for qualitative detection calls.
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Project description:UnlabelledHigh-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline.AvailabilitynEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Selection of biologically relevant genes from high-dimensional expression data is a key research problem in gene expression genomics. Most of the available gene selection methods are either based on relevancy or redundancy measure, which are usually adjudged through post selection classification accuracy. Through these methods the ranking of genes was conducted on a single high-dimensional expression data, which led to the selection of spuriously associated and redundant genes. Hence, we developed a statistical approach through combining a support vector machine with Maximum Relevance and Minimum Redundancy under a sound statistical setup for the selection of biologically relevant genes. Here, the genes were selected through statistical significance values and computed using a nonparametric test statistic under a bootstrap-based subject sampling model. Further, a systematic and rigorous evaluation of the proposed approach with nine existing competitive methods was carried on six different real crop gene expression datasets. This performance analysis was carried out under three comparison settings, i.e., subject classification, biological relevant criteria based on quantitative trait loci and gene ontology. Our analytical results showed that the proposed approach selects genes which are more biologically relevant as compared to the existing methods. Moreover, the proposed approach was also found to be better with respect to the competitive existing methods. The proposed statistical approach provides a framework for combining filter and wrapper methods of gene selection.
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Project description:BackgroundAccurate assignment of genes to pathways is essential in order to understand the functional role of genes and to map the existing pathways in a given genome. Existing algorithms predict pathways by extrapolating experimental data in one organism to other organisms for which this data is not available. However, current systems classify all genes that belong to a specific EC family to all the pathways that contain the corresponding enzymatic reaction, and thus introduce ambiguity.ResultsHere we describe an algorithm for assignment of genes to cellular pathways that addresses this problem by selectively assigning specific genes to pathways. Our algorithm uses the set of experimentally elucidated metabolic pathways from MetaCyc, together with statistical models of enzyme families and expression data to assign genes to enzyme families and pathways by optimizing correlated co-expression, while minimizing conflicts due to shared assignments among pathways. Our algorithm also identifies alternative ("backup") genes and addresses the multi-domain nature of proteins. We apply our model to assign genes to pathways in the Yeast genome and compare the results for genes that were assigned experimentally. Our assignments are consistent with the experimentally verified assignments and reflect characteristic properties of cellular pathways.ConclusionWe present an algorithm for automatic assignment of genes to metabolic pathways. The algorithm utilizes expression data and reduces the ambiguity that characterizes assignments that are based only on EC numbers.
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Project description:BackgroundThe advances in high-throughput sequencing technologies and growth in data sizes has highlighted the need for scalable tools to perform quality assurance testing. These tests are necessary to ensure that data is of a minimum necessary standard for use in downstream analysis. In this paper we present the SAMQA tool to rapidly and robustly identify errors in population-scale sequence data.ResultsSAMQA has been used on samples from three separate sets of cancer genome data from The Cancer Genome Atlas (TCGA) project. Using technical standards provided by the SAM specification and biological standards defined by researchers, we have classified errors in these sequence data sets relative to individual reads within a sample. Due to an observed linearithmic speedup through the use of a high-performance computing (HPC) framework for the majority of tasks, poor quality data was identified prior to secondary analysis in significantly less time on the HPC framework than the same data run using alternative parallelization strategies on a single server.ConclusionsThe SAMQA toolset validates a minimum set of data quality standards across whole-genome and exome sequences. It is tuned to run on a high-performance computational framework, enabling QA across hundreds gigabytes of samples regardless of coverage or sample type.
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Project description:We previously used RNA-seq to identify chemicals whose effects on neuronal gene expression mimicked transcriptional signatures of autism, aging, and neurodegeneration. However, this approach was costly and time consuming, which limited our study to testing a single chemical concentration on mixed sex cortical neuron cultures. Here, we adapted a targeted transcriptomic method (RASL-seq, similar to TempO-seq) to interrogate changes in expression of a set of 56 signature genes in response to a library of 350 chemicals and chemical mixtures at four concentrations in male and female mouse neuronal cultures. This enabled us to replicate and expand our previous classifications, and show that transcriptional responses were largely equivalent between sexes. Overall, we found that RASL-seq can be used to accelerate the pace at which chemicals and mixtures that transcriptionally mimic autism and other neuropsychiatric diseases can be identified, and provides a cost-effective way to quantify gene expression with a panel of marker genes.
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Project description:BACKGROUND:Though basigin (BSG) was reported to be overexpressed in nasopharyngeal carcinoma (NPC) and correlate with the development of NPC, the molecular basis of BSG in NPC remained elusive. The aim of the research was to investigate BSG expression in NPC and the potential molecular mechanism underlying it. MATERIALS AND METHODS:BSG expression in NPC tissues was detected with immunohistochemistry. Chi-square test, Kruskal-Wallis test and Spearman correlation test were performed to examine the relationship between BSG expression and the clinico-pathological features as well as EGFR and P-53 expression in NPC. In addition, data from the Human Protein Atlas (HPA) database and oncomine were collected to validate BSG expression in NPC. Meta-analysis was conducted to investigate the association between BSG expression and the clinico-pathological variables of NPC. The prognostic value and the alteration of BSG gene status were also analyzed with data from The Cancer Genome Atlas (TCGA). RESULTS:BSG presented notably higher expression in NPC tissues than in non-cancer tissues. Moreover, IHC results showed that BSG expression was significantly correlated with tumor progression. A positive correlation was also found between BSG expression and EGFR, P53 expression. Meta-analysis confirmed that BSG was indicative of lymph node metastasis and TNM stage in NPC. Additionally, data from cBioPortal indicated that alteration of BSG gene existed in 5% of NPC cases and BSG correlative genes were obtained from the Co-expression Analysis in TCGA. CONCLUSION:BSG was overexpressed in NPC and might have an oncogenic effect on the tumorigenesis and progression of NPC.
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Project description:BackgroundInterpretation of gene expression microarray data in the light of external information on both columns and rows (experimental variables and gene annotations) facilitates the extraction of pertinent information hidden in these complex data. Biologists classically interpret genes of interest after retrieving functional information from a subset of genes of interest. Transcription factors play an important role in orchestrating the regulation of gene expression. Their activity can be deduced by examining the presence of putative transcription factors binding sites in the gene promoter regions.ResultsIn this paper we present the multivariate statistical method RLQ which aims to analyze microarray data where additional information is available on both genes and samples. As an illustrative example, we applied RLQ methodology to analyze transcription factor activity associated with the time-course effect of steroids on the growth of primary human lung fibroblasts. RLQ could successfully predict transcription factor activity, and could integrate various other sources of external information in the main frame of the analysis. The approach was validated by means of alternative statistical methods and biological validation.ConclusionsRLQ provides an efficient way of extracting and visualizing structures present in a gene expression dataset by directly modeling the link between experimental variables and gene annotations.
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