Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods.
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ABSTRACT:
SUBMITTER: Adler P
PROVIDER: S-EPMC2812946 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationVisualization of genomic data is fundamental for gaining insights into genome function. Yet, co-visualization of a large number of datasets remains a challenge in all popular genome browsers and the development of new visualization methods is needed to improve the usability and user experience of genome browsers.ResultsWe present Dynamix, a JBrowse plugin that enables the parallel inspection of hundreds of genomic datasets. Dynamix takes advantage of a priori knowledge to automatically display data tracks with signal within a genomic region of interest. As the user navigates through the genome, Dynamix automatically updates data tracks and limits all manual operations otherwise needed to adjust the data visible on screen. Dynamix also introduces a new carousel view that optimizes screen utilization by enabling users to independently scroll through groups of tracks.Availability and implementationDynamix is hosted at http://furlonglab.embl.de/Dynamix .Contactcharles.girardot@embl.de.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5870560 | biostudies-literature
Project description:BackgroundHead and neck squamous cell cancer (HNSCC) is the sixth most common cancer in the world; its pathogenic mechanism remains to be further clarified.MethodsRobust rank aggregation (RRA) analysis was utilized to identify the metasignature dysregulated genes, which were then used for potential drug prediction. Weighted gene coexpression network analysis (WGCNA) was performed on all metasignature genes to find hub genes. DNA methylation analysis, GSEA, functional annotation, and immunocyte infiltration analysis were then performed on hub genes to investigate their potential role in HNSCC.ResultA total of 862 metasignature genes were identified, and 6 potential drugs were selected based on these genes. Based on the result of WGCNA, six hub genes (ITM2A, GALNTL1, FAM107A, MFAP4, PGM5, and OGN) were selected (GS > 0.1, MM > 0.75, GS p value < 0.05, and MM p value < 0.05). All six genes were downregulated in tumor tissue (FDR < 0.01) and were related to the clinical stage and prognosis of HNSCC in different degrees. Methylation analysis showed that the dysregulation of ITM2A, GALNTL1, FAM107A, and MFAP4 may be caused by hypermethylation. Moreover, the expression level of all 6 hub genes was positively associated with immune cell infiltration, and the result of GSEA showed that all hub genes may be involved in the process of immunoregulation.ConclusionAll identified hub genes could be potential biomarkers for HNSCC and provide a new insight into the diagnosis and treatment of head and neck tumors.
| S-EPMC7746451 | biostudies-literature
Project description:Quality control and normalization is considered the most important step in the analysis of microarray data. At present there are various methods available for quality assessments of microarray datasets. However there seems to be no standard visualization routine, which also depicts individual microarray quality. Here we present a convenient method for visualizing the results of standard quality control tests using Circos plots. In these plots various quality measurements are drawn in a circular fashion, thus allowing for visualization of the quality and all outliers of each distinct array within a microarray dataset. The proposed method is intended for use with the Affymetrix Human Genome platform (i.e., GPL 96, GPL570 and GPL571). Circos quality measurement plots are a convenient way for the initial quality estimate of Affymetrix datasets that are stored in publicly available databases.
| S-EPMC5003439 | biostudies-literature
Project description:PurposeLow-rank denoising of MRSI data results in an apparent increase in spectral SNR. However, it is not clear if this translates to a lower uncertainty in metabolite concentrations after spectroscopic fitting. Estimation of the true uncertainty after denoising is desirable for downstream analysis in spectroscopy. In this work, the uncertainty reduction from low-rank denoising methods based on spatiotemporal separability and linear predictability in MRSI are assessed. A new method for estimating metabolite concentration uncertainty after denoising is proposed. Automatic rank threshold selection methods are also assessed in simulated low SNR regimes.MethodsAssessment of denoising methods is conducted using Monte Carlo simulation of proton MRSI data and by reproducibility of repeated in vivo acquisitions in 5 subjects.ResultsIn simulated and in vivo data, spatiotemporal based denoising is shown to reduce the concentration uncertainty, but linear prediction denoising increases uncertainty. Uncertainty estimates provided by fitting algorithms after denoising consistently underestimate actual metabolite uncertainty. However, the proposed uncertainty estimation, based on an analytical expression for entry-wise variance after denoising, is more accurate. It is also shown automated rank threshold selection using Marchenko-Pastur distribution can bias the data in low SNR conditions. An alternative soft-thresholding function is proposed.ConclusionLow-rank denoising methods based on spatiotemporal separability do reduce uncertainty in MRS(I) data. However, thorough assessment is needed as assessment by SNR measured from residual baseline noise is insufficient given the presence of non-uniform variance. It is also important to select the right rank thresholding method in low SNR cases.
| S-EPMC7612041 | biostudies-literature
Project description:The data collected in this study consisted of 32 paired samples of cancerous and normal origins from 14 different patients and 8 different cancer types. Each pair of samples was taken from different parts of the same tissue in the same patient - one collected and extracted from the tumor and one collected and extracted from adjacent normal tissue. One pair from each of the breast, lymphoma and prostate tissues, 2 pairs from liver and lung tissues and 3 pairs from colon, ovary and testes tissues. 2 of the ovary and testes pairs were technical replicates. We use commercial adjacent tissue tumor-normal total RNA samples purchased from Ambion/ABI. The samples were hybridized to Agilent's miRNA microarray version 2.0. Keywords: miRNA For each microarray analysis, 100ng total RNAs were labeled with Cy3 per Agilent miRNA Micorarray Systems Protocol v1.5. The labeled RNA samples were hybridized onto Agilent miRNA microarray containing 799 miRNAs(Agilent Human miRNA Microarray kit, V2) for 21 hours at 55C. The arrays were then washed at room temperature and scanned to produce the hybridization signals (Agilent miRNA Micorarray Systems Protocol v1.5). The arrays were scanned with extended dynamic range at 5 and 100% PMT using the Agilent scanner (model G2565AA Technical replicate: Sample 7, Sample 29 Technical replicate: Sample 8, Sample 30 Technical replicate: Sample 19, Sample 31 Technical replicate: Sample 20, Sample 32
2010-05-18 | E-GEOD-14985 | biostudies-arrayexpress
Project description:In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. We applied this method to datasets extracted from both gene expression omnibus (GEO) and genetic association database (GAD). A total of 23,517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones. Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored. The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer.
| S-EPMC4060771 | biostudies-literature
Project description:The data collected in this study consisted of 32 paired samples of cancerous and normal origins from 14 different patients and 8 different cancer types. Each pair of samples was taken from different parts of the same tissue in the same patient - one collected and extracted from the tumor and one collected and extracted from adjacent normal tissue. One pair from each of the breast, lymphoma and prostate tissues, 2 pairs from liver and lung tissues and 3 pairs from colon, ovary and testes tissues. 2 of the ovary and testes pairs were technical replicates. We use commercial adjacent tissue tumor-normal total RNA samples purchased from Ambion/ABI. The samples were hybridized to Agilent's miRNA microarray version 2.0. Keywords: miRNA
2009-12-07 | GSE14985 | GEO
Project description:Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.
| S-EPMC6357556 | biostudies-literature
Project description:In spite of accumulating evidence suggesting that different complex traits share a common risk basis, namely pleiotropy, effective investigation of pleiotropic architecture still remains challenging. In order to address this challenge, we developed ShinyGPA, an interactive and dynamic visualization toolkit to investigate pleiotropic structure. ShinyGPA requires only the summary statistics from genome-wide association studies (GWAS), which reduces the burden on researchers using this tool. ShinyGPA allows users to effectively investigate genetic relationships among phenotypes using a flexible low-dimensional visualization and an intuitive user interface. In addition, ShinyGPA provides joint association mapping functionality that can facilitate biological understanding of the pleiotropic architecture. We analyzed GWAS summary statistics for 12 phenotypes using ShinyGPA and obtained visualization results and joint association mapping results that are well supported by the literature. The visualization produced by ShinyGPA can also be used as a hypothesis generating tool for relationships between phenotypes, which might also be used to improve the design of future genetic studies. ShinyGPA is currently available at https://dongjunchung.github.io/GPA/.
| S-EPMC5757942 | biostudies-literature
Project description:The development of high throughput screening (HTS) assays in the field of nanotoxicology provide new opportunities for the hazard assessment and ranking of engineered nanomaterials (ENMs). It is often necessary to rank lists of materials based on multiple risk assessment parameters, often aggregated across several measures of toxicity and possibly spanning an array of experimental platforms. Bayesian models coupled with the optimization of loss functions have been shown to provide an effective framework for conducting inference on ranks. In this article we present various loss-function-based ranking approaches for comparing ENM within experiments and toxicity parameters. Additionally, we propose a framework for the aggregation of ranks across different sources of evidence while allowing for differential weighting of this evidence based on its reliability and importance in risk ranking. We apply these methods to high throughput toxicity data on two human cell-lines, exposed to eight different nanomaterials, and measured in relation to four cytotoxicity outcomes. This article has supplementary material online.
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