An R package for divergence analysis of omics data.
Ontology highlight
ABSTRACT:
SUBMITTER: Dinalankara W
PROVIDER: S-EPMC8021195 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Multilayer omics profiling has become a major venue for understanding complex diseases. We develop NCutYX, an R package for clustering analysis of multilayer omics data. The package and methods jointly analyze multiple layers of omics measurements and effectively accommodate their regulations. They systematically conduct a series of analysis based on the normalized cut technique, including the clusterings of subjects and omics measurements and biclustering. The package can be valuable for its timely context, novel methods, and comprehensiveness. https://cran.r-project.org/web/packages/NCutYX/. Supplementary data are available at Bioinformatics online.
| S-EPMC8597621 | biostudies-literature
Project description:BackgroundPathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis.ResultstRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command 'tRigon::run_tRigon()'. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon.ConclusionstRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.
| S-EPMC10916305 | biostudies-literature
Project description:The authors present pathwayPCA, an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic-net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple covariates and/or interaction effects. It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets. In addition, several case studies are provided to illustrate pathwayPCA analysis with gene selection, estimating, and visualizing sample-specific pathway activities, identifying sex-specific pathway effects in kidney cancer, and building integrative models for predicting patient prognosis. pathwayPCA is an open-source R package, freely available through the Bioconductor repository. pathwayPCA is expected to be a useful tool for empowering the wider scientific community to analyze and interpret the wealth of available proteomics data, along with other types of molecular data recently made available by Clinical Proteomic Tumor Analysis Consortium and other large consortiums.
| S-EPMC7677175 | biostudies-literature
Project description:BackgroundThe statistical evaluation of pathway enrichment, i.e. of gene profiles' confluence to the pathway level, allows exploring molecular landscapes using functionally annotated gene sets. However, pathway scores can also be used as predictive features in machine learning. That requires, firstly, increasing statistical power and biological relevance via a network enrichment analysis (NEA) and, secondly, a fast and convenient procedure for rendering the original data into a space of pathway scores. However, previous implementations of NEA involved multiple runs of network randomization and were therefore slow.ResultsHere, we present a new R package NEArender which can transform raw 'omics' features of experimental or clinical samples into matrices describing the same samples with many fewer NEA-based pathway scores. This is done via a parametric estimation of the null binomial distribution and is thus much faster and less biased than randomization procedures. Further, we compare estimates from these two alternative procedures and demonstrate that the summarization of individual genes to pathways increases the statistical power compared to both the default differential expression analysis on individual genes and the state-of-the-art gene set enrichment analysis. The package also contains functions for preparing input, modeling null distributions, and evaluating alternative versions of the global network.ConclusionsBeyond the state-of-the-art exploration of molecular data through pathway enrichment, score matrices produced by NEArender can be used in larger bioinformatics pipelines as input for phenotype modeling, predicting disease outcomes etc. This approach is often more sensitive and robust than using the original data. The package NEArender is complementary to the online NEA tool EviNet ( https://www.evinet.org ) and, unlike of the latter, enables high performance of computations off-line. The R package NEArender version 1.4 is available at CRAN repository https://cran.r-project.org/web/packages/NEArender/.
| S-EPMC5374688 | biostudies-literature
Project description:Biologists are increasingly confronted with the challenge of quickly understanding genome-wide biological data, which usually involve a large number of genomic coordinates (e.g. genes) but a much smaller number of samples. To meet the need for data of this shape, we present an open-source package called 'supraHex' for training, analysing and visualising omics data. This package devises a supra-hexagonal map to self-organise the input data, offers scalable functionalities for post-analysing the map, and more importantly, allows for overlaying additional data for multilayer omics data comparisons. Via applying to DNA replication timing data of mouse embryogenesis, we demonstrate that supraHex is capable of simultaneously carrying out gene clustering and sample correlation, providing intuitive visualisation at each step of the analysis. By overlaying CpG and expression data onto the trained replication-timing map, we also show that supraHex is able to intuitively capture an inherent relationship between late replication, low CpG density promoters and low expression levels. As part of the Bioconductor project, supraHex makes accessible to a wide community in a simple way, what would otherwise be a complex framework for the ultrafast understanding of any tabular omics data, both scientifically and artistically. This package can run on Windows, Mac and Linux, and is freely available together with many tutorials on featuring real examples at http://supfam.org/supraHex.
| S-EPMC3905187 | biostudies-literature
Project description:BackgroundPan-cancer analysis examines both the commonalities and heterogeneity among genomic and cellular alterations across numerous types of tumors. Pan-cancer analysis of gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TIME), and methylation becomes available based on the multi-omics data from The Cancer Genome Atlas Program (TCGA). Some online tools provide analysis of gene and protein expression, mutation, methylation, and survival for TCGA data. However, these online tools were either Uni-functional or were not able to perform analysis of user-defined functions. Therefore, we created the TCGAplot R package to facilitate perform pan-cancer analysis and visualization of the built-in multi-omic TCGA data.ResultsTCGAplot provides several functions to perform pan-cancer paired/unpaired differential gene expression analysis, pan-cancer correlation analysis between gene expression and TMB, MSI, TIME, and promoter methylation. Functions for visualization include paired/unpaired boxplot, survival plot, ROC curve, heatmap, scatter, radar chart, and forest plot. Moreover, gene set based pan-cancer and tumor specific analyses were also available. Finally, all these built-in multi-omic data could be extracted for implementation for user-defined functions, making the pan-cancer analysis much more convenient.\ CONCLUSIONS: We developed an R-package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. The source code and pre-built package are available at GitHub ( https://github.com/tjhwangxiong/TCGAplot ).
| S-EPMC10726608 | biostudies-literature
Project description:BACKGROUND:With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. RESULTS:We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. CONCLUSIONS:We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLS and can be installed in R via install.packages("OmicsPLS").
| S-EPMC6182835 | biostudies-other
Project description:The advent of high throughput technologies has led to a wealth of publicly available 'omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a 'molecular signature') to explain or predict biological conditions, but mainly for a single type of 'omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous 'omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple 'omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of 'omics data available from the package.
| S-EPMC5687754 | biostudies-literature
Project description:OmicCircos is an R software package used to generate high-quality circular plots for visualizing genomic variations, including mutation patterns, copy number variations (CNVs), expression patterns, and methylation patterns. Such variations can be displayed as scatterplot, line, or text-label figures. Relationships among genomic features in different chromosome positions can be represented in the forms of polygons or curves. Utilizing the statistical and graphic functions in an R/Bioconductor environment, OmicCircos performs statistical analyses and displays results using cluster, boxplot, histogram, and heatmap formats. In addition, OmicCircos offers a number of unique capabilities, including independent track drawing for easy modification and integration, zoom functions, link-polygons, and position-independent heatmaps supporting detailed visualization.OmicCircos is available through Bioconductor at http://www.bioconductor.org/packages/devel/bioc/html/OmicCircos.html. An extensive vignette in the package describes installation, data formatting, and workflow procedures. The software is open source under the Artistic-2.0 license.
| S-EPMC3921174 | biostudies-other
Project description:MotivationDeciphering molecular signals from omics data helps understanding cellular processes and disease progression. Effective algorithms for extracting these signals are essential, with a strong emphasis on robustness and reproducibility.ResultsR/Bioconductor package consICA implements consensus independent component analysis (ICA)-a data-driven deconvolution method to decompose heterogeneous omics data and extract features suitable for patient stratification and multimodal data integration. The method separates biologically relevant molecular signals from technical effects and provides information about the cellular composition and biological processes. Build-in annotation, survival analysis, and report generation provide useful tools for the interpretation of extracted signals. The implementation of parallel computing in the package ensures efficient analysis using modern multicore systems. The package offers a reproducible and efficient data-driven solution for the analysis of complex molecular profiles, with significant implications for cancer research.Availability and implementationThe package is implemented in R and available under MIT license at Bioconductor (https://bioconductor.org/packages/consICA) or at GitHub (https://github.com/biomod-lih/consICA).
| S-EPMC11257712 | biostudies-literature