scholarly journals Stereo3D: using stereo images to enrich 3D visualization

2020 ◽  
Vol 36 (14) ◽  
pp. 4189-4190
Author(s):  
Yang Liu ◽  
Vinod Kumar Singh ◽  
Deyou Zheng
Keyword(s):  
3D Visualization ◽  
Complex Structure ◽  
R Package ◽  
Supplementary Information ◽  
High Dimensional ◽  
Complex Data ◽  
Stereo Images ◽  
3D Space ◽  
Visualization Software ◽  
Critical Process

Abstract Summary Visualization in 3D space is a standard but critical process for examining the complex structure of high-dimensional data. Stereoscopic imaging technology can be adopted to enhance 3D representation of many complex data, especially those consisting of points and lines. We illustrate the simple steps that are involved and strongly recommend others to implement it in designing visualization software. To facilitate its application, we created a new software that can convert a regular 3D scatterplot or network figure to a pair of stereo images. Availability and implementation Stereo3D is freely available as an open source R package released under an MIT license at https://github.com/bioinfoDZ/Stereo3D. Others can integrate the codes and implement the method in academic software. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data

2019 ◽  
Vol 36 (6) ◽  
pp. 1785-1794
Author(s):  
Jun Li ◽  
Qing Lu ◽  
Yalu Wen
Keyword(s):  
Risk Prediction ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
R Package ◽  
Kernel Functions ◽  
Adaptive Lasso ◽  
Supplementary Information ◽  
High Dimensional ◽  
Omics Data ◽  
Modeling Framework

Abstract Motivation The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed. Results We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer’s Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction. Availability and implementation The R-package is available at https://github.com/YaluWen/OmicPred. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A variable selection approach for highly correlated predictors in high-dimensional genomic data

2021 ◽  
Author(s):  
Wencan Zhu ◽  
Céline Lévy-Leduc ◽  
Nils Ternès
Keyword(s):  
Variable Selection ◽  
Linear Models ◽  
Synthetic Data ◽  
R Package ◽  
Supplementary Information ◽  
High Dimensional ◽  
Selection Approach ◽  
Generalized Lasso ◽  
Genomic Studies ◽  
Highly Correlated

Abstract Motivation In genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. Regularized approaches are classically used to perform variable selection in high-dimensional linear models. However, these methods can fail in highly correlated settings. Results We propose a novel variable selection approach called WLasso, taking these correlations into account. It consists in rewriting the initial high-dimensional linear model to remove the correlation between the biomarkers (predictors) and in applying the generalized Lasso criterion. The performance of WLasso is assessed using synthetic data in several scenarios and compared with recent alternative approaches. The results show that when the biomarkers are highly correlated, WLasso outperforms the other approaches in sparse high-dimensional frameworks. The method is also illustrated on publicly available gene expression data in breast cancer. Availabilityand implementation Our method is implemented in the WLasso R package which is available from the Comprehensive R Archive Network (CRAN). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Augmented reality in scientific visualization and communications: a new dawn of looking at antibody interactions

2020 ◽  
Vol 3 (3) ◽  
pp. 221-226
Author(s):  
Kwok-Fong Chan ◽  
Jun-Jie Poh ◽  
Wei-Ling Wu ◽  
Samuel Ken-En Gan
Keyword(s):  
Augmented Reality ◽  
Medical Training ◽  
3D Visualization ◽  
Three Dimensional ◽  
Molecular Structures ◽  
Stereo Images ◽  
Molecular Visualization ◽  
Holistic View ◽  
Visualization Software ◽  
Antibody Interactions

ABSTRACT The use of augmented reality (AR) in providing three-dimensional (3D) visual support and image depth have been applied in education, tourism, historical studies, and medical training. In research and development, there has been a slow but growing use of AR tools in chemical and drug discovery, but little has been implemented for whole 3D antibody structures (IgE, IgM, IgA, IgG, and IgD) and in communicating their interactions with the antigens or receptors in publications. Given that antibody interactions can vary significantly between different monoclonal antibodies, a convenient and easy to use 3D visualization can convey structural mechanisms clearer to readers, especially in how residues may interact with one another. While this was previously constrained to the use of stereo images on printed material or molecular visualization software on the computer, the revolution of smartphone and phablets now allows visualization of whole molecular structures on-the-go, allowing rotations, zooming in and out, and even animations without complex devices or the training of visual prowess. While not yet as versatile as molecular visualization software on the computer, such technology is an improvement from stereo-images and bridges the gap with molecular visualization tools. In this report, we discuss the use of AR and how they can be employed in the holistic view of antibodies and the future of the technology for better scientific communication.

scholarly journals Soft windowing application to improve analysis of high-throughput phenotyping data

2019 ◽  
Vol 36 (5) ◽  
pp. 1492-1500 ◽  
Author(s):  
Hamed Haselimashhadi ◽  
Jeremy C Mason ◽  
Violeta Munoz-Fuentes ◽  
Federico López-Gómez ◽  
Kolawole Babalola ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Methodological Approach ◽  
R Package ◽  
Supplementary Information ◽  
Complex Data ◽  
Control Data ◽  
International Mouse Phenotyping Consortium ◽  
High Throughput Phenotyping ◽  
The Uk

Abstract Motivation High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. Results Here we introduce ‘soft windowing’, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype–phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. Availability and implementation The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer

2020 ◽  
Author(s):  
Quanxue Li ◽  
Wentao Dai ◽  
Jixiang Liu ◽  
Qingqing Sang ◽  
Yi-Xue Li ◽  
...  
Keyword(s):  
Source Code ◽  
R Package ◽  
Supplementary Information ◽  
High Dimensional ◽  
Supplementary Data ◽  
Analysis Framework ◽  
Gene Dysregulation ◽  
Cell Processes ◽  
Effective Path

Abstract Summary Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis. Availability and implementation The source code and user’s guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig. Supplementary information Supplementary data are available at Bioinformatics online.

X-Ray Nanotomography (XRMT) Tool for Non-Destructive High-Resolution Imaging of ICs

2001 ◽  
Author(s):  
Wenbing Yun ◽  
Steve Wang ◽  
David Scott ◽  
Kenneth W. Nill ◽  
Waleed S. Haddad
Keyword(s):  
High Resolution ◽  
Process Development ◽  
3D Visualization ◽  
Tomographic Reconstruction ◽  
Reconstruction Algorithms ◽  
X Ray ◽  
Visualization Software ◽  
Volumetric Reconstruction ◽  
Resolution Imaging ◽  
Non Destructive

Abstract A high-resolution table-sized x-ray nanotomography (XRMT) tool has been constructed that shows the promise of nondestructively imaging the internal structure of a full IC stack with a spatial resolution better than 100 nm. Such a tool can be used to detect, localize, and characterize buried defects in the IC. By collecting a set of X-ray projections through the full IC (which may include tens of micrometers of silicon substrate and several layers of Cu interconnects) and applying tomographic reconstruction algorithms to these projections, a 3D volumetric reconstruction can be obtained, and analyzed for defects using 3D visualization software. XRMT is a powerful technique that will find use in failure analysis and IC process development, and may facilitate or supplant investigations using SEM, TEM, and FIB tools, which generally require destructive sample preparation and a vacuum environment.

ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

2021 ◽  
Author(s):  
Irzam Sarfraz ◽  
Muhammad Asif ◽  
Joshua D Campbell
Keyword(s):  
Single Cell ◽  
R Package ◽  
Poor Quality ◽  
Data Matrix ◽  
Supplementary Information ◽  
Data Provenance ◽  
Rna Seq ◽  
Efficient Management ◽  
The Matrix ◽  
The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

2021 ◽  
Author(s):  
Darawan Rinchai ◽  
Jessica Roelands ◽  
Mohammed Toufiq ◽  
Wouter Hendrickx ◽  
Matthew C Altman ◽  
...  
Keyword(s):  
Transcript Abundance ◽  
R Package ◽  
Supplementary Information ◽  
Illustrative Case ◽  
Bioinformatic Tools ◽  
Transcriptional Module ◽  
Wide Range ◽  
Downstream Analysis ◽  
Computing Module ◽  
Parallel Workflow

Abstract Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

movAPA: modeling and visualization of dynamics of alternative polyadenylation across biological samples

2020 ◽  
Author(s):  
Wenbin Ye ◽  
Tao Liu ◽  
Hongjuan Fu ◽  
Congting Ye ◽  
Guoli Ji ◽  
...  
Keyword(s):  
Biological Samples ◽  
Tissue Specificity ◽  
Single Cells ◽  
Alternative Polyadenylation ◽  
R Package ◽  
Supplementary Information ◽  
Rna Seq ◽  
Mouse Sperm ◽  
High Scalability ◽  
A Site

Abstract Motivation Alternative polyadenylation (APA) has been widely recognized as a widespread mechanism modulated dynamically. Studies based on 3′ end sequencing and/or RNA-seq have profiled poly(A) sites in various species with diverse pipelines, yet no unified and easy-to-use toolkit is available for comprehensive APA analyses. Results We developed an R package called movAPA for modeling and visualization of dynamics of alternative polyadenylation across biological samples. movAPA incorporates rich functions for preprocessing, annotation and statistical analyses of poly(A) sites, identification of poly(A) signals, profiling of APA dynamics and visualization. Particularly, seven metrics are provided for measuring the tissue-specificity or usages of APA sites across samples. Three methods are used for identifying 3′ UTR shortening/lengthening events between conditions. APA site switching involving non-3′ UTR polyadenylation can also be explored. Using poly(A) site data from rice and mouse sperm cells, we demonstrated the high scalability and flexibility of movAPA in profiling APA dynamics across tissues and single cells. Availability and implementation https://github.com/BMILAB/movAPA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

2019 ◽  
Vol 35 (21) ◽  
pp. 4356-4363 ◽  
Author(s):  
Gaëlle Lefort ◽  
Laurence Liaubet ◽  
Cécile Canlet ◽  
Patrick Tardivel ◽  
Marie-Christine Père ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Nmr Spectra ◽  
Complex Mixture ◽  
R Package ◽  
Statistical Analyses ◽  
Supplementary Information ◽  
Automatic Identification ◽  
Analysis Workflow ◽  
Expert Analysis ◽  
New Biomarkers

Abstract Motivation In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way. Results We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition. Availability and implementation ASICS is distributed as an R package, available on Bioconductor. Supplementary information Supplementary data are available at Bioinformatics online.

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