scholarly journals Streamlining differential exon and 3′ UTR usage with diffUTR

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stefan Gerber ◽  
Gerhard Schratt ◽  
Pierre-Luc Germain
Keyword(s):  
State Of The Art ◽  
Real Data ◽  
Bioconductor Package ◽  
Transcriptomic Data ◽  
Biological Phenomena ◽  
Exon Usage ◽  
Usage Analysis

Abstract Background Despite the importance of alternative poly-adenylation and 3′ UTR length for a variety of biological phenomena, there are limited means of detecting UTR changes from standard transcriptomic data. Results We present the diffUTR Bioconductor package which streamlines and improves upon differential exon usage (DEU) analyses, and leverages existing DEU tools and alternative poly-adenylation site databases to enable differential 3′ UTR usage analysis. We demonstrate the diffUTR features and show that it is more flexible and more accurate than state-of-the-art alternatives, both in simulations and in real data. Conclusions diffUTR enables differential 3′ UTR analysis and more generally facilitates DEU and the exploration of their results.

scholarly journals Streamlining differential exon and 3’ UTR usage with diffUTR

2021 ◽  
Author(s):  
Stefan Gerber ◽  
Gerhard Schratt ◽  
Pierre-Luc Germain
Keyword(s):  
State Of The Art ◽  
Alternative Polyadenylation ◽  
Real Data ◽  
Polyadenylation Site ◽  
Bioconductor Package ◽  
Transcriptomic Data ◽  
Biological Phenomena ◽  
Exon Usage ◽  
Usage Analysis

AbstractBackgroundDespite the importance of alternative poly-adenylation and 3’ UTR length for a variety of biological phenomena, there are limited means of detecting UTR changes from standard transcriptomic data.ResultsWe present the diffUTR Bioconductor package which streamlines and improves upon differential exon usage (DEU) analyses, and leverages existing DEU tools and alternative polyadenylation site databases to enable differential 3’ UTR usage analysis. We demonstrate the diffUTR features and show that it is more flexible and more accurate than state-of-the-art alternatives, both in simulations and in real data.ConclusionsdiffUTR enables differential 3’ UTR analysis and more generally facilitates DEU and the exploration of their results.

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals The state of the art in soybean transcriptomics resources and gene coexpression networks

2021 ◽  
Author(s):  
Fabricio Almeida-Silva ◽  
Kanhu C Moharana ◽  
Thiago M Venancio
Keyword(s):  
State Of The Art ◽  
The State ◽  
Gene Coexpression Network ◽  
Rna Seq ◽  
Transcriptomic Data ◽  
The Past ◽  
Gene Coexpression ◽  
Genomics Research ◽  
Public Repositories ◽  
Coexpression Networks

Abstract In the past decade, over 3000 samples of soybean transcriptomic data have accumulated in public repositories. Here, we review the state of the art in soybean transcriptomics, highlighting the major microarray and RNA-seq studies that investigated soybean transcriptional programs in different tissues and conditions. Further, we propose approaches for integrating such big data using gene coexpression network and outline important web resources that may facilitate soybean data acquisition and analysis, contributing to the acceleration of soybean breeding and functional genomics research.

scholarly journals BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

2021 ◽  
Vol 17 (3) ◽  
pp. e1008256
Author(s):  
Shuonan Chen ◽  
Jackson Loper ◽  
Xiaoyin Chen ◽  
Alex Vaughan ◽  
Anthony M. Zador ◽  
...  
Keyword(s):  
State Of The Art ◽  
Spatial Regression ◽  
Image Data ◽  
Real Data ◽  
Optimization Methods ◽  
Spatial Density ◽  
Signal Recovery ◽  
Cloud Platform ◽  
Rna Transcripts ◽  
Different Types

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform.

Reproducibility-optimized detection of differential DNA methylation

Epigenomics ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 747-755
Author(s):  
Veronika Suni ◽  
Fatemeh Seyednasrollah ◽  
Bishwa Ghimire ◽  
Sini Junttila ◽  
Asta Laiho ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Throughput Sequencing ◽  
State Of The Art ◽  
Methylation Status ◽  
Real Data ◽  
Epigenetic Mechanism ◽  
Methylation Analysis ◽  
Test Statistic ◽  
Differentially Methylated Regions ◽  
Dna Methylation Analysis

Aim: DNA methylation is a key epigenetic mechanism regulating gene expression. Identifying differentially methylated regions is integral to DNA methylation analysis and there is a need for robust tools reliably detecting regions with significant differences in their methylation status. Materials & methods: We present here a reproducibility-optimized test statistic (ROTS) for detection of differential DNA methylation from high-throughput sequencing or array-based data. Results: Using both simulated and real data, we demonstrate the ability of ROTS to identify differential methylation between sample groups. Conclusion: Compared with state-of-the-art methods, ROTS shows competitive sensitivity and specificity in detecting consistently differentially methylated regions.

scholarly journals Optimal Prescriptive Trees

2019 ◽  
Vol 1 (2) ◽  
pp. 164-183 ◽  
Author(s):  
Dimitris Bertsimas ◽  
Jack Dunn ◽  
Nishanth Mundru
Keyword(s):  
Decision Making ◽  
Linear Models ◽  
State Of The Art ◽  
Online Advertising ◽  
Real Data ◽  
Attractive Alternative ◽  
High Quality ◽  
And Performance ◽  
Multiple Treatments ◽  
And Personalized Medicine

Motivated by personalized decision making, given observational data [Formula: see text] involving features [Formula: see text], assigned treatments or prescriptions [Formula: see text], and outcomes [Formula: see text], we propose a tree-based algorithm called optimal prescriptive tree (OPT) that uses either constant or linear models in the leaves of the tree to predict the counterfactuals and assign optimal treatments to new samples. We propose an objective function that balances optimality and accuracy. OPTs are interpretable and highly scalable, accommodate multiple treatments, and provide high-quality prescriptions. We report results involving synthetic and real data that show that OPTs either outperform or are comparable with several state-of-the-art methods. Given their combination of interpretability, scalability, generalizability, and performance, OPTs are an attractive alternative for personalized decision making in a variety of areas, such as online advertising and personalized medicine.

scholarly journals projectR: An R/Bioconductor package for transfer learning via PCA, NMF, correlation, and clustering

2019 ◽  
Author(s):  
Gaurav Sharma ◽  
Carlo Colantuoni ◽  
Loyal A Goff ◽  
Elana J Fertig ◽  
Genevieve Stein-O’Brien
Keyword(s):  
Single Cell ◽  
Transfer Learning ◽  
Single Cell Analysis ◽  
Ground Truth ◽  
Biological Data ◽  
Bioconductor Package ◽  
Reduction Techniques ◽  
Biological Phenomena ◽  
Feature Discovery ◽  
Dimension Reduction Techniques

AbstractMotivationDimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically import to large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically-driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset.ResultsWe developed an R/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation, and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis.AvailabilityprojectR is available on Bioconductor and at https://github.com/genesofeve/[email protected]; [email protected]

scholarly journals Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification

2021 ◽  
Author(s):  
Nelson Diaz ◽  
Juan Marcos ◽  
Esteban Vera ◽  
Henry Arguello
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Contextual Information ◽  
Real Data ◽  
Experimental Setup ◽  
Spectral Image ◽  
Data Set ◽  
Adaptive Sensing

Results of extensive simulations are shown for two state-of-the-art databases: Pavia University and Indian Pines. Furthermore, an experimental setup that performs the adaptive sensing was built to test the performance of the proposed approach on a real data set.

scholarly journals Latent Distribution Preserving Deep Subspace Clustering

2019 ◽  
Author(s):  
Lei Zhou ◽  
Xiao Bai ◽  
Dong Wang ◽  
Xianglong Liu ◽  
Jun Zhou ◽  
...  
Keyword(s):  
Linear Subspace ◽  
State Of The Art ◽  
Subspace Clustering ◽  
Real Data ◽  
High Dimensional ◽  
Clustering Methods ◽  
Real World Data ◽  
Latent Distribution ◽  
Computer Vision Applications ◽  
Strong Capacity

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is smaller than the ambient dimension. Traditional subspace clustering methods often rely on the self-expressiveness property, which has proven effective for linear subspace clustering. However, they perform unsatisfactorily on real data with complex nonlinear subspaces. More recently, deep autoencoder based subspace clustering methods have achieved success owning to the more powerful representation extracted by the autoencoder network. Unfortunately, these methods only considering the reconstruction of original input data can hardly guarantee the latent representation for the data distributed in subspaces, which inevitably limits the performance in practice. In this paper, we propose a novel deep subspace clustering method based on a latent distribution-preserving autoencoder, which introduces a distribution consistency loss to guide the learning of distribution-preserving latent representation, and consequently enables strong capacity of characterizing the real-world data for subspace clustering. Experimental results on several public databases show that our method achieves significant improvement compared with the state-of-the-art subspace clustering methods.

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