CAGExploreR: an R package for the analysis and visualization of promoter dynamics across multiple experiments

2014 ◽  
Vol 30 (8) ◽  
pp. 1183-1184 ◽  
Author(s):  
Emmanuel Dimont ◽  
Oliver Hofmann ◽  
Shannan J Ho Sui ◽  
Alistair R R Forrest ◽  
Hideya Kawaji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Real Data ◽  
Cell Types ◽  
R Package ◽  
Supplementary Information ◽  
Promoter Regions ◽  
Powerful Approach ◽  
Alternate Promoter ◽  
Cap Analysis ◽  
Insight Into

Summary Alternate promoter usage is an important molecular mechanism for generating RNA and protein diversity. Cap Analysis Gene Expression (CAGE) is a powerful approach for revealing the multiplicity of transcription start site (TSS) events across experiments and conditions. An understanding of the dynamics of TSS choice across these conditions requires both sensitive quantification and comparative visualization. We have developed CAGExploreR, an R package to detect and visualize changes in the use of specific TSS in wider promoter regions in the context of changes in overall gene expression when comparing different CAGE samples. These changes provide insight into the modification of transcript isoform generation and regulatory network alterations associated with cell types and conditions. CAGExploreR is based on the FANTOM5 and MPromDb promoter set definitions but can also work with user-supplied regions. The package compares multiple CAGE libraries simultaneously. Supplementary Materials describe methods in detail, and a vignette demonstrates a workflow with a real data example. Availability and implementation: The package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/CAGExploreR). Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals A statistical simulator scDesign for rational scRNA-seq experimental design

2019 ◽  
Vol 35 (14) ◽  
pp. i41-i50 ◽  
Author(s):  
Wei Vivian Li ◽  
Jingyi Jessica Li
Keyword(s):  
Biological Sciences ◽  
Gene Expression ◽  
Experimental Design ◽  
Method Development ◽  
Cell Types ◽  
R Package ◽  
Computational Method ◽  
Supplementary Information ◽  
Reduction Methods ◽  
Sequencing Platforms

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. Results Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA–seq computational methods based on specific research goals. Availability and implementation We have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

2019 ◽  
Vol 36 (7) ◽  
pp. 2017-2024
Author(s):  
Weiwei Zhang ◽  
Ziyi Li ◽  
Nana Wei ◽  
Hua-Jun Wu ◽  
Xiaoqi Zheng
Keyword(s):  
Real Data ◽  
R Package ◽  
Differential Methylation ◽  
Least Square ◽  
Epigenetic Mechanism ◽  
Supplementary Information ◽  
Cpg Sites ◽  
Tumor Purity ◽  
Different Sources ◽  
Normal Controls

Abstract Motivation Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for. Results We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose. Availability and implementation InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

2020 ◽  
Author(s):  
Nurlan Kerimov ◽  
James D Hayhurst ◽  
Kateryna Peikova ◽  
Jonathan R Manning ◽  
Peter Walter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Cell Types ◽  
Summary Statistics ◽  
Ensembl Genome Browser ◽  
Cell Type Specific ◽  
Trait Locus ◽  
Rest Api ◽  
Complex Human Traits ◽  
Insight Into

An increasing number of gene expression quantitative trait locus (eQTL) studies have made summary statistics publicly available, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and colocalisation. However, differences between these datasets, in their variants tested, allele codings, and in the transcriptional features quantified, are a barrier to their widespread use. Consequently, target genes for most GWAS signals have still not been identified. Here, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl/), a resource which contains quality controlled, uniformly re-computed QTLs from 21 eQTL studies. We find that for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies, enabling the integrative analysis of these data. Although most cis-eQTLs were shared between most bulk tissues, the analysis of purified cell types identified a greater diversity of cell-type-specific eQTLs, a subset of which also manifested as novel disease colocalisations. Our summary statistics can be downloaded by FTP, accessed via a REST API, and visualised on the Ensembl genome browser. New datasets will continuously be added to the eQTL Catalogue, enabling the systematic interpretation of human GWAS associations across many cell types and tissues.

scholarly journals Chicdiff: a computational pipeline for detecting differential chromosomal interactions in Capture Hi-C data

2019 ◽  
Vol 35 (22) ◽  
pp. 4764-4766 ◽  
Author(s):  
Jonathan Cairns ◽  
William R Orchard ◽  
Valeriya Malysheva ◽  
Mikhail Spivakov
Keyword(s):  
Multiple Testing ◽  
Positive Association ◽  
R Package ◽  
Supplementary Information ◽  
Gene Promoters ◽  
Robust Detection ◽  
Testing Procedures ◽  
Multiple Testing Procedures ◽  
Powerful Approach ◽  
Cell Type Specific

Abstract Summary Capture Hi-C is a powerful approach for detecting chromosomal interactions involving, at least on one end, DNA regions of interest, such as gene promoters. We present Chicdiff, an R package for robust detection of differential interactions in Capture Hi-C data. Chicdiff enhances a state-of-the-art differential testing approach for count data with bespoke normalization and multiple testing procedures that account for specific statistical properties of Capture Hi-C. We validate Chicdiff on published Promoter Capture Hi-C data in human Monocytes and CD4+ T cells, identifying multitudes of cell type-specific interactions, and confirming the overall positive association between promoter interactions and gene expression. Availability and implementation Chicdiff is implemented as an R package that is publicly available at https://github.com/RegulatoryGenomicsGroup/chicdiff. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals iMIRAGE: an R package to impute microRNA expression using protein-coding genes

2019 ◽  
Vol 36 (8) ◽  
pp. 2608-2610
Author(s):  
Aritro Nath ◽  
Jeremy Chang ◽  
R Stephanie Huang
Keyword(s):  
Gene Expression ◽  
Small Rnas ◽  
Transcriptional Regulators ◽  
R Package ◽  
Machine Learning Algorithms ◽  
Supplementary Information ◽  
Expression Data ◽  
Protein Coding ◽  
Altered Protein ◽  
Independent Test

Abstract Summary MicroRNAs (miRNAs) are critical post-transcriptional regulators of gene expression. Due to challenges in accurate profiling of small RNAs, a vast majority of public transcriptome datasets lack reliable miRNA profiles. However, the biological consequence of miRNA activity in the form of altered protein-coding gene (PCG) expression can be captured using machine-learning algorithms. Here, we present iMIRAGE (imputed miRNA activity from gene expression), a convenient tool to predict miRNA expression using PCG expression of the test datasets. The iMIRAGE package provides an integrated workflow for normalization and transformation of miRNA and PCG expression data, along with the option to utilize predicted miRNA targets to impute miRNA activity from independent test PCG datasets. Availability and implementation The iMIRAGE package for R, along with package documentation and vignette, is available at https://aritronath.github.io/iMIRAGE/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Dissecting differential signals in high-throughput data from complex tissues

2019 ◽  
Vol 35 (20) ◽  
pp. 3898-3905 ◽  
Author(s):  
Ziyi Li ◽  
Zhijin Wu ◽  
Peng Jin ◽  
Hao Wu
Keyword(s):  
High Throughput ◽  
Cell Types ◽  
R Package ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Clinical Practices ◽  
High Throughput Data ◽  
Heterogeneous Samples ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract Motivation Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for. Results We develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a variety of cell-type specific changes. Extensive simulation studies and analyses of two real datasets demonstrate the favorable performance of our proposed method compared with existing ones serving similar purpose. Availability and implementation The proposed method is implemented as an R package and is freely available on GitHub (https://github.com/ziyili20/TOAST). Supplementary information Supplementary data are available at Bioinformatics online.

powmic: an R package for power assessment in microbiome case–control studies

2020 ◽  
Vol 36 (11) ◽  
pp. 3563-3565
Author(s):  
Li Chen
Keyword(s):  
Power Analysis ◽  
Real Data ◽  
Analytical Form ◽  
R Package ◽  
Case Control ◽  
Supplementary Information ◽  
Metagenomic Sequencing ◽  
Case Control Studies ◽  
Simulation Based ◽  
Over Dispersion

Abstract Summary Power analysis is essential to decide the sample size of metagenomic sequencing experiments in a case–control study for identifying differentially abundant (DA) microbes. However, the complexity of microbial data characteristics, such as excessive zeros, over-dispersion, compositionality, intrinsically microbial correlations and variable sequencing depths, makes the power analysis particularly challenging because the analytical form is usually unavailable. Here, we develop a simulation-based power assessment strategy and R package powmic, which considers the complexity of microbial data characteristics. A real data example demonstrates the usage of powmic. Availability and implementation powmic R package and online tutorial are available at https://github.com/lichen-lab/powmic. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MTTFsite: cross-cell type TF binding site prediction by using multi-task learning

2019 ◽  
Vol 35 (24) ◽  
pp. 5067-5077 ◽  
Author(s):  
Jiyun Zhou ◽  
Qin Lu ◽  
Lin Gui ◽  
Ruifeng Xu ◽  
Yunfei Long ◽  
...  
Keyword(s):  
Gene Expression ◽  
Histone Modification ◽  
Prediction Method ◽  
Cell Types ◽  
Supplementary Information ◽  
Learning Approaches ◽  
Cell Type ◽  
Baseline Method ◽  
Common Features ◽  
Task Learning

AbstractMotivationThe prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data.ResultsIn this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained.Availability and implementationThe resource and executable code are freely available at http://hlt.hitsz.edu.cn/MTTFsite/ and http://www.hitsz-hlt.com:8080/MTTFsite/.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals DataRemix: a universal data transformation for optimal inference from gene expression datasets

2020 ◽  
Author(s):  
Weiguang Mao ◽  
Javad Rahimikollu ◽  
Ryan Hausler ◽  
Maria Chikina
Keyword(s):  
Gene Expression ◽  
R Package ◽  
Supplementary Information ◽  
Eqtl Analysis ◽  
Thompson Sampling ◽  
Normalization Methods ◽  
Special Cases ◽  
Biological Signals ◽  
Gene Correlation ◽  
Value Decomposition

Abstract Motivation RNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study. Results We describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-k approximation and removing the top k principal components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweigh the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also show that DataRemix can be efficiently optimized via Thompson sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally, we apply our method to the Religious Orders Study and Memory and Aging Project dataset, and we report what to our knowledge is the first replicable trans-eQTL effect in human brain. Availabilityand implementation DataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix). Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document