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.

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 A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits

2019 ◽  
Vol 36 (3) ◽  
pp. 842-850 ◽  
Author(s):  
Cheng Peng ◽  
Jun Wang ◽  
Isaac Asante ◽  
Stan Louie ◽  
Ran Jin ◽  
...  
Keyword(s):  
Real Data ◽  
R Package ◽  
Integrative Model ◽  
Supplementary Information ◽  
Phenotypic Traits ◽  
Omics Data ◽  
Data Types ◽  
Specific Effects ◽  
Metabolomic Data ◽  
Future Prediction

Abstract Motivation Epidemiologic, clinical and translational studies are increasingly generating multiplatform omics data. Methods that can integrate across multiple high-dimensional data types while accounting for differential patterns are critical for uncovering novel associations and underlying relevant subgroups. Results We propose an integrative model to estimate latent unknown clusters (LUCID) aiming to both distinguish unique genomic, exposure and informative biomarkers/omic effects while jointly estimating subgroups relevant to the outcome of interest. Simulation studies indicate that we can obtain consistent estimates reflective of the true simulated values, accurately estimate subgroups and recapitulate subgroup-specific effects. We also demonstrate the use of the integrated model for future prediction of risk subgroups and phenotypes. We apply this approach to two real data applications to highlight the integration of genomic, exposure and metabolomic data. Availability and Implementation The LUCID method is implemented through the LUCIDus R package available on CRAN (https://CRAN.R-project.org/package=LUCIDus). Supplementary information Supplementary materials are available at Bioinformatics online.

scholarly journals scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment

2020 ◽  
Vol 36 (10) ◽  
pp. 3115-3123 ◽  
Author(s):  
Teng Fei ◽  
Tianwei Yu
Keyword(s):  
Single Cell ◽  
Differential Expression Analysis ◽  
Distance Matrix ◽  
Real Data ◽  
R Package ◽  
Batch Effect ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Differential Expression

Abstract Motivation Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. Results We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. Availability and implementation The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SPARSim single cell: a count data simulator for scRNA-seq data

2019 ◽  
Author(s):  
Giacomo Baruzzo ◽  
Ilaria Patuzzi ◽  
Barbara Di Camillo
Keyword(s):  
Single Cell ◽  
Count Data ◽  
Simulated Data ◽  
Real Data ◽  
R Package ◽  
Supplementary Information ◽  
Rna Seq ◽  
Distribution Of Zeros ◽  
New Methods ◽  
Research Fields

Abstract Motivation Single cell RNA-seq (scRNA-seq) count data show many differences compared with bulk RNA-seq count data, making the application of many RNA-seq pre-processing/analysis methods not straightforward or even inappropriate. For this reason, the development of new methods for handling scRNA-seq count data is currently one of the most active research fields in bioinformatics. To help the development of such new methods, the availability of simulated data could play a pivotal role. However, only few scRNA-seq count data simulators are available, often showing poor or not demonstrated similarity with real data. Results In this article we present SPARSim, a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. We demonstrate that SPARSim allows to generate count data that resemble real data in terms of count intensity, variability and sparsity, performing comparably or better than one of the most used scRNA-seq simulator, Splat. In particular, SPARSim simulated count matrices well resemble the distribution of zeros across different expression intensities observed in real count data. Availability and implementation SPARSim R package is freely available at http://sysbiobig.dei.unipd.it/? q=SPARSim and at https://gitlab.com/sysbiobig/sparsim. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals flexiMAP: a regression-based method for discovering differential alternative polyadenylation events in standard RNA-seq data

2020 ◽  
Author(s):  
Krzysztof J Szkop ◽  
David S Moss ◽  
Irene Nobeli
Keyword(s):  
Simulated Data ◽  
Alternative Polyadenylation ◽  
Real Data ◽  
R Package ◽  
Supplementary Information ◽  
Beta Regression ◽  
Rna Seq ◽  
Good Balance ◽  
Flexible Modeling ◽  
Specificity And Sensitivity

Abstract Motivation We present flexible Modeling of Alternative PolyAdenylation (flexiMAP), a new beta-regression-based method implemented in R, for discovering differential alternative polyadenylation events in standard RNA-seq data. Results We show, using both simulated and real data, that flexiMAP exhibits a good balance between specificity and sensitivity and compares favourably to existing methods, especially at low fold changes. In addition, the tests on simulated data reveal some hitherto unrecognized caveats of existing methods. Importantly, flexiMAP allows modeling of multiple known covariates that often confound the results of RNA-seq data analysis. Availability and implementation The flexiMAP R package is available at: https://github.com/kszkop/flexiMAP. Scripts and data to reproduce the analysis in this paper are available at: https://doi.org/10.5281/zenodo.3689788. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Matched Forest: supervised learning for high-dimensional matched case–control studies

2019 ◽  
Author(s):  
Nooshin Shomal Zadeh ◽  
Sangdi Lin ◽  
George C Runger
Keyword(s):  
Variable Selection ◽  
Case Control ◽  
Supplementary Information ◽  
High Dimensional ◽  
Control Analysis ◽  
Case Control Studies ◽  
Control Data ◽  
Matched Case ◽  
Case Control Analysis ◽  
And Control

Abstract Motivation Matched case–control analysis is widely used in biomedical studies to identify exposure variables associated with health conditions. The matching is used to improve the efficiency. Existing variable selection methods for matched case–control studies are challenged in high-dimensional settings where interactions among variables are also important. We describe a quite different method for high-dimensional matched case–control data, based on the potential outcome model, which is not only flexible regarding the number of matching and exposure variables but also able to detect interaction effects. Results We present Matched Forest (MF), an algorithm for variable selection in matched case–control data. The method preserves the case and control values in each instance but transforms the matched case–control data with added counterfactuals. A modified variable importance score from a supervised learner is used to detect important variables. The method is conceptually simple and can be applied with widely available software tools. Simulation studies show the effectiveness of MF in identifying important variables. MF is also applied to data from the biomedical domain and its performance is compared with alternative approaches. Availability and implementation R code for implementing MF is available at https://github.com/NooshinSh/Matched_Forest. Supplementary information Supplementary data are available at Bioinformatics online.

MetaTX: deciphering the distribution of mRNA-related features in the presence of isoform ambiguity, with applications in epitranscriptome analysis

2020 ◽  
Author(s):  
Yue Wang ◽  
Kunqi Chen ◽  
Zhen Wei ◽  
Frans Coenen ◽  
Jionglong Su ◽  
...  
Keyword(s):  
Distribution Pattern ◽  
Direct Methods ◽  
Real Data ◽  
R Package ◽  
Supplementary Information ◽  
Biological Features ◽  
Statistical Framework ◽  
Mrna Transcripts ◽  
Functional Relevance ◽  
Compositional Diversity

Abstract Motivation The distribution of biological features strongly indicates their functional relevance. Compared to DNA-related features, deciphering the distribution of mRNA-related features is non-trivial due to the existence of isoform ambiguity and compositional diversity of mRNAs. Results We propose here a rigorous statistical framework, MetaTX, for deciphering the distribution of mRNA-related features. Through a standardized mRNA model, MetaTX firstly unifies various mRNA transcripts of diverse compositions, and then corrects the isoform ambiguity by incorporating the overall distribution pattern of the features through an EM algorithm. MetaTX was tested on both simulated and real data. Results suggested that MetaTX substantially outperformed existing direct methods on simulated datasets, and that a more informative distribution pattern was produced for all the three datasets tested, which contain N6-Methyladenosine sites generated by different technologies. MetaTX should make a useful tool for studying the distribution and functions of mRNA-related biological features, especially for mRNA modifications such as N6-Methyladenosine. Availability and implementation The MetaTX R package is freely available at GitHub: https://github.com/yue-wang-biomath/MetaTX.1.0. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Partition: a surjective mapping approach for dimensionality reduction

2019 ◽  
Vol 36 (3) ◽  
pp. 676-681 ◽  
Author(s):  
Joshua Millstein ◽  
Francesca Battaglin ◽  
Malcolm Barrett ◽  
Shu Cao ◽  
Wu Zhang ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Multiple Testing ◽  
Progression Free Survival ◽  
Real Data ◽  
R Package ◽  
Information Loss ◽  
Response To Treatment ◽  
Supplementary Information ◽  
Surjective Mapping ◽  
New Feature

Abstract Motivation Large amounts of information generated by genomic technologies are accompanied by statistical and computational challenges due to redundancy, badly behaved data and noise. Dimensionality reduction (DR) methods have been developed to mitigate these challenges. However, many approaches are not scalable to large dimensions or result in excessive information loss. Results The proposed approach partitions data into subsets of related features and summarizes each into one and only one new feature, thus defining a surjective mapping. A constraint on information loss determines the size of the reduced dataset. Simulation studies demonstrate that when multiple related features are associated with a response, this approach can substantially increase the number of true associations detected as compared to principal components analysis, non-negative matrix factorization or no DR. This increase in true discoveries is explained both by a reduced multiple-testing challenge and a reduction in extraneous noise. In an application to real data collected from metastatic colorectal cancer tumors, more associations between gene expression features and progression free survival and response to treatment were detected in the reduced than in the full untransformed dataset. Availability and implementation Freely available R package from CRAN, https://cran.r-project.org/package=partition. Supplementary information Supplementary data are available at Bioinformatics online.

Interaction screening by Kendall’s partial correlation for ultrahigh-dimensional data with survival trait

2020 ◽  
Vol 36 (9) ◽  
pp. 2763-2769
Author(s):  
Jie-Huei Wang ◽  
Yi-Hau Chen
Keyword(s):  
Partial Correlation ◽  
Association Studies ◽  
B Cell Lymphoma ◽  
Real Data ◽  
R Package ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Interaction Screening ◽  
Relationship Of

Abstract Motivation In gene expression and genome-wide association studies, the identification of interaction effects is an important and challenging issue owing to its ultrahigh-dimensional nature. In particular, contaminated data and right-censored survival outcome make the associated feature screening even challenging. Results In this article, we propose an inverse probability-of-censoring weighted Kendall’s tau statistic to measure association of a survival trait with biomarkers, as well as a Kendall’s partial correlation statistic to measure the relationship of a survival trait with an interaction variable conditional on the main effects. The Kendall’s partial correlation is then used to conduct interaction screening. Simulation studies under various scenarios are performed to compare the performance of our proposal with some commonly available methods. In the real data application, we utilize our proposed method to identify epistasis associated with the clinical survival outcomes of non-small-cell lung cancer, diffuse large B-cell lymphoma and lung adenocarcinoma patients. Both simulation and real data studies demonstrate that our method performs well and outperforms existing methods in identifying main and interaction biomarkers. Availability and implementation R-package ‘IPCWK’ is available to implement this method, together with a reference manual describing how to perform the ‘IPCWK’ package. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document