LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Jiehuan Sun ◽  
Jose D. Herazo-Maya ◽  
Jane-Ling Wang ◽  
Naftali Kaminski ◽  
Hongyu Zhao
Keyword(s):  
Gene Expression ◽  
R Package ◽  
Survival Outcome ◽  
Survival Outcomes ◽  
Simulation Studies ◽  
Expression Data ◽  
Computationally Efficient ◽  
Efficient Tool ◽  
Extensive Simulation ◽  
Improved Performance

Abstract Longitudinal genomics data and survival outcome are common in biomedical studies, where the genomics data are often of high dimension. It is of great interest to select informative longitudinal biomarkers (e.g. genes) related to the survival outcome. In this paper, we develop a computationally efficient tool, LCox, for selecting informative biomarkers related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome. We show that LCox has improved performance compared to existing methods through extensive simulation studies. In addition, by applying LCox to a dataset of patients with idiopathic pulmonary fibrosis, we are able to identify biologically meaningful genes while all other methods fail to make any discovery. An R package to perform LCox is freely available at https://CRAN.R-project.org/package=LCox.

scholarly journals Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty

F1000Research ◽  
2022 ◽  
Vol 9 ◽  
pp. 1159
Author(s):  
Qian (Vicky) Wu ◽  
Wei Sun ◽  
Li Hsu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Penalized Regression ◽  
R Package ◽  
Expression Data ◽  
Computationally Efficient ◽  
P Gene ◽  
Novel Approach

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs.Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog

scholarly journals Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1159
Author(s):  
Qian (Vicky) Wu ◽  
Wei Sun ◽  
Li Hsu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Penalized Regression ◽  
R Package ◽  
Expression Data ◽  
Computationally Efficient ◽  
P Gene ◽  
Novel Approach

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs.Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog

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 REGULARIZATION STRATEGIES FOR HYPERPLANE CLASSIFIERS: APPLICATION TO CANCER CLASSIFICATION WITH GENE EXPRESSION DATA

2007 ◽  
Vol 05 (01) ◽  
pp. 79-104 ◽  
Author(s):  
ERIK ANDRIES ◽  
THOMAS HAGSTROM ◽  
SUSAN R. ATLAS ◽  
CHERYL WILLMAN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Linear Equations ◽  
Numerical Linear Algebra ◽  
Point Of View ◽  
Diagnostic Tools ◽  
Expression Data ◽  
Linear Discrimination ◽  
Improved Performance ◽  
Value Decomposition

Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularization. Here, we examine the ill-posedness involved in the linear discrimination of cancer gene expression data with respect to outcome and tumor subclasses. We show that a filter factor representation, based upon Singular Value Decomposition, yields insight into the numerical ill-posedness of the hyperplane-based separation when applied to gene expression data. We also show that this representation yields useful diagnostic tools for guiding the selection of classifier parameters, thus leading to improved performance.

Predicting survival outcomes in ovarian cancer using gene expression data

2018 ◽  
Vol 21 (4) ◽  
pp. 339
Author(s):  
Taesung Park ◽  
Se Ik Kim ◽  
Yonggab Kim ◽  
TaeJin Ahn ◽  
Nayeon Kang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Data ◽  
Survival Outcomes ◽  
Expression Data

scholarly journals NormExpression: an R package to normalize gene expression data using evaluated methods

2018 ◽  
Author(s):  
Zhenfeng Wu ◽  
Weixiang Liu ◽  
Xiufeng Jin ◽  
Deshui Yu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
R Package ◽  
Data Normalization ◽  
Expression Data ◽  
Normalization Methods ◽  
Normalize Gene Expression

AbstractData normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the current normalization methods, the different metrics yield inconsistent results. In this study, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods, achieving consistency in our evaluation results using both bulk RNA-seq and scRNA-seq data from the same library construction protocol. This consistency has validated the underlying theory that a sucessiful normalization method simultaneously maximizes the number of uniform genes and minimizes the correlation between the expression profiles of gene pairs. This consistency can also be used to analyze the quality of gene expression data. The gene expression data, normalization methods and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis.

scholarly journals target: an R package to predict combined function of transcription factors

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 344
Author(s):  
Mahmoud Ahmed ◽  
Deok Ryong Kim
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
R Package ◽  
Expression Data ◽  
Bioconductor Package ◽  
Chip Experiment ◽  
Binding Data ◽  
Two Factors

Researchers use ChIP binding data to identify potential transcription factor binding sites. Similarly, they use gene expression data from sequencing or microarrays to quantify the effect of the factor overexpression or knockdown on its targets. Therefore, the integration of the binding and expression data can be used to improve the understanding of a transcription factor function. Here, we implemented the binding and expression target analysis (BETA) in an R/Bioconductor package. This algorithm ranks the targets based on the distances of their assigned peaks from the factor ChIP experiment and the signed statistics from gene expression profiling with factor perturbation. We further extend BETA to integrate two sets of data from two factors to predict their targets and their combined functions. In this article, we briefly describe the workings of the algorithm and provide a workflow with a real dataset for using it. The gene targets and the aggregate functions of transcription factors YY1 and YY2 in HeLa cells were identified. Using the same datasets, we identified the shared targets of the two factors, which were found to be, on average, more cooperatively regulated.

scholarly journals Differential proportionality - a normalization-free approach to differential gene expression

2017 ◽  
Author(s):  
Ionas Erb ◽  
Thomas Quinn ◽  
David Lovell ◽  
Cedric Notredame
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Computationally Efficient ◽  
Data Set ◽  
Sequencing Technologies ◽  
Expression Of Genes ◽  
The One ◽  
Log Ratio

AbstractGene expression data, such as those generated by next generation sequencing technologies (RNA-seq), are of an inherently relative nature: the total number of sequenced reads has no biological meaning. This issue is most often addressed with various normalization techniques which all face the same problem: once information about the total mRNA content of the origin cells is lost, it cannot be recovered by mere technical means. Additional knowledge, in the form of an unchanged reference, is necessary; however, this reference can usually only be estimated. Here we propose a novel method where sample normalization is unnecessary, but important insights can be obtained nevertheless. Instead of trying to recover absolute abundances, our method is entirely based on ratios, so normalization factors cancel by default. Although the differential expression of individual genes cannot be recovered this way, the ratios themselves can be differentially expressed (even when their constituents are not). Yet, most current analyses are blind to these cases, while our approach reveals them directly. Specifically, we show how the differential expression of gene ratios can be formalized by decomposing log-ratio variance (LRV) and deriving intuitive statistics from it. Although small LRVs have been used to detect proportional genes in gene expression data before, we focus here on the change in proportionality factors between groups of samples (e.g. tissue-specific proportionality). For this, we propose a statistic that is equivalent to the squared t-statistic of one-way ANOVA, but for gene ratios. In doing so, we show how precision weights can be incorporated to account for the peculiarities of count data, and, moreover, how a moderated statistic can be derived in the same way as the one following from a hierarchical model for individual genes. We also discuss approaches to deal with zero counts, deriving an expression of our statistic that is able to incorporate them. In providing a detailed analysis of the connections between the differential expression of genes and the differential proportionality of pairs, we facilitate a clear interpretation of new concepts. The proposed framework is applied to a data set from GTEx consisting of 98 samples from the cerebellum and cortex, with selected examples shown. A computationally efficient implementation of the approach in R has been released as an addendum to the propr package.1

Prediction of Finite Population Proportion When Responses Are Misclassified

2020 ◽  
Author(s):  
Sumanta Adhya ◽  
Surupa Roy ◽  
Tathagata Banerjee
Keyword(s):  
Empirical Study ◽  
Finite Population ◽  
Asymptotic Properties ◽  
Auxiliary Variable ◽  
Binary Response ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Computationally Efficient ◽  
Extensive Simulation ◽  
Population Proportion

Abstract We propose a model-based predictive estimator of the finite population proportion of a misclassified binary response, when information on the auxiliary variable(s) is available for all units in the population. Asymptotic properties of the misclassification-adjusted predictive estimator are also explored. We propose a computationally efficient bootstrap variance estimator that exhibits better performance compared to usual analytical variance estimator. The performance of the proposed estimator is compared with other commonly used design-based estimators through extensive simulation studies. The results are supplemented by an empirical study based on literacy data.

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