scholarly journals SVAPLSseq: A Method to correct for hidden sources of variability in differential gene expression studies based on RNAseq data

2016 ◽  
Author(s):  
Sutirtha Chakraborty
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Latent Variables ◽  
Statistical Tests ◽  
Substantial Reduction ◽  
Real Life ◽  
R Package ◽  
Superior Performance ◽  
Rnaseq Data ◽  
Gene Expression Studies

AbstractRNAseq technology has revolutionized the face of gene expression profiling by generating read count data measuring the transcript abundances for each queried gene. But on the other side, the underlying technical artefacts generate a wide variety of hidden effects that may potentially distort the primary signals of differential expression between two sample groups. This is in addition to the factors of unwanted biological variability may give rise to a highly complicated pattern of residual expression heterogeneity in the data. Standard normalization techniques fail to correct for these latent variables and leads to a substantial reduction in the power of common statistical tests for differential expression. Here I introduce a novel method SVAPLSseq that aims to capture the traces of hidden variability in the data and incorporate them in a regression framework to re-estimate the primary signals for finding the truly positive genes. Application on both simulated and real-life RNAseq data shows the superior performance of the method compared to other available techniques. The method is provided as an R package ‘SVAPLSseq’ that has been submitted to Bioconductor.

Integrative, normalization-insusceptible statistical analysis of RNA-Seq data, with improved differential expression and unbiased downstream functional analysis

2020 ◽  
Author(s):  
Dionysios Fanidis ◽  
Panagiotis Moulos
Keyword(s):  
Gene Expression ◽  
Statistical Analysis ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Superior Performance ◽  
P Value ◽  
Rna Seq ◽  
Daily Lives ◽  
Differential Gene

Abstract The study of differential gene expression patterns through RNA-Seq comprises a routine task in the daily lives of molecular bioscientists, who produce vast amounts of data requiring proper management and analysis. Despite widespread use, there are still no widely accepted golden standards for the normalization and statistical analysis of RNA-Seq data, and critical biases, such as gene lengths and problems in the detection of certain types of molecules, remain largely unaddressed. Stimulated by these unmet needs and the lack of in-depth research into the potential of combinatorial methods to enhance the analysis of differential gene expression, we had previously introduced the PANDORA P-value combination algorithm while presenting evidence for PANDORA’s superior performance in optimizing the tradeoff between precision and sensitivity. In this article, we present the next generation of the algorithm along with a more in-depth investigation of its capabilities to effectively analyze RNA-Seq data. In particular, we show that PANDORA-reported lists of differentially expressed genes are unaffected by biases introduced by different normalization methods, while, at the same time, they comprise a reliable input option for downstream pathway analysis. Additionally, PANDORA outperforms other methods in detecting differential expression patterns in certain transcript types, including long non-coding RNAs.

scholarly journals Topconfects: a package for confident effect sizes in differential expression analysis provides improved usability ranking genes of interest

2018 ◽  
Author(s):  
Paul F. Harrison ◽  
Andrew D. Pattison ◽  
David R. Powell ◽  
Traude H. Beilharz
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Effect Size ◽  
Differential Expression Analysis ◽  
R Package ◽  
Confidence Bound ◽  
Data Set ◽  
Cancer Data ◽  
Wide Range

AbstractBackgroundA differential gene expression analysis may produce a set of significantly differentially expressed genes that is too large to easily investigate, so that a means of ranking genes by their biological interest level is desirable. The life-sciences have grappled with the abuse of p-values to rank genes for this purpose. As an alternative, a lower confidence bound on the magnitude of Log Fold Change (LFC) could be used to rank genes, but it has been unclear how to reconcile this with the need to perform False Discovery Rate (FDR) correction. The TREAT test of McCarthy and Smyth is a step in this direction, finding genes significantly exceeding a specified LFC threshold. Here we describe the use of test inversion on TREAT to present genes ranked by a confidence bound on the LFC, while still controlling FDR.ResultsTesting the Topconfects R package with simulated gene expression data shows the method outperforming current statistical approaches across a wide range of experiment sizes in the identification of genes with largest LFCs. Applying the method to a TCGA breast cancer data-set shows the method ranks some genes with large LFC higher than would traditional ranking by p-value. Importantly these two ranking methods lead to a different biological emphasis, in terms both of specific highly ranked genes and gene-set enrichment.ConclusionsThe choice of ranking method in differential expression analysis can affect the biological interpretation. The common default of ranking by p-value is implicitly by an effect size in which each gene is standardized to its own variability, rather than comparing genes on a common scale, which may not be appropriate. The Topconfects approach of presenting genes ranked by confident LFC effect size is a variation on the TREAT method with improved usability, removing the need to fine-tune a threshold parameter and removing the temptation to abuse p-values as a de-facto effect size.

scholarly journals HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection

2017 ◽  
Author(s):  
John C. Stansfield ◽  
Mikhail G. Dozmorov
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Regulation Of Gene Expression ◽  
3D Structure ◽  
Real Life ◽  
R Package ◽  
Chromatin Interaction ◽  
Interaction Detection ◽  
Chromatin Interactions ◽  
A Genome

AbstractChanges in spatial chromatin interactions are now emerging as a unifying mechanism or-chestrating regulation of gene expression. Evolution of chromatin conformation capture methods into Hi-C sequencing technology now allows an insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence- and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions. We developed a simple and effective method HiCcompare for the joint normalization and differential analysis of multiple Hi-C datasets. The method avoids constraining Hi-C data within a rigid statistical model, allowing a data-driven normalization of biases using locally weighted linear regression (loess). The method identifies region-specific chromatin interaction changes complementary to changes due to large-scale genomic rearrangements, such as copy number variants (CNVs). HiCcompare outperforms methods for normalizing individual Hi-C datasets in detecting a priori known chromatin interaction differences in simulated and real-life settings while detecting biologically relevant changes. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/.Author SummaryAdvances in chromosome conformation capture sequencing technologies (Hi-C) have sparked interest in studying the 3-dimensional (3D) chromatin interaction structure of the human genome. The 3D structure of the genome is now considered as a primary regulator of gene expression. Changes to the 3D chromatin interactions are now emerging as a hallmark of cancer and other complex diseases. With the growing availability of Hi-C data generated under different conditions (e.g. tumor-normal, cell-type-specific), methods are needed to compare them. However, biases in Hi-C data hinder their comparative analysis. To account for biases, several normalization techniques have been developed for removing biases in individual Hi-C datasets, but very few were designed to account for between-datasets biases. We developed a new method and R package HiCcompare for the joint normalization of multiple Hi-C datasets and differential chromatin interaction detection. Our results show the superiority of our joint normalization methods compared to methods for normalizing individual datasets in detecting true chromatin interaction changes. HiCcompare enables further research into discovering the dynamics of 3D genomic changes.

scholarly journals An Application of Sequential Meta-Analysis to Gene Expression Studies

2015 ◽  
Vol 14s5 ◽  
pp. CIN.S27718 ◽  
Author(s):  
Putri W. Novianti ◽  
Ingeborg Van Der Tweel ◽  
Victor L. Jong ◽  
Kit C. B. Roes ◽  
Marinus J. C. Eijkemans
Keyword(s):  
Gene Expression ◽  
Statistical Power ◽  
Type I Error ◽  
Meta Analysis ◽  
Gene List ◽  
Real Life ◽  
Type I ◽  
Optimal Subset ◽  
Expression Studies ◽  
Gene Expression Studies

Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is used to evaluate whether the accumulated samples show enough evidence or more experiments should be initiated. We found 313 differentially expressed genes, based on the cumulative information of the experiments. SMA offers an alternative to existing methods in generating a gene list by evaluating the adequacy of the cumulative information.

scholarly journals DECENT: Differential Expression with Capture Efficiency adjustmeNT for single-cell RNA-seq data

2017 ◽  
Author(s):  
Chengzhong Ye ◽  
Terence P Speed ◽  
Agus Salim
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
R Package ◽  
Statistical Analyses ◽  
Capture Efficiency ◽  
Superior Performance ◽  
Common Phenomenon ◽  
Rna Seq ◽  
Improve Performance

AbstractDropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the dropout process. We develop DECENT, a DE method for scRNA-seq data that explicitly models the dropout process and performs statistical analyses on the inferred pre-dropout counts. We demonstrate using simulated and real datasets the superior performance of DECENT compared to existing methods. DECENT does not require spike-in data, but spike-ins can be used to improve performance when available. The method is implemented in a publicly-available R package.

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