RNA-Seq optimization with eQTL gold standards.

BackgroundRNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.ResultsTo address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.ConclusionAs each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one's data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression

Scissor for finding outliers in RNA-seq

The impressive progress of high-throughput technologies has provided many interesting modern data types, which has tremendously increased the demand for Statistics. RNA-seq, in particular, allows a rich characterization of the genome with many exciting applications. This dissertation makes contributions to RNA-seq data analysis by addressing several statistical challenges especially characterized by high dimensionality. The dissertation is composed of two major parts. The first part concerns the issue of high dimensional outliers which are challenging to distinguish from inliers due to the special structure of high dimensional space. We introduce a new notion of high dimensional outliers that embraces various types and provides deep insights into understanding the behavior of these outliers based on several asymptotic regimes. Using this new framework, we develop an outlier detection method called Scissor that aims to identify sample outliers with distinct forms or patterns of transcripts across RNA-seq cohorts. Scissor offers a novel approach to unsupervised screening of a variety of shape changes that are possibly associated with important genetic events. Scissor has been implemented in R and is available online. The second part is motivated by a challenge raised by an application of PCA to RNA-seq data. A fundamental question using PCA is how many principal components are effective for reducing dimensions. Although several algorithms have been developed to address this question, it has been observed that these algorithms may not be appropriate for RNA-seq data due to its abnormal noise structure. We propose a new algorithm for determining an effective number of principal componentsin RNA-seq data assuming a flexible noise structure based on some fundamental results in random matrix theory. The proposed method also provides a visualization tool for assessing the noise assumption. This methodology has been successful in offering more reasonable numbers of principal components for RNA-seq data and implemented in Scissor.Doctor of Philosoph

Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data

Background: Long non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets. Results: Gene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines exhibit inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and benchmark RNA-seq datasets. The substandard performance of DE tools for lncRNAs applies also to low-abundance mRNAs. No single tool uniformly outperformed the others. Variability, number of samples, and fraction of DE genes markedly influenced DE tool performance. Conclusions: Overall, linear modeling with empirical Bayes moderation (limma) and a non-parametric approach (SAMSeq) showed good control of the false discovery rate and reasonable sensitivity. Of note, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in realistic settings such as in clinical cancer research. About half of the methods showed a substantial excess of false discoveries, making these methods unreliable for DE analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, giving guidance on selection of the optimal DE tool (http://statapps.ugent.be/tools/AppDGE/)

Methods For Robust Quantification Of Rna Alternative Splicing In Heterogeneous Rna-Seq Datasets

RNA alternative splicing is primarily responsible for transcriptome diversity and is relevant to human development and disease. However, current approaches to splicing quantication make simplifying assumptions which are violated when RNA sequencing data are heterogeneous. Influences from genetic and environmental background contribute to variability within a group of samples purported to represent the same biological condition. This work describes three methods which account for data heterogeneity when detecting differential RNA splicing between sample groups. First, a robust model is implemented for outlier detection within a group of purported replicates. Next, large RNA-seq datasets with high within-group variability are addressed with a statistical approach which retains power to detect changing splice junctions without sacricing specicity. Finally, applying these tools to call sQTLs in GTEx tissues has identified splicing variations associated with risk loci for cardiovascular disease and anomalous skeletal development. Each of these methods correctly handles the properties of heterogeneous RNA-seq data to improve precision and reduce false discovery rate

Transcriptomics in Toxicogenomics, Part II : Preprocessing and Differential Expression Analysis for High Quality Data

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.Peer reviewe

Machine learning and computational methods to identify molecular and clinical markers for complex diseases – case studies in cancer and obesity

In biomedical research, applied machine learning and bioinformatics are the essential disciplines heavily involved in translating data-driven findings into medical practice. This task is especially accomplished by developing computational tools and algorithms assisting in detection and clarification of underlying causes of the diseases. The continuous advancements in high-throughput technologies coupled with the recently promoted data sharing policies have contributed to presence of a massive wealth of data with remarkable potential to improve human health care. In concordance with this massive boost in data production, innovative data analysis tools and methods are required to meet the growing demand. The data analyzed by bioinformaticians and computational biology experts can be broadly divided into molecular and conventional clinical data categories. The aim of this thesis was to develop novel statistical and machine learning tools and to incorporate the existing state-of-the-art methods to analyze bio-clinical data with medical applications. The findings of the studies demonstrate the impact of computational approaches in clinical decision making by improving patients risk stratification and prediction of disease outcomes. This thesis is comprised of five studies explaining method development for 1) genomic data, 2) conventional clinical data and 3) integration of genomic and clinical data. With genomic data, the main focus is detection of differentially expressed genes as the most common task in transcriptome profiling projects. In addition to reviewing available differential expression tools, a data-adaptive statistical method called Reproducibility Optimized Test Statistic (ROTS) is proposed for detecting differential expression in RNA-sequencing studies. In order to prove the efficacy of ROTS in real biomedical applications, the method is used to identify prognostic markers in clear cell renal cell carcinoma (ccRCC). In addition to previously known markers, novel genes with potential prognostic and therapeutic role in ccRCC are detected. For conventional clinical data, ensemble based predictive models are developed to provide clinical decision support in treatment of patients with metastatic castration resistant prostate cancer (mCRPC). The proposed predictive models cover treatment and survival stratification tasks for both trial-based and realworld patient cohorts. Finally, genomic and conventional clinical data are integrated to demonstrate the importance of inclusion of genomic data in predictive ability of clinical models. Again, utilizing ensemble-based learners, a novel model is proposed to predict adulthood obesity using both genetic and social-environmental factors. Overall, the ultimate objective of this work is to demonstrate the importance of clinical bioinformatics and machine learning for bio-clinical marker discovery in complex disease with high heterogeneity. In case of cancer, the interpretability of clinical models strongly depends on predictive markers with high reproducibility supported by validation data. The discovery of these markers would increase chance of early detection and improve prognosis assessment and treatment choice

Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. RESULTS: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. AVAILABILITY AND IMPLEMENTATION: The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online