Quantile regression for challenging cases of eQTL mapping

Abstract Mapping of expression quantitative trait loci (eQTLs) facilitates interpretation of the regulatory path from genetic variants to their associated disease or traits. High-throughput sequencing of RNA (RNA-seq) has expedited the exploration of these regulatory variants. However, eQTL mapping is usually confronted with the analysis challenges caused by overdispersion and excessive dropouts in RNA-seq. The heavy-tailed distribution of gene expression violates the assumption of Gaussian distributed errors in linear regression for eQTL detection, which results in increased Type I or Type II errors. Applying rank-based inverse normal transformation (INT) can make the expression values more normally distributed. However, INT causes information loss and leads to uninterpretable effect size estimation. After comprehensive examination of the impact from overdispersion and excessive dropouts, we propose to apply a robust model, quantile regression, to map eQTLs for genes with high degree of overdispersion or large number of dropouts. Simulation studies show that quantile regression has the desired robustness to outliers and dropouts, and it significantly improves eQTL mapping. From a real data analysis, the most significant eQTL discoveries differ between quantile regression and the conventional linear model. Such discrepancy becomes more prominent when the dropout effect or the overdispersion effect is large. All the results suggest that quantile regression provides more reliable and accurate eQTL mapping than conventional linear models. It deserves more attention for the large-scale eQTL mapping.

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No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2017-0010 ◽

2017 ◽

Vol 16 (2) ◽

Cited By ~ 1

Author(s):

Aaron T. L. Lun ◽

Gordon K. Smyth

Keyword(s):

Software Package ◽

Error Control ◽

Degrees Of Freedom ◽

Linear Models ◽

Type I Error ◽

Real Data ◽

Type I ◽

Rna Seq ◽

Study Gene Expression ◽

Complex Models

AbstractRNA sequencing (RNA-seq) is widely used to study gene expression changes associated with treatments or biological conditions. Many popular methods for detecting differential expression (DE) from RNA-seq data use generalized linear models (GLMs) fitted to the read counts across independent replicate samples for each gene. This article shows that the standard formula for the residual degrees of freedom (d.f.) in a linear model is overstated when the model contains fitted values that are exactly zero. Such fitted values occur whenever all the counts in a treatment group are zero as well as in more complex models such as those involving paired comparisons. This misspecification results in underestimation of the genewise variances and loss of type I error control. This article proposes a formula for the reduced residual d.f. that restores error control in simulated RNA-seq data and improves detection of DE genes in a real data analysis. The new approach is implemented in the quasi-likelihood framework of the edgeR software package. The results of this article also apply to RNA-seq analyses that apply linear models to log-transformed counts, such as those in the limma software package, and more generally to any count-based GLM where exactly zero fitted values are possible.

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Effective Adaptive Kalman Filter for MEMS-IMU/Magnetometers Integrated Attitude and Heading Reference Systems

Journal of Navigation ◽

10.1017/s0373463312000331 ◽

2012 ◽

Vol 66 (1) ◽

pp. 99-113 ◽

Cited By ~ 113

Author(s):

Wei Li ◽

Jinling Wang

Keyword(s):

Kalman Filter ◽

Linear Models ◽

Dynamic Performance ◽

Real Data ◽

Operating Conditions ◽

Measurement Unit ◽

Accurate Estimation ◽

Reference Systems ◽

Adaptive Kalman Filter ◽

The Impact

To improve the computational efficiency and dynamic performance of low cost Inertial Measurement Unit (IMU)/magnetometer integrated Attitude and Heading Reference Systems (AHRS), this paper has proposed an effective Adaptive Kalman Filter (AKF) with linear models; the filter gain is adaptively tuned according to the dynamic scale sensed by accelerometers. This proposed approach does not need to model the system angular motions, avoids the non-linear problem which is inherent in the existing methods, and considers the impact of the dynamic acceleration on the filter. The experimental results with real data have demonstrated that the proposed algorithm can maintain an accurate estimation of orientation, even under various dynamic operating conditions.

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SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data

BioMed Research International ◽

10.1155/2015/780519 ◽

2015 ◽

Vol 2015 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Yuxiang Tan ◽

Yann Tambouret ◽

Stefano Monti

Keyword(s):

Sample Size ◽

Rna Sequencing ◽

High Throughput Sequencing ◽

Performance Metrics ◽

Simulated Data ◽

Real Data ◽

Rna Seq ◽

Sequencing Data ◽

Detection Algorithms ◽

Fusion Detection

The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

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cILR: Competitive isometric log-ratio for taxonomic enrichment analysis

10.1101/2021.09.07.459294 ◽

2021 ◽

Author(s):

Quang P. Nguyen ◽

Anne G. Hoen ◽

H. Robert Frost

Keyword(s):

High Throughput ◽

Null Hypothesis ◽

High Throughput Sequencing ◽

Enrichment Analysis ◽

Real Data ◽

High Dimensional ◽

Type I ◽

Differential Abundance ◽

Log Ratio ◽

Competitive Null Hypothesis

AbstractResearch in human associated microbiomes often involves the analysis of taxonomic count tables generated via high-throughput sequencing. It is difficult to apply statistical tools as the data is high-dimensional, sparse, and strictly compositional. An approachable way to alleviate high-dimensionality and sparsity is to aggregate variables into pre-defined sets. Set-based analysis is ubiquitous in the genomics literature, and has demonstrable impact in improving interpretability and power of downstream analysis. Unfortunately, there is a lack of sophisticated set-based analysis methods specific to microbiome taxonomic data, where current practice often employs abundance summation as a technique for aggregation. This approach prevents comparison across sets of different sizes, does not preserve inter-sample distances, and amplifies protocol bias. Here, we attempt to fill this gap with a new single sample taxon set enrichment method based on the isometric log ratio transformation and the competitive null hypothesis commonly used in the enrichment analysis literature. Our approach, titled competitive isometric log ratio (cILR), generates sample-specific enrichment scores as the scaled log ratio of the subcomposition defined by taxa within a set and the subcomposition defined by its complement. We provide sample-level significance testing by estimating an empirical null distribution of our test statistic with valid p-values. Herein we demonstrate using both real data applications and simulations that cILR controls for type I error even under high sparsity and high inter-taxa correlation scenarios. Additionally, it provides informative scores that can be inputs to downstream differential abundance and prediction tasks.Author summaryThe study of human associated microbiomes relies on genomic surveys via high-throughput sequencing. However, microbiome taxonomic data is sparse and high dimensional which prevents the application of standard statistical techniques. One approach to address this problem is to perform analyses at the level of taxon sets. Set-based analysis has a long history in the genomics literature, with demonstrable impact in improving both power and interpretability. Unfortunately, there is not a lot of research in developing new set-based tools for microbiome taxonomic data specifically, given that compared to other ‘omics data types microbiome taxonomic data is strictly compositional. We developed a new tool to generate taxon set enrichment scores at the sample level by combining the isometric log-ratio and the competitive null hypothesis. Our scores can be used for statistical inference, and as inputs to other downstream analyses such as differential abundance and prediction models. We demonstrate the performance of our method against competing approaches across both real data analyses and simulation studies.

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Mapping Tumor-Specific Expression QTLs in Impure Tumor Samples

10.1101/136614 ◽

2017 ◽

Cited By ~ 3

Author(s):

Douglas R. Wilson ◽

Wei Sun ◽

Joseph G. Ibrahim

Keyword(s):

Gene Expression ◽

Type I Error ◽

The Cancer Genome Atlas ◽

Type I ◽

Eqtl Mapping ◽

Rna Seq ◽

Specific Expression ◽

Normal Cells ◽

Technology Application ◽

Tumor Tissues

AbstractThe study of gene expression quantitative trait loci (eQTL) is an effective approach to illuminate the functional roles of genetic variants. Computational methods have been developed for eQTL mapping using gene expression data from microarray or RNA-seq technology. Application of these methods for eQTL mapping in tumor tissues is problematic because tumor tissues are composed of both tumor and infiltrating normal cells (e.g. immune cells) and eQTL effects may vary between tumor and infiltrating normal cells. To address this challenge, we have developed a new method for eQTL mapping using RNA-seq data from tumor samples. Our method separately estimates the eQTL effects in tumor and infiltrating normal cells using both total expression and allele-specific expression (ASE). We demonstrate that our method controls type I error rate and has higher power than some alternative approaches. We applied our method to study RNA-seq data from The Cancer Genome Atlas and illustrated the similarities and differences of eQTL effects in tumor and normal cells.

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Impact of human gene annotations on RNA-seq differential expression analysis

10.21203/rs.3.rs-301856/v1 ◽

2021 ◽

Author(s):

Yu Hamaguchi ◽

Chao Zeng ◽

Michiaki Hamada

Keyword(s):

Differential Expression ◽

High Throughput ◽

High Throughput Sequencing ◽

Human Gene ◽

Gene Annotation ◽

Differential Expression Analysis ◽

Rna Seq ◽

Gene Annotations ◽

Sequencing Technologies ◽

The Impact

Abstract Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

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Glutton: large-scale integration of non-model organism transcriptome data for comparative analysis

10.1101/077511 ◽

2016 ◽

Cited By ~ 2

Author(s):

Alan Medlar ◽

Laura Laakso ◽

Andreia Miraldo ◽

Ari Löytynoja

Keyword(s):

Comparative Analysis ◽

Large Scale ◽

De Novo ◽

Sequence Data ◽

Model Organism ◽

Model Organisms ◽

Rna Seq ◽

Reference Species ◽

Wide Range ◽

The Impact

AbstractHigh-throughput RNA-seq data has become ubiquitous in the study of non-model organisms, but its use in comparative analysis remains a challenge. Without a reference genome for mapping, sequence data has to be de novo assembled, producing large numbers of short, highly redundant contigs. Preparing these assemblies for comparative analyses requires the removal of redundant isoforms, assignment of orthologs and converting fragmented transcripts into gene alignments. In this article we present Glutton, a novel tool to process transcriptome assemblies for downstream evolutionary analyses. Glutton takes as input a set of fragmented, possibly erroneous transcriptome assemblies. Utilising phylogeny-aware alignment and reference data from a closely related species, it reconstructs one transcript per gene, finds orthologous sequences and produces accurate multiple alignments of coding sequences. We present a comprehensive analysis of Glutton’s performance across a wide range of divergence times between study and reference species. We demonstrate the impact choice of assembler has on both the number of alignments and the correctness of ortholog assignment and show substantial improvements over heuristic methods, without sacrificing correctness. Finally, using inference of Darwinian selection as an example of downstream analysis, we show that Glutton-processed RNA-seq data give results comparable to those obtained from full length gene sequences even with distantly related reference species. Glutton is available from http://wasabiapp.org/software/glutton/ and is licensed under the GPLv3.

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Comparison Between Dichotomous and Polytomous Scoring of Innovative Items in a Large-Scale Computerized Adaptive Test

Educational and Psychological Measurement ◽

10.1177/0013164411422903 ◽

2011 ◽

Vol 72 (3) ◽

pp. 493-509 ◽

Cited By ~ 2

Author(s):

Hong Jiao ◽

Junhui Liu ◽

Kathleen Haynie ◽

Ada Woo ◽

Jerry Gorham

Keyword(s):

Large Scale ◽

Credit Scoring ◽

Real Data ◽

Simulation Studies ◽

Partial Credit ◽

Ability Estimates ◽

Operational Test ◽

Dichotomous Scoring ◽

Scoring Algorithms ◽

The Impact

This study explored the impact of partial credit scoring of one type of innovative items (multiple-response items) in a computerized adaptive version of a large-scale licensure pretest and operational test settings. The impacts of partial credit scoring on the estimation of the ability parameters and classification decisions in operational test settings were explored in one real data analysis and two simulation studies when two different polytomous scoring algorithms, automated polytomous scoring and rater-generated polytomous scoring, were applied. For the real data analyses, the ability estimates from dichotomous and polytomous scoring were highly correlated; the classification consistency between different scoring algorithms was nearly perfect. Information distribution changed slightly in the operational item bank. In the two simulation studies comparing each polytomous scoring with dichotomous scoring, the ability estimates resulting from polytomous scoring had slightly higher measurement precision than those resulting from dichotomous scoring. The practical impact related to classification decision was minor because of the extremely small number of items that could be scored polytomously in this current study.

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The Impact of Normalization Methods on RNA-Seq Data Analysis

BioMed Research International ◽

10.1155/2015/621690 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 44

Author(s):

J. Zyprych-Walczak ◽

A. Szabelska ◽

L. Handschuh ◽

K. Górczak ◽

K. Klamecka ◽

...

Keyword(s):

High Throughput Sequencing ◽

Data Sets ◽

Complex Data ◽

Rna Seq ◽

Medical Problems ◽

Data Set ◽

Normalization Methods ◽

Wide Range ◽

The Impact ◽

Selection Of

High-throughput sequencing technologies, such as the Illumina Hi-seq, are powerful new tools for investigating a wide range of biological and medical problems. Massive and complex data sets produced by the sequencers create a need for development of statistical and computational methods that can tackle the analysis and management of data. The data normalization is one of the most crucial steps of data processing and this process must be carefully considered as it has a profound effect on the results of the analysis. In this work, we focus on a comprehensive comparison of five normalization methods related to sequencing depth, widely used for transcriptome sequencing (RNA-seq) data, and their impact on the results of gene expression analysis. Based on this study, we suggest a universal workflow that can be applied for the selection of the optimal normalization procedure for any particular data set. The described workflow includes calculation of the bias and variance values for the control genes, sensitivity and specificity of the methods, and classification errors as well as generation of the diagnostic plots. Combining the above information facilitates the selection of the most appropriate normalization method for the studied data sets and determines which methods can be used interchangeably.

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Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data

10.1101/073973 ◽

2016 ◽

Cited By ~ 3

Author(s):

Aaron T. L. Lun ◽

John C. Marioni

Keyword(s):

Single Cell ◽

Error Control ◽

Type I Error ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Simulated Data ◽

Real Data ◽

Type I ◽

Rna Seq ◽

Cell Counts

AbstractAn increasing number of studies are using single-cell RNA-sequencing (scRNA-seq) to characterize the gene expression profiles of individual cells. One common analysis applied to scRNA-seq data involves detecting differentially expressed (DE) genes between cells in different biological groups. However, many experiments are designed such that the cells to be compared are processed in separate plates or chips, meaning that the groupings are confounded with systematic plate effects. This confounding aspect is frequently ignored in DE analyses of scRNA-seq data. In this article, we demonstrate that failing to consider plate effects in the statistical model results in loss of type I error control. A solution is proposed whereby counts are summed from all cells in each plate and the count sums for all plates are used in the DE analysis. This restores type I error control in the presence of plate effects without compromising detection power in simulated data. Summation is also robust to varying numbers and library sizes of cells on each plate. Similar results are observed in DE analyses of real data where the use of count sums instead of single-cell counts improves specificity and the ranking of relevant genes. This suggests that summation can assist in maintaining statistical rigour in DE analyses of scRNA-seq data with plate effects.

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