ABI Innovation: doseR: a novel framework for dosage compensation and global expression analysis

本研究旨在开发并推广一种新的统计方法，用于分析由染色体拷贝数差异引起的基因表达模式，特别是性染色体上的基因表达模式（例如，XX雌性与XY雄性）。通常，染色体拷贝数的差异会导致这些染色体的基因剂量的差异，从而对基因表达产生相应的影响，称为“剂量效应”。然而，在许多生物体中，特殊的“剂量补偿”机制已经进化到减轻这些由染色体拷贝差异引起的剂量效应，例如性染色体上的雄性和雌性之间的剂量效应。人们对了解哪些生物体采用剂量补偿机制及其原因越来越感兴趣。基因表达全基因组测定的最新进展大大扩展了可用于剂量补偿分析的分类群，但这些数据的分析方法各不相同，应用不一致，并且通常不能充分利用数据中提供的信息。从这个项目中开发的方法和软件，称为“doseR”，将为这些问题提供一个连贯和全面的解决方案。因此，“剂量补偿”项目填补了目前存在于剂量补偿基因组研究分析方法中的一个主要空白。此外，除了剂量补偿分析之外，该方法还可以推广到识别在不同生物学条件下产生的基因组之间基因表达的广泛变化。因此，剂量计的开发和部署将弥合生物直觉和生物信息学推断之间的差距，不仅适用于剂量补偿，而且适用于许多尚未预见的研究方向。该项目还为本科生创造了几个培训机会，包括密集的生物信息学培训讲习班和直接参与研究活动。本研究旨在利用全基因组RNA-seq表达数据，开发并推广一种新的线性建模统计方法，用于分析性染色体剂量补偿。鉴于数据的性质和所需的推断集，目前用于这种分析的统计方法远非理想。目前，生物复制被平均为每个基因的单个测量，并且高度规范化。然后，使用绝对表达评估基因表达对性染色体相对于常染色体的特定影响，而使用表达比率评估基因剂量效应，在这两种情况下都使用非参数统计检验。一种统计上更可靠的方法是采用基因表达的线性混合效应建模。这提供了一个统一的统计框架来评估染色体特异性和剂量对基因表达的影响的大小和意义。此外，它直接应用于每个基因的测序读取计数，并将测序深度和转录物长度等缩放因子纳入描述数据的模型，就像在大多数差异表达分析中所做的那样。因此，避免了广泛的规范化，并且很容易将统计重复纳入分析。这些方法将在一个名为“doseR”的新软件包中实现，该软件包用R统计编程语言编写，并作为生物信息学软件工具Bioconductor套件的一部分分发。新的统计模型及其软件实现相对于以前的剂量补偿评估方法的性能将通过广泛的rna测序数据模拟来评估。还将审查和评估与剂量补偿有关的具体经验数据集的应用。虽然软件开发的直接动机是剂量补偿分析，但所提出的方法可以用于任何需要检测多个特定基因子集表达方向变化的分析场景。因此，它为系统生物学研究提供了一个广泛实用的工具。该项目的状态和结果可在https://walterslab.github.io/doseR/上找到。

This research aims to develop and make broadly available a novel statistical methodology for analyzing patterns of gene expression that result from differences in chromosome copy number, in particular as arise on the sex-chromosomes (e.g. XX females versus XY males). Often a difference in chromosome copy number, which causes a difference in gene dose for those chromosomes, results in a corresponding effect on gene expression, termed a "dosage effect". However, in many organisms, special "dosage compensation" mechanisms have evolved to mitigate these dosage effects arising from differences in chromosome copies, such as between males and females on the sex-chromosomes. There is increasing interest in understanding which organisms employ dosage compensation mechanisms, and why. Recent advances in genome-wide assays of gene expression have greatly expanded which taxa can be assayed for dosage compensation, but analytical approaches for such data are varied, inconsistently applied, and typically do not make full use of the information available in the data. The methods and software developed from this project, called "doseR", will provide a cohesive and comprehensive solution to each of these issues. As such, the "doseR" project fills a major gap that currently exists in analytical methods for genomic investigations of dosage compensation. Furthermore, beyond dosage compensation analysis, this methodology can be generalized to identify broad shifts in gene expression between groups of genes that arise under different biological conditions. Thus, development and deployment of doseR will bridge the gap between biological intuition and bioinformatic inference, not only for dosage compensation, but also for many still as yet unforeseen lines of inquiry. This project also creates several training opportunities for undergraduate students, including intensive bioinformatic training workshops and direct participation in research activities.This research aims to develop and make broadly available a novel linear-modeling statistical methodology for analyzing sex-chromosome dosage compensation using genome-wide RNA-seq expression data. The statistical approaches currently employed for such analyses are far from ideal given the nature of the data and the desired set of inferences. Currently, biological replicates are averaged into a single measurement per gene and heavily normalized. Then particular effects of gene expression on the sex-chromosome relative to autosomes are evaluated using absolute expression while gene dosage effects are assessed using expression ratios, in both cases using non-parametric statistical tests. A more statistically robust approach is to employ linear mixed-effects modeling of gene expression. This provides a unified statistical framework to assess magnitude and significance of both chromosome-specific and dosage effects on gene expression. Moreover, it is applied directly to the sequencing read counts for each gene and incorporates scaling factors such as sequencing depth and transcript length into the models describing the data, as is done in most analyses of differential expression. As such, extensive normalization is avoided and statistical replicates are readily incorporated into the analysis. These methods will be implemented in a new software package, named "doseR", written in the R statistical programming language and distributed as part of the Bioconductor suite of bioinformatic software tools. Performance of the new statistical model and its software implementation relative to previous methods of assessing dosage compensation will be evaluated through extensive simulations of RNA-sequencing data. Application to specific empirical data sets relevant to dosage compensation will also be examined and evaluated. While the immediate motivation for software development is dosage compensation analysis, the proposed methodology can be employed in any analytical scenario requiring the detection of directional shifts in expression for multiple, specific subsets of genes. It therefore provides a tool with broad utility in systems biology research. Status and results of this project can be found at https://walterslab.github.io/doseR/.