Biology subjects

Crowley, T. M.

Publications and source records attributed to Crowley, T. M..

3 recordsLinked to original sources

bioRxiv2018

A field guide for the compositional analysis of any-omics data

Next-generation sequencing (NGS) has made it possible to determine the sequence and relative abundance of all nucleotides in a biological or environmental sample. Today, NGS is routinely used to understand many important topics in biology from human disease to microorganism diversity. A cornerstone of NGS is the quantification of RNA or DNA presence as counts. However, these counts are not counts per se: the magnitude of the counts are determined arbitrarily by the sequencing depth, not by the input material. Consequently, counts must undergo normalization prior to use. Conventional normalization methods require a set of assumptions: they assume that the majority of features are unchanged, and that all environments under study have the same carrying capacity for nucleotide synthesis. These assumptions are often untestable and may not hold when comparing heterogeneous samples (e.g., samples collected across distinct cancers or tissues). Instead, methods developed within the field of compositional data analysis offer a general solution that is assumption-free and valid for all data. In this manuscript, we synthesize the extant literature to provide a concise guide on how to apply compositional data analysis to NGS count data. In doing so, we review zero replacement, differential abundance analysis, and within-group and between-group coordination analysis. We then discuss how this pipeline can accommodate complex study design, facilitate the analysis of vertically and horizontally integrated data, including multiomics data, and further extend to single-cell sequencing data. In highlighting the limitations of total library size, effective library size, and spike-in normalizations, we propose the log-ratio transformation as a general solution to answer the question, \"Relative to some important activity of the cell, what is changing?\". Taken together, this manuscript establishes the first fully comprehensive analysis protocol that is suitable for any and all -omics data.

bioinformatics↗

bioRxiv2018

Solving for X: evidence for sex-specific autism biomarkers across multiple transcriptomic studies

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers (and characterise their function), we present an analysis of a single paired-end post-mortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both post-mortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

neuroscience↗

bioRxiv2017

Understanding sequencing data as compositions: anoutlook and review

MotivationAlthough seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g., gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e., library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that renders invalid many conventional analyses, including distance measures, correlation coefficients, and multivariate statistical models.\n\nResultsThe purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study.

bioinformatics↗