Biology subjects

Quinn, T.

Publications and source records attributed to Quinn, T..

4 recordsLinked to original sources

bioRxiv2018

Cancer as a tissue anomaly: classifying tumor transcriptomes based only on healthy data

Since the turn of the century, researchers have sought to diagnose cancer based on gene expression signatures measured from the blood or biopsy as biomarkers. This task, known as classification, is typically solved using a suite of algorithms that learn a mathematical rule capable of discriminating one group (e.g., cases) from another (e.g., controls). However, discriminatory methods can only identify cancerous samples that resemble those that the algorithm already saw during training. As such, we argue that discriminatory methods are fundamentally ill-suited for the classification of cancer: because the possibility space of cancer is definitively large, the existence of a one-of-a-kind gene expression signature becomes very likely. Instead, we propose using an established surveillance method that detects anomalous samples based on their deviation from a learned normal steady-state structure. By transferring this method to transcriptomic data, we can create an anomaly detector for tissue transcriptomes, a \"tissue detector\", that is capable of identifying cancer without ever seeing a single cancer example. Using models trained on normal GTEx samples, we show that our \"tissue detector\" can accurately classify TCGA samples as normal or cancerous and that its performance is further improved by including more normal samples in the training set. We conclude this report by emphasizing the conceptual advantages of anomaly detection and by highlighting future directions for this field of study.

bioinformatics↗

bioRxiv2017

Differential expression analysis of log-ratio transformed counts: benchmarking methods for RNA-Seq data

BackgroundCount data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary \"library size\" by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq differential expression methods.\n\nResultsTo evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and one real, RNA-Seq data sets. The latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations.\n\nConclusionsOur results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have high precision (i.e., few false positives) in simulations and perform as good as, or better than, than conventional methods on real data. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can work as a single tool for data from multiple sequencing modalities.

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bioRxiv2017

Differential Proportionality - A Normalization-Free Approach To Differential Gene Expression

Gene expression data, such as those generated by next generation sequencing technologies (RNA-seq), are of an inherently relative nature: the total number of sequenced reads has no biological meaning. This issue is most often addressed with various normalization techniques which all face the same problem: once information about the total mRNA content of the origin cells is lost, it cannot be recovered by mere technical means. Additional knowledge, in the form of an unchanged reference, is necessary; however, this reference can usually only be estimated. Here we propose a novel method where sample normalization is unnecessary, but important insights can be obtained nevertheless. Instead of trying to recover absolute abundances, our method is entirely based on ratios, so normalization factors cancel by default. Although the differential expression of individual genes cannot be recovered this way, the ratios themselves can be differentially expressed (even when their constituents are not). Yet, most current analyses are blind to these cases, while our approach reveals them directly. Specifically, we show how the differential expression of gene ratios can be formalized by decomposing log-ratio variance (LRV) and deriving intuitive statistics from it. Although small LRVs have been used to detect proportional genes in gene expression data before, we focus here on the change in proportionality factors between groups of samples (e.g. tissue-specific proportionality). For this, we propose a statistic that is equivalent to the squared t-statistic of one-way ANOVA, but for gene ratios. In doing so, we show how precision weights can be incorporated to account for the peculiarities of count data, and, moreover, how a moderated statistic can be derived in the same way as the one following from a hierarchical model for individual genes. We also discuss approaches to deal with zero counts, deriving an expression of our statistic that is able to incorporate them. In providing a detailed analysis of the connections between the differential expression of genes and the differential proportionality of pairs, we facilitate a clear interpretation of new concepts. The proposed framework is applied to a data set from GTEx consisting of 98 samples from the cerebellum and cortex, with selected examples shown. A computationally efficient implementation of the approach in R has been released as an addendum to the propr package.1

bioinformatics↗

bioRxiv2017

propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis

In the life sciences, many assays measure only the relative abundances of components for each sample. These data, called compositional data, require special handling in order to avoid misleading conclusions. For example, in the case of correlation, treating relative data like absolute data can lead to the discovery of falsely positive associations. Recently, researchers have proposed proportionality as a valid alternative to correlation for calculating pairwise association in relative data. Although the question of how to best measure proportionality remains open, we present here a computationally efficient R package that implements two proposed measures of proportionality. In an effort to advance the understanding and application of proportionality analysis, we review the mathematics behind proportionality, demonstrate its application to genomic data, and discuss some ongoing challenges in the analysis of relative abundance data.

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