Biology subjects

Terence P Speed

Publications and source records attributed to Terence P Speed.

3 recordsLinked to original sources

bioRxiv2016

Enrich2: a statistical framework for analyzing deep mutational scanning data

Measuring the functional consequences of protein variants can reveal how a protein works or help unlock the meaning of an individuals genome. Deep mutational scanning is a widely used method for multiplex measurement of the functional consequences of protein variants. A major limitation of this method has been the lack of a common analysis framework. We developed a statistical model for estimating variant scores that can be applied to many experimental designs. Our method generates an error estimate for each score that captures both sampling error and consistency between replicates. We apply our model to one novel and five published datasets comprising 243,732 variants and demonstrate its superiority, particularly for removing noisy variants, detecting variants of small effect, and conducting hypothesis testing. We implemented our model in easy-to-use software, Enrich2, that can empower researchers analyzing deep mutational scanning data.

Bioinformatics↗

bioRxiv2016

The healthy ageing gene expression signature for Alzheimer’s disease diagnosis: a random sampling perspective

1In a recent publication, Sood et al. [2015] presented a set of 150 probe-sets which could be used in a diagnosis of Alzheimer disease (AD) based on gene expression. We reproduce some of their experiments, and show that the performance of their particular set of 150 probe-sets does not stand out compared to that of randomly sampled sets of 150 probe-sets from the same array.

Bioinformatics↗

bioRxiv2015

Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data

Due to their relatively low-cost per sample and broad, gene-centric coverage of CpGs across the human genome, Illuminas 450k arrays are widely used in large scale differential methylation studies. However, by their very nature, large studies are particularly susceptible to the effects of unwanted variation. The effects of unwanted variation have been extensively documented in gene expression array studies and numerous methods have been developed to mitigate these effects. However, there has been much less research focused on the appropriate methodology to use for accounting for unwanted variation in methylation array studies. Here we present a novel 2-stage approach using RUV-inverse in a differential methylation analysis of 450k data and show that it outperforms existing methods.

Bioinformatics↗