Biology subjects

Jeffrey T Leek

Publications and source records attributed to Jeffrey T Leek.

9 recordsLinked to original sources

bioRxiv2016

A framework for RNA quality correction in differential expression analysis

RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on high-quality RNA. We demonstrate here that statistical adjustment employing existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from a molecular degradation experiment of human brain tissue, we introduce the quality surrogate variable (qSVA) analysis framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show this approach results in greatly improved replication rates (>3x) across two large independent postmortem human brain studies of schizophrenia. Finally, we explored public datasets to demonstrate potential RNA quality confounding when comparing expression levels of different brain regions and diagnostic groups beyond schizophrenia. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from the human brain.

Bioinformatics↗

bioRxiv2016

Is most published research really false?

There has been an increasing concern that most published medical findings are false. But what does it mean to be false? Here we describe the range of definitions of false discoveries in the scientific literature. We summarize the philosophical, statistical, and experimental evidence for each type of false discovery. We discuss common underpinning problems with the scientific and data analytic practices and point to tools and behaviors that can be implemented to reduce the problems with published scientific results.

Scientific Communication and Education↗

bioRxiv2016

Human splicing diversity across the Sequence Read Archive

We aligned 21,504 publicly available Illumina-sequenced human RNA-seq samples from the Sequence Read Archive (SRA) to the human genome and compared detected exon-exon junctions with junctions in several recent gene annotations. 56,865 junctions (18.6%) found in at least 1,000 samples were not annotated, and their expression associated with tissue type. Newer samples contributed few novel well-supported junctions, with 96.1% of junctions detected in at least 20 reads across samples present in samples before 2013. Junction data is compiled into a resource called intropolis available at http://intropolis.rail.bio. We discuss an application of this resource to cancer involving a recently validated isoform of the ALK gene.

Genomics↗

bioRxiv2015

Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce

Motivation: Public archives contain thousands of trillions of bases of valuable sequencing data. More than 40% of the Sequence Read Archive is human data protected by provisions such as dbGaP To analyze dbGaP-protected data, researchers must typically work with IT administrators and signing officials to ensure all levels of security are implemented at their institution. This is a major obstacle, impeding reproducibility and reducing the utility of archived data.\n\nResults: We present a protocol and software tool for analyzing protected data in a commercial cloud. The protocol, Rail-dbGaP, is applicable to any tool running on Amazon Web Services Elastic MapReduce. The tool, Rail-RNA v0.2, is a spliced aligner for RNA- seq data, which we demonstrate by running on 9,662 samples from the dbGaP-protected GTEx consortium dataset. The Rail-dbGaP protocol makes explicit for the first time the steps an investigator must take to develop Elastic MapReduce pipelines that analyze dbGaP-protected data in a manner compliant with NIH guidelines. Rail-RNA automates implementation of the protocol, making it easy for typical biomedical investigators to study protected RNA-seq data, regardless of their local IT resources or expertise.\n\nAvailability: Rail-RNA is available from http://rail.bio. Technical details on the Rail-dbGaP protocol as well as an implementation walkthrough are available at https://github.com/nellore/rail-dbgap. Detailed instructions on running Rail-RNA on dbGaP-protected data using Amazon Web Services are available at http://docs.rail.bio/dbgap/.\n\nContact: anellore@gmail.com, langmea@cs.jhu.edu

Bioinformatics↗

bioRxiv2015

Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it is difficult to reproduce the exact analysis without access to original computing resources. We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16 hours for US$0.91 per sample. Rail-RNA produces alignments and base-resolution bigWig coverage files, ready for use with downstream packages for reproducible statistical analysis. We identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounders. Rail-RNA is open-source software available at http://rail.bio.

Bioinformatics↗

bioRxiv2015

Measuring the Contribution of Genomic Predictors to Improving Estimator Precision in Randomized trials

The use of genomic data in the clinic has not been as widespread as was envisioned when sequencing and genomic analysis became common techniques. An underlying difficulty is the direct assessment of how much additional information genomic data are providing beyond standard clinical measurements. This is hard to quantify in the clinical setting where laboratory tests based on genomic signatures are fairly new and there are not sufficient data collected to determine how valuable these tests have been in practice. Here we focus on the potential precision gain from using the popular MammaPrint genomic signature in a covariate-adjusted, randomized clinical trial. We describe how adjustment of an estimator for the average treatment effect using baseline measurements can improve precision. This precision gain can be translated directly into sample size reduction and corresponding cost savings. We conduct a simulation study using genomic and clinical data gathered for breast cancer patients and find that adjusting for clinical factors alone provides a gain in precision of 5-6%, adjusting for genomic factors alone provides a similar gain (5%), and combining the two yields a 2-3% additional gain over only adjusting for clinical covariates.

Bioinformatics↗

bioRxiv2015

regionReport: Interactive reports for region-based analyses

regionReport is a R package for generating detailed interactive reports from regions of the genome. The report includes quality-control checks, an overview of the results, an interactive table of the genomic regions, and reproducibility information. regionReport can easily be expanded with report templates for other specialized analyses. In particular, regionReport has an extensive report template for exploring derfinder results from annotation-agnostic RNA-seq differential expression analyses.\n\nAvailabilityregionReport is freely available via Bioconductor at bioconductor.org/packages/release/bioc/html/regionReport.html.

Bioinformatics↗

bioRxiv2014

Avoiding test set bias with rank-based prediction

Background: Prior to applying genomic predictors to clinical samples, the genomic data must be properly normalized. The most effective normalization methods depend on the data from multiple patients. From a biomedical perspective this implies that predictions for a single patient may change depending on which other patient samples they are normalized with. This test set bias will occur when any cross-sample normalization is used before clinical prediction. Methods: We developed a new prediction modeling framework based on the relative ranks of features within a sample in order to prevent the need for cross-sample normalization, therefore effectively avoiding test set bias. We employed modeling with previously published Top-Scoring Pairs (TSPs) methodology to build the rank-based predictors. We further investigated the robustness of the rank-based models in case of heterogeneous datasets using diverse microarray technologies. Results: We demonstrated that results from existing genetic signatures which rely on normalizing test data may be unreproducible when the patient population changes compo- sition or size. Using pairwise comparisons of features, we produced a ten gene, platform- robust, and interpretable alternative to the PAM50 subtyping signature and evaluated the robustness of our signature across 6,297 patients samples from 28 curated breast cancer microarray datasets spanning 15 different platforms. Conclusion: We propose a new approach to developing genomic signatures that avoids test set bias through the robustness of rank-based features. Our small, interpretable alter- native to PAM50 produces comparable predictions and patient survival differentiation to the original signature. Additionally, we are able to ensure that the same patient will be classified the same way in every context.

Genomics↗

bioRxiv2014

Flexible analysis of transcriptome assemblies with Ballgown

Introduction Introduction Negative control experiment Positive control experiment Confirmation of statistical... Analysis of RNA-seq experiments... Analysis of quantitative... Expression quantitative trait... Computational Efficiency Summary References A key advantage of RNA sequencing (RNA-seq) over hybridization-based technologies such as microarrays is that RNA-seq makes it possible to reconstruct complete gene structures, including multiple splice variants, from raw RNA-seq reads without relying on previously-established annotations [20, 32, 9]. But with this added flexibility, there are increased computational demands on upstream processing tasks such as alignment and ass ...

Bioinformatics↗