Humans

Predictability of human differential gene expression

Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report significant results between their groups of interest, the degree to which results are specific to the question at hand …

Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor

Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover and characterize cell types; however, the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine its replicability. …

EGAD: ultra-fast functional analysis of gene networks

Summary: Evaluating gene networks with respect to known biology is a common task but often a computationally costly one. Many computational experiments are difficult to apply exhaustively in network analysis due to run-times. To permit …

Strength of functional signature correlates with effect size in autism

BACKGROUND: Disagreements over genetic signatures associated with disease have been particularly prominent in the field of psychiatric genetics, creating a sharp divide between disease burdens attributed to common and rare variation, with study …

Using predictive specificity to determine when gene set analysis is biologically meaningful

Gene set analysis, which translates gene lists into enriched functions, is among the most common bioinformatic methods. Yet few would advocate taking the results at face value. Not only is there no agreement on the algorithms themselves, there is no …

AuPairWise: A Method to Estimate RNA-Seq Replicability through Co-expression

In addition to detecting novel transcripts and higher dynamic range, a principal claim for RNA-sequencing has been greater replicability, typically measured in sample-sample correlations of gene expression levels. Through a re-analysis of ENCODE …

Positive and negative forms of replicability in gene network analysis

MOTIVATION: Gene networks have become a central tool in the analysis of genomic data but are widely regarded as hard to interpret. This has motivated a great deal of comparative evaluation and research into best practices. We explore the possibility …

Guidance for RNA-seq co-expression network construction and analysis: safety in numbers

MOTIVATION: RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression …

Measuring the wisdom of the crowds in network-based gene function inference

MOTIVATION: Network-based gene function inference methods have proliferated in recent years, but measurable progress remains elusive. We wished to better explore performance trends by controlling data and algorithm implementation, with a particular …

Novel therapeutics for coronary artery disease from genome-wide association study data

BACKGROUND: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been …