Biology subjects

Sara Ballouz

Publications and source records attributed to Sara Ballouz.

4 recordsLinked to original sources

bioRxiv2016

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 agreement on how to benchmark them. In this paper, we evaluate the robustness and uniqueness of enrichment results as a means of assessing methods even where correctness is unknown. We show that heavily annotated (\"multifunctional\") genes are likely to appear in genomics study results and drive the generation of biologically non-specific enrichment results as well as highly fragile significances. By providing a means of determining where enrichment analyses report non-specific and non-robust findings, we are able to assess where we can be confident in their use. We find significant progress in recent bias correction methods for enrichment and provide our own software implementation. Our approach can be readily adapted to any pre-existing package.

Bioinformatics↗

bioRxiv2016

EGAD: Ultra-fast functional analysis of gene networks

SummaryEvaluating 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 high-throughput analysis of gene networks, we have implemented a set of very efficient tools to calculate functional properties in networks based on guilt-by-association methods. EGAD (Extending Guilt-by-Association by Degree) allows gene networks to be evaluated with respect to hundreds or thousands of gene sets. The methods predict novel members of gene groups, assess how well a gene network groups known sets of genes, and determines the degree to which generic predictions drive performance. By allowing fast evaluations, whether of random sets or real functional ones, EGAD provides the user with an assessment of performance which can easily be used in controlled evaluations across many parameters.\n\nAvailability and ImplementationThe software package is freely available at https://github.com/sarbal/EGAD and implemented for use in R and Matlab. The package is also freely available under the LGPL license from the Bioconductor web site (http://bioconductor.org).\n\nContactJGillis@cshl.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online and the full manual at http://gillislab.labsites.cshl.edu/software/egad-extending-guilt-by-association-by-degree/.

Bioinformatics↗

bioRxiv2016

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 data, we show that replicability of transcript abundances will provide misleading estimates of the replicability of conditional variation in transcript abundances (i.e., most expression experiments). Heuristics which implicitly address this problem have emerged in quality control measures to obtain good differential expression results. However, these methods involve strict filters such as discarding low expressing genes or using technical replicates to remove discordant transcripts, and are costly or simply ad hoc. As an alternative, we model gene-level replicability of differential activity using co-expressing genes. We find that sets of housekeeping interactions provide a sensitive means of estimating the replicability of expression changes, where the co-expressing pair can be regarded as pseudo-replicates of one another. We model the effects of noise that perturbs a genes expression within its usual distribution of values and show that perturbing expression by only 5% within that range is readily detectable (AUROC~0.73). We have made our method available as a set of easily implemented R scripts.\n\nAuthor SummaryRNA-sequencing has become a popular means to detect the expression levels of genes. However, quality control is still challenging, requiring both extreme measures and rules which are set in stone from extensive previous analysis. Instead of relying on these rules, we show that co-expression can be used to measure biological replicability with extremely high precision. Co-expression is a well-studied phenomenon, in which two genes that are known to form a functional unit are also expressed at similar levels, and change in similar ways across conditions. Using this concept, we can detect how well an experiment replicates by measuring how well it has retained the co-expression pattern across defined gene-pairs. We do this by measuring how easy it is to detect a sample to which some noise has been added. We show this method is a useful tool for quality control.

Bioinformatics↗

bioRxiv2016

Assessment of functional convergence across study designs in autism

BackgroundDisagreements 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 designs independently targeting each. Meta-analysis within each of these study designs is routine, whether using raw data or summary statistics, but combining results across study designs is atypical. However, tests of functional convergence are used across all study designs, where candidate gene sets are assessed for overlaps with previously known properties. This suggests one possible avenue for combining not study data, but the functional conclusions that they reach.\n\nMethodIn this work, we test for functional convergence in autism spectrum disorder (ASD) across different study types, and specifically whether the degree to which a gene is implicated in autism is correlated with the degree to which it drives functional convergence. Because different study designs are distinguishable by their differences in effect size, this also provides a unified means of incorporating the impact of study design into the analysis of convergence.\n\nResultsWe detected remarkably significant positive trends in aggregate (p < 2.2e-16) with 14 individually significant properties (FDR<0.01), many in areas researchers have targeted based on different reasoning, such as the fragile X mental retardation protein (FMRP) interactor enrichment (FDR 0.003). We are also able to detect novel technical effects and we see that network enrichment from protein-protein interaction data is heavily confounded with study design, arising readily in control data.\n\nConclusionsWe see a convergent functional signal for a subset of known and novel functions in ASD from all sources of genetic variation. Meta-analytic approaches explicitly accounting for different study designs can be adapted to other diseases to discover novel functional associations and increase statistical power.

Genetics↗