MOTIVATION: Systems biology analyses often use correlations in gene expression profiles to infer co-expression networks that are then used as input for gene regulatory network inference or to identify functional modules of co-expressed or putatively co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene, resulting in improved fidelity of DE analysis. However, such adjustments do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading to the identification of false, non-biological associations, even when the input data are corrected using standard batch correction. RESULTS: In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. We then introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data. AVAILABILITY AND IMPLEMENTATION: COBRA is available under the GLP3 open source license in R and Python in netZoo (https://netzoo.github.io).

MOTIVATION: In order to minimize the effects of genetic confounding on the analysis of high-throughput genetic association studies, e.g. (whole-genome) sequencing (WGS) studies, genome-wide association studies (GWAS), etc., we propose a general framework to assess and to test formally for genetic heterogeneity among study subjects. As the approach fully utilizes the recent ancestor information captured by rare variants, it is especially powerful in WGS studies. Even for relatively moderate sample sizes, the proposed testing framework is able to identify study subjects that are genetically too similar, e.g. cryptic relationships, or that are genetically too different, e.g. population substructure. The approach is computationally fast, enabling the application to whole-genome sequencing data, and straightforward to implement. RESULTS: Simulation studies illustrate the overall performance of our approach. In an application to the 1000 Genomes Project, we outline an analysis/cleaning pipeline that utilizes our approach to formally assess whether study subjects are related and whether population substructure is present. In the analysis of the 1000 Genomes Project data, our approach revealed subjects that are most likely related, but had previously passed standard qc-filters. AVAILABILITY AND IMPLEMENTATION: An implementation of our method, Similarity Test for Estimating Genetic Outliers (STEGO), is available in the R package stego from Github at https://github.com/dschlauch/stego . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that begins with a model of transcription factor–target gene interactions and uses message passing to update the network model given available transcriptomic and protein–protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks. Availability and Implementation: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.

Abstract Summary: RNA-Seq is an exciting methodology that leverages the power of high-throughput sequencing to measure RNA transcript counts at an unprecedented accuracy. However, the data generated from this process are extremely large and biologist-friendly tools with which to analyze it are sorely lacking. MultiExperiment Viewer (MeV) is a Java-based desktop application that allows advanced analysis of gene expression data through an intuitive graphical user interface. Here, we report a significant enhancement to MeV that allows analysis of RNA-Seq data with these familiar, powerful tools. We also report the addition to MeV of several RNA-Seq-specific functions, addressing the differences in analysis requirements between this data type and traditional gene expression data. These tools include automatic conversion functions from raw count data to processed RPKM or FPKM values and differential expression detection and functional annotation enrichment detection based on published methods. Availability: MeV version 4.7 is written in Java and is freely available for download under the terms of the open-source Artistic License version 2.0. The website (http://mev.tm4.org/) hosts a full user manual as well as a short quick-start guide suitable for new users. Contact: [email protected]