Biology subjects

Parsana, P.

Publications and source records attributed to Parsana, P..

3 recordsLinked to original sources

bioRxiv2024

Aggregation of recount3 RNA-seq data improves the inference of consensus and tissue-specific gene co-expression networks.

BackgroundGene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks. ResultsWe compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples. ConclusionThis study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

genomics↗

bioRxiv2020

The architecture of brain co-expression reveals the brain-wide basis of disease susceptibility

Gene networks have proven their utility for elucidating transcriptome structure in the brain, yielding numerous biological insights. Most analyses have focused on expression relationships within a circumspect number of regions - how these relationships vary across a broad array of brain regions is largely unknown. By leveraging RNA-sequencing in 864 samples representing 12 brain regions in a cohort of 131 phenotypically normal individuals, we identify 12 brain-wide, 114 region-specific, and 50 cross-regional co-expression modules. We replicate the majority (81%) of modules in regional microarray datasets. Nearly 40% of expressed genes fall into brain-wide modules corresponding to major cell classes and conserved biological processes. Region-specific modules comprise 25% of expressed genes and correspond to region-specific cell types and processes, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. We further leverage these modules to capture cell-type-specific lncRNA and gene isoforms, both of which contribute substantially to regional synaptic diversity. We identify enrichment of neuropsychiatric disease risk variants in brain wide and multi-regional modules, consistent with their broad impact on cell classes, and highlight specific roles in neuronal proliferation and activity-dependent processes. Finally, we examine the manner in which gene co-expression and gene regulatory networks reflect genetic risk, including the recently framed omnigenic model of disease architecture.

genetics↗

bioRxiv2017

Addressing confounding artifacts in reconstruction of gene co-expression networks

BackgroundGene co-expression networks capture diverse biological relationships between genes, and are important tools in predicting gene function and understanding disease mechanisms. Functional interactions between genes have not been fully characterized for most organisms, and therefore reconstruction of gene co-expression networks has been of common interest in a variety of settings. However, methods routinely used for reconstruction of gene co-expression networks do not account for confounding artifacts known to affect high dimensional gene expression measurements.\n\nResultsIn this study, we show that artifacts such as batch effects in gene expression data confound commonly used network reconstruction algorithms. Both theoretically and empirically, we demonstrate that removing the effects of top principal components from gene expression measurements prior to network inference can reduce false discoveries, especially when well annotated technical covariates are not available. Using expression data from the GTEx project in multiple tissues and hundreds of individuals, we show that this latent factor residualization approach often reduces false discoveries in the reconstructed networks.\n\nConclusionNetwork reconstruction is susceptible to confounders that affect measurements of gene expression. Even controlling for major individual known technical covariates fails to fully eliminate confounding variation from the data. In studies where a wide range of annotated technical factors are measured and available, correcting gene expression data with multiple covariates can also improve network reconstruction, but such extensive annotations are not always available. Our study shows that principal component correction, which does not depend on study design or annotation of all relevant confounders, removes patterns of artifactual variation and improves network reconstruction in both simulated data, and gene expression data from GTEx project. We have implemented our PC correction approach in the Bioconductor package sva which can be used prior to network reconstruction with a range of methods.

genomics↗