Biology subjects

Hersh, C. P.

Publications and source records attributed to Hersh, C. P..

3 recordsLinked to original sources

bioRxiv2018

Novel Data Transformations for RNA-seq Data Analysis

We propose eight data transformations for RNA-seq data analysis aiming to make the transformed sample mean to be representative of the distribution center since it is not always possible to transform count data to satisfy the normality assumption. Simulation studies showed that limma based on transformed data by using the rv transformation (denoted as limma+rv) performed best compared with limma based on transformed data by using other transformation methods in term of high accuracy and low FNR, while keeping FDR at the nominal level. For large sample size, limma based on transformed data by using the 8 proposed transformation methods had similar performance to limma based on transformed data by using existing transformation methods for equal library size scenarios. Otherwise, limma based on transformed data by using the rv, lv, rv2, or lv2 transformation, or by using the existing voom transformation performed better than limma based on data from other transformation methods. Real data analysis results showed that limma+ l2 performed best, while limma+ rv also had good performance.

bioinformatics↗

bioRxiv2017

Integrative genomics analysis identifies ACVR1B as a candidate causal gene of emphysema distribution in non-alpha 1-antitrypsin deficient smokers

BackgroundSeveral genetic risk loci associated with emphysema apico-basal distribution (EABD) have been identified through genome-wide association studies (GWAS), but the biological functions of these variants are unknown. To characterize gene regulatory functions of EABD-associated variants, we integrated EABD GWAS results with 1) a multi-tissue panel of expression quantitative trait loci (eQTL) from subjects with COPD and the GTEx project and 2) epigenomic marks from 127 cell types in the Roadmap Epigenomics project. Functional validation was performed for a variant near ACVR1B.\n\nResultsSNPs from 168 loci with P-values<5x10-5 in the largest GWAS meta-analysis of EABD (Boueiz A. et al, AJRCCM 2017) were analyzed. 54 loci overlapped eQTL regions from our multi-tissue panel, and 7 of these loci showed a high probability of harboring a single, shared GWAS and eQTL causal variant (colocalization posterior probability[≥]0.9). 17 cell types exhibited greater than expected overlap between EABD loci and DNase-I hypersensitive peaks, DNaseI hotspots, enhancer marks, or digital DNaseI footprints (permutation P-value < 0.05), with the strongest enrichment observed in CD4+, CD8+, and regulatory T cells. A region near ACVR1B demonstrated significant colocalization with a lung eQTL and overlapped DNase-I hypersensitive regions in multiple cell types, and reporter assays in human bronchial epithelial cells confirmed allele-specific regulatory activity for the lead variant, rs7962469.\n\nConclusionsIntegrative analysis highlights candidate causal genes, regulatory variants, and cell types that may contribute to the pathogenesis of emphysema distribution. These findings will enable more accurate functional validation studies and better understanding of emphysema distribution biology.

genomics↗

bioRxiv2016

Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Our results demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

systems biology↗