Biology subjects

Whitfield, M. L.

Publications and source records attributed to Whitfield, M. L..

3 recordsLinked to original sources

bioRxiv2018

RNA-seq analyses of molecular abundance (RoMA) for detecting differential gene expression

MotivationVarious methods have been proposed, each with its own limitations. Some naive normal-based tests have low testing power with invalid normal distribution assumptions for RNA-seq read counts, whereas count-based methods lack a biologically meaningful interpretation and have limited capability for integration with other analysis packages for mRNA abundance. In this study, we propose an improved method, RoMA, to accurately detect differential expression and unlock the integration with upstream and downstream analyses on mRNA abundance in RNA-seq studies.\n\nResultsRoMA incorporates information from both mRNA abundance and raw counts. Studies on simulated data and two real datasets showed that RoMA provides an accurate quantification of mRNA abundance and a data adjustment-tolerant DE analysis with high AUC, low FDR, and an efficient control of type I error rate. This study provides a valid strategy for mRNA abundance modeling and data analysis integration for RNA-seq studies, which will greatly facilitate the identification and interpretation of DE genes.\n\nAvailability and implementationRoMA is available at https://github.com/GuoshuaiCai/RoMA.\n\nContactGCAI@mailbox.sc.edu or Michael.L.Whitfield@Dartmouth.edu

bioinformatics↗

bioRxiv2017

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

BackgroundCardiac involvement is a leading cause of death in systemic sclerosis (SSc/scleroderma). The complexity of SSc cardiac manifestations is not fully captured by the current clinical SSc classification, which is based on extent of skin involvement and specific autoantibodies. Therefore, we sought to develop a clinically relevant SSc cardiac disease classification to improve clinical care and increase understanding of SSc cardiac disease pathobiology. We hypothesized that machine learning could identify novel SSc cardiac disease subgroups, and that gene expression assessment of skin could provide insights into molecular pathogenesis of these SSc pheno-groups.\n\nMethodsWe used unsupervised model-based clustering (phenomapping) of SSc patient echocardiographic and clinical data to identify clinically relevant SSc pheno-groups in a discovery cohort (n=316), and validated these findings in an external SSc validation cohort (n=67). Cox regression was used to evaluate survival differences among groups. Gene expression profiles from skin biopsies from a subset of SSc patients (n=68) and controls (n=18) were analyzed with weighted gene co-expression network analyses to identify gene modules that were associated with cardiac pheno-groups and echocardiographic parameters.\n\nResultsFour SSc cardiac pheno-groups were identified with distinct profiles. Pheno-group #1 displayed a predominant cutaneous phenotype without cardiac involvement; pheno-group #2 had long-standing SSc with limited skin and cardiac involvement; pheno-group #3 had diffuse skin involvement, a high frequency of interstitial lung disease (88%), and significant right heart remodeling/dysfunction; and pheno-group #4 had prolonged SSc disease duration, limited skin involvement, and marked biventricular cardiac involvement. After multivariable adjustment, pheno-group #3 (hazard ratio [HR] 7.8, 95% confidence interval [CI] 1.5-33.0) and pheno-group #4 (HR 10.5, 95% CI 2.1-52.7) remained associated with mortality (P<0.05). The addition of pheno-group classification was additive to conventional survival models (P<0.05 by likelihood ratio test for all models), a finding that was replicated in the validation cohort. Skin gene expression analysis identified 2 gene modules (representing fibrosis and skin integrity, respectively) that differed among the cardiac pheno-groups and were associated with specific echocardiographic parameters.\n\nConclusionsMachine learning of echocardiographic and skin gene expression data in SSc identifies clinically relevant subgroups with distinct cardiac phenotypes, survival, and associated molecular pathways in skin.

genomics↗

bioRxiv2016

A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy

Systemic sclerosis (SSc) is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of SSc patients treated with five therapies: mycophenolate mofetil (MMF), rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% OR -5 modified Rodnan Skin Score was applied to each study. We developed a machine learning approach that captured features beyond differential expression that was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab non-improvers were downregulated in MMF improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue-specificity and sensitivity of differential expression results.

genomics↗