Biology subjects

Ibrahim, J. G.

Publications and source records attributed to Ibrahim, J. G..

9 recordsLinked to original sources

bioRxiv2024

Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing

Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.

genomics↗

bioRxiv2020

MRLocus: identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci displaying allelic heterogeneity, that is, containing multiple LD-independent eQTLs. MRLocus makes use of a colocalization step applied to each eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five causal candidate genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.

bioinformatics↗

bioRxiv2020

Model-based Feature Selection and Clustering of RNA-seq Data for Unsupervised Subtype Discovery

Clustering is a form of unsupervised learning that aims to un-cover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and utilized the quadratic penalty method with a SCAD penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

genomics↗

bioRxiv2019

Efficient Detection and Classification of Epigenomic Changes Under Multiple Conditions

SO_SCPLOWUMMARYC_SCPLOWEpigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively-parallel sequencing (ChIP-seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP-seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, mixNBHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.

bioinformatics↗

bioRxiv2019

Nonparametric expression analysis using inferential replicate counts

A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases present in the observations. Ideally, a statistical testing procedure should incorporate information about the inherent uncertainty of the abundance estimates, whether at the gene or transcript level, that arise from quantification of abundance. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts or scaled counts for each gene or transcript, and a subset of methods can incorporate information about the uncertainty of the counts. Previous work has shown that nonparametric models for RNA-seq differential expression may in some cases have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account the inferential uncertainty of the observations, leading to an inflated false discovery rate, in particular at the transcript level. Here we propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty, batch effects, and sample pairing. We compare our method, "SAMseq With Inferential Samples Helps", or Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a singlecell RNA-seq dataset, assessing sensitivity to recover DE genes between sub-populations of cells, and compare its performance to the Wilcoxon rank sum test.

bioinformatics↗

bioRxiv2018

ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing For Aberrant Gene Expression Patterns

Immunotherapies have achieved phenomenal success in the treatment of cancer and promise even more breakthroughs in the near future. The need to understand the underlying mechanisms of immunotherapies and to develop precision immunotherapy regimens has spurred great interest in characterizing immune cell composition within the tumor microenvironment. Several methods have been developed to estimate immune cell composition using gene expression data from bulk tumor samples. However, these methods are not flexible enough to handle aberrant patterns of gene expression data, e.g., inconsistent cell type-specific gene expression between purified reference samples and this cell type in tumor samples. In this paper, we present a novel statistical model for expression deconvolution called ICeD-T (Immune Cell Deconvolution in Tumor tissues), which models gene expression by a log-normal distribution that is appropriate for both microarray and RNA-seq data. ICeD-T automatically identifies aberrant genes whose expressions are inconsistent with the deconvolution model and down-weights their contributions to cell type abundance estimates. We evaluated the performance of ICeD-T versus existing methods in simulation studies and several real data analyses. ICeD-T displayed comparable or superior performance to these competing methods. Applying these methods to assess the relationship between immunotherapy response and immune cell composition, ICeD-T is able to identify significant associations that are missed by its competitors.

genomics↗

bioRxiv2018

Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences

In RNA-seq differential expression analysis, investigators aim to detect genes with changes in expression across conditions, despite technical and biological variability. A common task is to accurately estimate the effect size. When the counts are low or highly variable, the simple effect size estimate has high variance, leading to poor ranking of genes by effect size. Here we propose apeglm, which uses a heavy-tailed Cauchy prior distribution for effect sizes, resulting in lower bias than previous shrinkage estimators, while still reducing variance. apeglm is available at http://bioconductor.org/packages/apeglm, and can be used from within the DESeq2 software.

bioinformatics↗

bioRxiv2017

Heritability of regional brain volumes in large-scale neuroimaging and genetic studies

Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (p ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population. We observed omni-genic impact across the genome as well as enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks and brain regions. The variation in heritability across regions was not related to measurement reliability. Heritability estimates exhibit a symmetric pattern across left and right hemispheres and are consistent in females and males. Our main findings in UK Biobank are consistent with those in Alzheimers Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, with more stable estimates in UK Biobank.

genetics↗

bioRxiv2017

Mapping Tumor-Specific Expression QTLs In Impure Tumor Samples

The study of gene expression quantitative trait loci (eQTL) is an effective approach to illuminate the functional roles of genetic variants. Computational methods have been developed for eQTL mapping using gene expression data from microarray or RNA-seq technology. Application of these methods for eQTL mapping in tumor tissues is problematic because tumor tissues are composed of both tumor and infiltrating normal cells (e.g. immune cells) and eQTL effects may vary between tumor and infiltrating normal cells. To address this challenge, we have developed a new method for eQTL mapping using RNA-seq data from tumor samples. Our method separately estimates the eQTL effects in tumor and infiltrating normal cells using both total expression and allele-specific expression (ASE). We demonstrate that our method controls type I error rate and has higher power than some alternative approaches. We applied our method to study RNA-seq data from The Cancer Genome Atlas and illustrated the similarities and differences of eQTL effects in tumor and normal cells.

genomics↗