Biology subjects

Roeder, K.

Publications and source records attributed to Roeder, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Model-free Prediction Test with Application to Genomics Data

Testing the significance of prediction in a regression model is one of the most important topics in statistics. This problem is especially difficult without any parametric assumptions on the data. This paper aims to test the null hypothesis that, given confounding variables Z, X does not significantly contribute to the prediction of Y under the model-free setting, where X and Z are possibly high dimensional. We propose a general framework that first fits nonparametric regression models on the Y|X and Y|(X, Z), then compares the prediction power of the two models. The proposed method allows us to leverage the strength of the most powerful regression algorithms developed from the modern machine learning community. The p-value for the test can be easily obtained by permutation. In simulations, we find that the proposed method is more powerful compared to existing methods. The proposed method allows us to draw biologically meaningful conclusions from two gene expression data analyses without strong distributional assumptions: (a) testing prediction power of sequencing RNA for the proteins in CITE-seq data, and (b) identification of spatially variable genes in spatially resolved transcriptomics data.

genomics↗

bioRxiv2021

Investigating the structural changes due to adenosine methylation of the Kaposi's sarcoma-associated herpes virus ORF50 transcript

Kaposis sarcoma-associated herpes virus (KSHV) is a human oncovirus. KSHV relies on manipulating the host cell N6-methyl adenosine (m6A) RNA modification pathway. Methylation within a RNA stem loop of the open reading frame 50 (ORF50) enhances transcript stability via the recruitment of the m6A reader, SND1. In this contribution we explore the energy landscapes of the unmethylated and methylated RNA stem loops of ORF50 to investigate the effect of methylation on the structure of the stem loop. We observe a significant shift upon methylation between an open and closed configuration of the top of the stem loop. In the unmethylated stem loop the closed configuration is much lower in energy, and, as a result, exhibits higher occupancy.

biophysics↗

bioRxiv2021

Expansion of RNA sequence diversity and RNA editing rates throughout human cortical development

Post-transcriptional modifications by RNA editing are essential for neurodevelopment, yet their developmental and regulatory features remain poorly resolved. We constructed a full temporal view of base-specific RNA editing in the developing human cortex, from early progenitors through fully mature cells found in the adult brain. Developmental regulation of RNA editing is characterized by an increase in editing rates for more than 10,000 selective editing sites, shifting between mid-fetal development and infancy, and a massive expansion of RNA hyper-editing sites that amass in the cortex through postnatal development into advanced age. These sites occur disproportionally in 3UTRs of essential neurodevelopmental genes. These profiles are preserved in non-human primate and murine models, illustrating evolutionary conserved regulation of RNA editing in mammalian cortical development. RNA editing levels are commonly genetically regulated (editing quantitative trait loci, edQTLs) consistently across development or predominantly during prenatal or postnatal periods. Both consistent and temporal-predominant edQTLs co-localize with risk loci associated with neurological traits and disorders, including attention deficit hyperactivity disorder, schizophrenia, and sleep disorders. These findings expand the repertoire of highly regulated RNA editing sites in the brain and provide insights of how epitranscriptional sequence diversity by RNA editing contributes to neurodevelopment.

developmental biology↗

bioRxiv2021

An approach to gene-based testing accounting for dependence of tests among nearby genes

In genome-wide association studies (GWAS), it has become commonplace to test millions of SNPs for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a genes test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive p-value thresholding (AdaPT), guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

genetics↗

bioRxiv2021

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

The majority of variants associated with complex traits and common diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown regulatory effects in cis and trans. By leveraging biobank-scale GWAS data, massively parallel CRISPR screens and single cell transcriptome sequencing, we discovered target genes of noncoding variants for blood trait loci. The closest gene was often the target gene, but this was not always the case. We also identified trans-effects networks of noncoding variants when cis target genes encoded transcription factors, such as GFI1B and NFE2. We observed that GFI1B trans-target genes were enriched for GFI1B binding sites and fine-mapped GWAS variants, and expressed in human bone marrow progenitor cells, suggesting that GFI1B acts as a master regulator of blood traits. This platform will enable massively parallel assays to catalog the target genes of human noncoding variants in both cis and trans.

genomics↗

bioRxiv2021

Constructing Local Cell Sepcific Networks from Single Cell Data

Single-cell RNA sequencing facilitates investigations of gene co-expression networks at the cellular level potentially yielding unique biological insights, but the estimation problem is challenging. We develop a non-parametric approach to estimate cell-specific networks for each cell and cell type and propose novel downstream analyses. The individual networks preserve the cellular heterogeneity and facilitate testing for differences between cell groups. To further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the gene networks of cells sampled from case and control subjects to reveal patterns in gene co-expression.

genomics↗

bioRxiv2021

Cell Type Hierarchy Reconstruction via Reconciliation of Multi-resolution Cluster Tree

A wealth of clustering algorithms are available for Single-cell RNA sequencing (scRNA-seq), but it remains challenging to compare and characterize the features across different scales of resolution. To resolve this challenge Multi-resolution Reconciled Tree (MRtree), builds a hierarchical tree structure based on multi-resolution partitions that is highly flexible and can be coupled with most scRNA-seq clustering algorithms. MRtree out-performs bottom-up or divisive hierarchical clustering approaches because it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. Application to fetal brain cells yields insight into subtypes of cells that can be reliably estimated.

cell biology↗

bioRxiv2020

A data harmonization pipeline to leverage external controls and boost power in GWAS

AO_SCPLOWBSTRACTC_SCPLOWThe use of external controls in genome-wide association study (GWAS) can significantly increase the size and diversity of the control sample, enabling high-resolution ancestry matching and enhancing the power to detect association signals. However, the aggregation of controls from multiple sources is challenging due to batch effects, difficulty in identifying genotyping errors, and the use of different genotyping platforms. These obstacles have impeded the use of external controls in GWAS and can lead to spurious results if not carefully addressed. We propose a unified data harmonization pipeline that includes an iterative approach to quality control (QC) and imputation, implemented before and after merging cohorts and arrays. We apply this harmonization pipeline to aggregate 27,517 European control samples from 16 collections within dbGaP. We leverage these harmonized controls to conduct a GWAS of Crohns disease. We demonstrate a boost in power over using the cohort samples alone, and that our procedure results in summary statistics free of any significant batch effects. This harmonization pipeline for aggregating genotype data from multiple sources can also serve other applications where individual level genotypes, rather than summary statistics, are required.

genetics↗

bioRxiv2020

Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

MotivationMarker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. ResultsTo capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementationWe implement this method as an R package markerpen, hosted on https://github.com/yixuan/markerpen. Contactroeder@andrew.cmu.edu

bioinformatics↗

bioRxiv2020

ESCO: single cell expression simulation incorporating gene co-expression

MotivationGene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner. ResultsTherefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data. AvailabilityThe ESCO implementation is available as R package SplatterESCO (https://github.com/JINJINT/SplatterESCO). Contactroeder@andrew.cmu.edu

genetics↗

bioRxiv2020

Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data

Scientists often embed cells into a lower-dimensional space when studying single-cell RNA-seq data for improved downstream analyses such as developmental trajectory analyses, but the statistical properties of such non-linear embedding methods are often not well understood. In this article, we develop the eSVD (exponential-family SVD), a non-linear embedding method for both cells and genes jointly with respect to a random dot product model using exponential-family distributions. Our estimator uses alternating minimization, which enables us to have a computationally-efficient method, prove the identifiability conditions and consistency of our method, and provide statistically-principled procedures to tune our method. All these qualities help advance the single-cell embedding literature, and we provide extensive simulations to demonstrate that the eSVD is competitive compared to other embedding methods. We apply the eSVD via Gaussian distributions where the standard deviations are proportional to the means to analyze a single-cell dataset of oligodendrocytes in mouse brains (Marques et al., 2016). Using the eSVD estimated embedding, we then investigate the cell developmental trajectories of the oligodendrocytes. While previous results are not able to distinguish the trajectories among the mature oligodendrocyte cell types, our diagnostics and results demonstrate there are two major developmental trajectories that diverge at mature oligodendrocytes.

genomics↗

bioRxiv2020

cFIT: Integration and transfer learning of single cell transcriptomes, illustrated by fetal brain cell development

Large, comprehensive collections of scRNA-seq data sets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these data sets or transfer knowledge from one to the other to better understand cellular identity and functions. Here, we present a simple yet surprisingly effective method named cFIT for capturing various batch effects across experiments, technologies, subjects, and even species. The proposed method models the shared information between various data sets by a common factor space, while allowing for unique distortions and shifts in gene-wise expression in each batch. The model parameters are learned under an iterative non-negative matrix factorization (NMF) framework and then used for synchronized integration from across-domain assays. In addition, the model enables transferring via low-rank matrix from more informative data to allow for precise identification in data of lower quality. Compared to existing approaches, our method imposes weaker assumptions on the cell composition of each individual data set, however, is shown to be more reliable in preserving biological variations. We apply cFIT to multiple scRNA-seq data sets of developing brain from human and mouse, varying by technologies and developmental stages. The successful integration and transfer uncover the transcriptional resemblance across systems. The study helps establish a comprehensive landscape of brain cell type diversity and provides insights into brain development.

genomics↗

bioRxiv2020

H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives

The snp-wise mean model of Multi-marker Analysis of GenoMic Annotation is often used to perform gene-level testing for association with disease and other phenotypes. This methodology, in turn, forms the foundation for H-MAGMA. Unfortunately, that foundation is unsound, with implications for H-MAGMA results published in Nature Neuroscience regarding genes associated with psychiatric disorders: e.g., only 125 of H-MAGMAs 275 reported discoveries for autism replicate when the foundations flaws are corrected.

genetics↗

bioRxiv2020

Conditional resampling improves sensitivity and specificity of single cell CRISPR regulatory screens

Single-cell CRISPR screens are the most promising biotechnology for mapping regulatory elements to their target genes at genome-wide scale. However, the analysis of these screens presents significant statistical challenges. For example, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two recent high multiplicity of infection single-cell CRISPR screens how these challenges cause calibration issues among existing analysis methods. To address these challenges, we propose SCEPTRE: analysis of single-cell perturbation screens via conditional re-sampling. This methodology, designed to avoid calibration issues due to technical confounders and expression model misspecification, infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates excellent calibration and sensitivity on the CRISPR screen data and yields hundreds of new regulatory relationships, supported by orthogonal functional evidence.

genomics↗

bioRxiv2020

Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

When assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimers disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

genomics↗

bioRxiv2019

Cognitive and default mode networks support developmental stability in functional connectome fingerprinting through adolescence

Pioneering studies have shown that individual correlation measures from resting-state functional magnetic resonance imaging studies can identify another scan from that same individual. This method is known as "connectotyping" or functional connectome "fingerprinting". We analyzed a unique dataset of 12-30 years old (N=140) individuals who had two distinct resting state scans on the same day and again 12-18 months later to assess the sensitivity and specificity of fingerprinting accuracy across different time scales (same day, ~1.5 years apart) and developmental periods (youths, adults). Sensitivity and specificity to identify ones own scan was high (average AUC=0.94), although it was significantly higher in the same day (average AUC=0.97) than 1.5-year years later (average AUC=0.91). Accuracy in youths (average AUC=0.93) was not significantly different from adults (average AUC=0.96). Multiple statistical methods revealed select connections from the Frontoparietal, Default, and Dorsal Attention networks that enhanced the ability to identify an individual. Identification of these features generalized across datasets and improved fingerprinting accuracy in a longitudinal replication data set (N=208). These results provide a framework for understanding the sensitivity and specificity of fingerprinting accuracy in adolescents and adults at multiple time scales. Importantly, distinct features of ones "fingerprint" contribute to ones uniqueness, suggesting that cognitive and default networks play a primary role in the individualization of ones connectome.

neuroscience↗

bioRxiv2019

Application of post-selection inference to multi-omics data yields insights into the etiologies of human diseases

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptive p-value thresholding (Lei & Fithian 2018, AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS association p-values play the role of the primary data for AdaPT; SNPs are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically-correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene-gene coexpression, captured by subnetwork (module) membership. In all 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefontal cortex (Werling et al. 2019). We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

genetics↗

bioRxiv2019

Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex

Variation in gene expression underlies neurotypical development, while genomic variants contribute to neuropsychiatric disorders. BrainVar is a unique resource of paired whole-genome sequencing and bulk-tissue RNA-sequencing from the human dorsolateral prefrontal cortex of 176 neurotypical individuals across prenatal and postnatal development, providing the opportunity to assay genomic and transcriptomic variation in tandem. Leveraging this resource, we identified rare premature stop codons with commensurate reduced and allele-specific expression of corresponding genes, and common variants that alter gene expression (expression quantitative trait loci, eQTLs). Categorizing eQTLs by prenatal and postnatal effect, genes affected by temporally-specific eQTLs, compared to constitutive eQTLs, are enriched for haploinsufficiency, protein-protein interactions, and neuropsychiatric disorder risk loci. Expression levels of over 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell type specific genes and neuropsychiatric disorder loci, underscoring the importance of cataloguing developmental trajectories in understanding cortical physiology and pathology.\n\nHighlightsO_LIWhole-genome and RNA-sequencing across human prefrontal cortex development in BrainVar\nC_LIO_LIGene-specific developmental trajectories characterize the late-fetal transition\nC_LIO_LIIdentification of constitutive, prenatal-specific, postnatal-specific, and rare eQTLs\nC_LIO_LIIntegrated analysis reveals genetic and developmental influences on CNS traits and disorders\nC_LI

neuroscience↗