Biology subjects

Keles, S.

Publications and source records attributed to Keles, S..

At least 19 recordsLinked to original sources

bioRxiv2026

PETRO enables robust estimation of on-target perturbation effects in Perturb-seq

Perturb-seq uses multiple guides per target as replicates, but off-target effects can bias target-level estimates even with efficient target perturbation. We present PETRO, which recovers the on-target perturbation effect while allowing, for each outcome, an unknown minority of guides to have arbitrary off-target effects. Across CRISPRa and CRISPRi datasets, sequence-associated off-target bias persisted after pooling cells across guides and nearly doubled in one CRISPRa dataset after a published guide filter. PETRO largely removed this bias and reduced transmission of effects from independently flagged promiscuous guides. In simulations, PETRO nearly matched pooling in power without off-target effects; with off-target effects, pooling remained biased as cell numbers increased, whereas PETRO recovered the on-target perturbation effect. In cortical Perturb-seq, most ZNF219 pathway enrichments from pooling rested on one guide and did not survive majority-supported aggregation, unlike those for targets with concordant guides. PETRO enables robust target-level Perturb-seq analysis from replicate-guide agreement.

genomics↗

bioRxiv2026

CMAPS: Causal Mediation Analysis of Perturbation Screens with Application to Genome-scale Perturb-seq Data

CRISPR-Cas9 perturbation screens coupled with single-cell multi-omic profiling enable dissection of gene regulatory mechanisms, yet existing analyses largely quantify total perturbation effects and offer limited insight into the molecular intermediates that transmit these effects. We introduce CMAPS (Causal Mediation Analysis for Perturbation Screens), a semiparametric framework for robust mediation analysis that accommodates unmeasured mediator-outcome confounding and incorporates an adaptive bootstrap test with false discovery rate control. Simulations and data-driven computational experiments show that CMAPS yields accurate, calibrated mediation estimates and robust mediator identification, as confirmed through negative controls and permutation-based validation. Applied to K562 Perturb-seq, CMAPS recapitulates transcriptional cascades downstream of GATA1. In BT16 MultiPerturb-seq data, CMAPS identifies promoter-centric, enhancer-distributed, and mixed cis-regulatory programs linking chromatin remodeling factors to transcriptional responses. CMAPS provides a rigorous and interpretable framework for mechanistic inference in single-cell perturbation screens. CMAPS is implemented in R and is available at https://github.com/keleslab/CMAPS.

genomics↗

bioRxiv2026

Causal gene regulatory network inference from Perturb-seq via adaptive instrumental variable modeling

Inferring causal gene regulatory networks (GRNs) from observational single-cell data is challenging due to confounding. While Perturb-seq provides causal leverage, existing methods are often biased by heterogeneous CRISPRi knockdown efficiencies and restrictive assumptions like acyclicity. We present ADAPRE, a framework that treats CRISPR interventions as instrumental variables within a Poisson-lognormal model. By adaptively accounting for variable perturbation strength, ADAPRE recovers potentially cyclic structures and outperforms existing methods. Applied to a genome-wide K562 Perturb-seq dataset, it reconstructs networks enriched for known biological interactions and identifies coherent, leukemia-associated subnetworks, establishing a scalable approach for causal GRN inference.

genomics↗

bioRxiv2025

Sex-Specific Differential DNA Methylation in Mild Cognitive Impairment and Alzheimers Disease

Sex differences in late-onset Alzheimers disease (AD) progression include accelerated decrements in cognitive status and greater amyloid and tau biomarker burdens in females. To identify sex-specific differentially methylated positions (DMPs) and genes in persons with mild cognitive impairment (MCI) and AD, we analyzed whole genome methylation sequencing on blood samples from participants with MCI (N=99, 52% female), AD (N=109, 43% female), and those cognitively unimpaired (CU; N=174, 52% female). Ninety-four percent of DMPs from MCI vs. CU, AD vs. CU, and AD vs. MCI pairwise comparisons were sex-specific. Female-specific DMPs were enriched in neurologic gene sets (e.g., synaptic membrane, ion channel complex), while male-specific DMPs showed limited enrichment. Sex-specific DMPs overlapped blood-specific enhancers, promoters, and transcription factor binding motifs, highlighting divergent epigenetic regulation by sex. These findings identify sex-specific genes and molecular pathways in MCI and AD and support that blood DNA methylation levels can distinguish cognitive status.

genomics↗

bioRxiv2025

Systematic Background Selection for Enhanced Contrastive Dimension Reduction

Contrastive dimension reduction enhances the analysis of high-dimensional data by generating target-specific low-dimensional representations relative to a background. Emerging methods for contrastive dimension reduction have demonstrated their utility in extracting target-specific signals across domains involving high-dimensional observations, including genomics, transcriptomics, and pattern recognition. However, even though choosing an appropriate background is critical to the success of contrastive dimension reduction, no established criterion currently exists for selecting such backgrounds. To address this gap, we introduce BasCoD, a novel testing framework grounded in the spectral theory of subspace inclusion, to enable rigorous evaluation and optimal selection of backgrounds. Extensive application of BasCoD across diverse single-cell datasets demonstrates its effectiveness in systematically identifying suitable backgrounds, thereby significantly improving the contrast and interpretability of the derived target representations. We further illustrate how BasCoD can facilitate design of appropriate backgrounds in large-scale single cell experiments under heterogeneous conditions.

genomics↗

bioRxiv2024

Whole genome methylation sequencing in blood from persons with mild cognitive impairment and dementia due to Alzheimer's disease identifies cognitive status

INTRODUCTIONWhole genome methylation sequencing (WGMS) in blood identifies differential DNA methylation in persons with late-onset dementia due to Alzheimers disease (AD) but has not been tested in persons with mild cognitive impairment (MCI). METHODSWe used WGMS to compare DNA methylation levels at 25,244,219 CpG loci in 382 blood samples from 99 persons with MCI, 109 with AD, and 174 who are cognitively unimpaired (CU). RESULTSWGMS identified 9,756 differentially methylated positions (DMPs) in persons with MCI, including 1,743 differentially methylated genes encoding proteins in biological pathways related to synapse organization, dendrite development, and ion transport. 447 DMPs exhibit progressively increasing or decreasing DNA methylation levels between CU, MCI, and AD that correspond to cognitive status. DISCUSSIONWGMS identifies DMPs in known and newly detected genes in blood from persons with MCI and AD that support blood DNA methylation levels as candidate biomarkers of cognitive status.

genomics↗

bioRxiv2024

Empirical Bayes spline model learns multi-way genomic interactions from single cell 3D genome data

A number of foundational analysis methods have emerged for single cell high throughput chromatin conformation (scHi-C) datasets capturing 3D organizations of genomes with pairwise measurements at the single cell or nuclei resolution; however, these datasets are currently under-utilized. The canonical analyses of scHi-C data encompass, beyond standard cell type identification, inference of chromosomal structures and pairwise interactions. However, multi-way chromatin interactions among genomic elements are entirely overlooked. We introduce MINTsC to learn Multi-way INTeractions from single cell Hi-C. MINTsC builds on a dirichlet-multinomial spline model and yields multi-way interaction scores by aggregating pairwise interactions across cells of a context and summarizing them using order statistics of pairwise test statistics. MINTsC yields well-calibrated p-values for controlling the false discovery rate. Evaluation of MINTsC with scHi-C datasets from cell lines and complex tissues using multiple external genomic and epigenomic datasets support multi-way interactions inferred by MINTsC. Application of MINTsC to scHi-C data from human prefrontal cortex revealed multi-way chromatin interactions with biological implications of gene regulation by multiple enhancers. Most notably, MINTsC-inferred multi-way interactions demonstrate its potential for probing molecular QTL and association studies for epistatic SNP effects by significantly reducing the multiple-testing burden. MINTsC is publicly available at https://github.com/keleslab/mintsc.

genomics↗

bioRxiv2023

Deep Phenotyping of Sleep in Drosophila

Animal behavior depends on internal state. While subtle movements can signify significant changes in internal state, computational methods for analyzing these "microbehaviors" are lacking. Here, we present FlyVISTA, a machine-learning platform to characterize microbehaviors in freely-moving flies, which we use to perform deep phenotyping of sleep. This platform comprises a high-resolution closed-loop video imaging system, coupled with a deep-learning network to annotate 35 body parts, and a computational pipeline to extract behaviors from high-dimensional data. FlyVISTA reveals the distinct spatiotemporal dynamics of sleep-associated microbehaviors in flies. We further show that stimulation of dorsal fan-shaped body neurons induces micromovements, not sleep, whereas activating R5 ring neurons triggers rhythmic proboscis extension followed by persistent sleep. Importantly, we identify a novel microbehavior ("haltere switch") exclusively seen during quiescence that indicates a deeper sleep stage. These findings enable the rigorous analysis of sleep in Drosophila and set the stage for computational analyses of microbehaviors.

neuroscience↗

bioRxiv2023

Dozer: Debiased personalized gene co-expression networks for population-scale scRNA-seq data

Population-scale single cell RNA-seq (scRNA-seq) datasets create unique opportunities for quantifying expression variation across individuals at the gene co-expression network level. Estimation of co-expression networks is well-established for bulk RNA-seq; however, single-cell measurements pose novel challenges due to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased towards zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq datasets and accurately quantify network level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the datasets. Compared to alternatives, Dozer results in fewer false positive edges in the co-expression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the datasets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Co-expression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer disease and controls uniquely reveals co-expression modules of innate immune response with markedly different co-expression levels between the diagnoses. Dozer represents an important advance in estimating personalized co-expression networks from scRNA-seq data.

genomics↗

bioRxiv2023

Joint tensor modeling of single cell 3D genome and epigenetic data with Muscle

Emerging single cell technologies that simultaneously capture long-range interactions of genomic loci together with their DNA methylation levels are advancing our understanding of three-dimensional genome structure and its interplay with the epigenome at the single cell level. While methods to analyze data from single cell high throughput chromatin conformation capture (scHi-C) experiments are maturing, methods that can jointly analyze multiple single cell modalities with scHi-C data are lacking. Here, we introduce Muscle, a semi-nonnegative joint decomposition of Multiple single cell tensors, to jointly analyze 3D conformation and DNA methylation data at the single cell level. Muscle takes advantage of the inherent tensor structure of the scHi-C data, and integrates this modality with DNA methylation. We developed an alternating least squares algorithm for estimating Muscle parameters and established its optimality properties. Parameters estimated by Muscle directly align with the key components of the downstream analysis of scHi-C data in a cell type specific manner. Evaluations with data-driven experiments and simulations demonstrate the advantages of the joint modeling framework of Muscle over single modality modeling or a baseline multi modality modeling for cell type delineation and elucidating associations between modalities. Muscle is publicly available at https://github.com/keleslab/muscle.

genomics↗

bioRxiv2022

AdaLiftOver: High-resolution identification of orthologous regulatory elements with adaptive liftOver

Elucidating orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from the genome-wide association studies. Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues. We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets and GWAS SNPs yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs. The R package AdaLiftOver is available from https://github.com/ThomasDCY/AdaLiftOver.

bioinformatics↗

bioRxiv2021

MuDCoD: Multi-Subject Community Detection in Personalized Dynamic Gene Networks from Single Cell RNA Sequencing

MotivationWith the wide availability of single-cell RNA-seq (scRNA-seq) technology, population-scale scRNA-seq datasets across multiple individuals and time points are emerging. While the initial investigations of these datasets tend to focus on standard analysis of clustering and differential expression, leveraging the power of scRNA-seq data at the personalized dynamic gene co-expression network level has the potential to unlock subject and/or time-specific network-level variation, which is critical for understanding phenotypic differences. Community detection from co-expression networks of multiple time points or conditions has been well-studied; however, none of the existing settings included networks from multiple subjects and multiple time points simultaneously. To address this, we develop MuDCoD for multi-subject community detection in personalized dynamic gene networks from scRNA-seq. MuDCoD builds on the spectral clustering framework and promotes information sharing among the networks of the subjects as well as networks at different time points. It clusters genes in the personalized dynamic gene networks and reveals gene communities that are variable or shared not only across time but also among subjects. ResultsEvaluation and benchmarking of MuDCoD against existing approaches reveal that MuDCoD effectively leverages apparent shared signals among networks of the subjects at individual time points, and performs robustly when there is no or little information sharing among the networks. Applications to population-scale scRNA-seq datasets of human-induced pluripotent stem cells during dopaminergic neuron differentiation and CD4+ T cell activation indicate that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid in the exploration of subject-specific biological processes that vary across time. AvailabilityMuDCoD is publicly available at https://github.com/bo1929/MuDCoD as a Python package. Implementation includes simulation and real-data experiments together with extensive documentation. Contactkeles@stat.wisc.edu, otastan@sabanciuniv.edu Supplementary informationSupplementary data are available at Bioinformatics online.

genomics↗

bioRxiv2021

scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data

SummaryQuantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and to integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene level while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. scGAD enables identifying genes with significant chromatin interactions within and between cell types. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. AvailabilityscGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html. Contactkeles@stat.wisc.edu, yzheng23@fredhutch.org Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Genome-wide association studies have revealed many non-coding variants associated with complex traits. However, model organism studies have largely remained as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for Diversity Outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMAs superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/

bioinformatics↗

bioRxiv2021

Gene by environment interaction mouse model reveals a functional role for 5-hydroxymethylcytosine in neurodevelopmental disorders

Mouse knockouts of Cntnap2 exhibit altered neurodevelopmental behavior and a genome-wide disruption of 5-hydroxymethylcytosine (5hmC). Here we examined whether adult Cntnap2 heterozygous mice (Cntnap2+/-, lacking behavioral or neuropathological abnormalities) subjected to a prenatal stress would have disruptions in brain 5hmC levels and exhibit altered behaviors similar to the knockout mice. Adult prenatally stressed Cntnap2+/- female mice showed repetitive behaviors and altered sociability, similar to the homozygote phenotype. Genomic profiling revealed disruptions in hippocampal and striatal 5hmC levels that were correlated to altered transcript levels of genes linked to these phenotypes (e.g., Reln, Dst, Trio and Epha5). Chromatin-immunoprecipitation coupled with high-throughput sequencing and hippocampal nuclear lysate pull-down data indicated that 5hmC abundance alters the binding of the transcription factor CLOCK near the promoters of differentially expressed genes (e.g., Palld, Gigyf1, and Fry), providing a mechanistic role for 5hmC (disruption of transcription factor binding) in gene regulation of developmentally important genes.

genetics↗

bioRxiv2021

MLG: Multilayer graph clustering for multi-condition scRNA-seq data

Single-cell transcriptome sequencing (scRNA-seq) enabled investigations of cellular heterogeneity at exceedingly higher resolutions. Identification of novel cell types or transient developmental stages across multiple experimental conditions is one of its key applications. Linear and non-linear dimensionality reduction for data integration became a foundational tool in inference from scRNA-seq data. We present Multi Layer Graph Clustering (MLG) as an integrative approach for combining multiple dimensionality reduction of multi-condition scRNA-seq data. MLG generates a multilayer shared nearest neighbor cell graph with higher signal-to-noise ratio and outperforms current best practices in terms of clustering accuracy across large-scale bench-marking experiments. Application of MLG to a wide variety of datasets from multiple conditions highlights how MLG boosts signal-to-noise ratio for fine-grained sub-population identification. MLG is widely applicable to settings with single cell data integration via dimension reduction.

genomics↗

bioRxiv2021

Normalization and De-noising of Single-cell Hi-C Data with BandNorm and 3DVI

Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling long-range genomic interactions at the single-cell resolution; however, data from these technologies are prone to technical noise and bias that, when unaccounted for, hinder downstream analysis. Here we developed a fast band normalization approach, BandNorm, and a deep generative modeling framework, 3DVI, to explicitly account for scHi-C specific technical biases. We present robust performances of BandNorm and 3DVI compared to existing state-of-the-art methods. BandNorm is effective in separating cell types, identification of interaction features, and recovery of cell-cell relationship, whereas de-noising by 3DVI successfully enables 3D compartments and domains recovery, especially for rare cell types.

bioinformatics↗

bioRxiv2020

Single cell analysis of FOXP3 deficiencies in humans and mice unmasks intrinsic and extrinsic CD4+ T cell perturbations

ABSTRACTFOXP3 deficiency in humans with IPEX syndrome and mice results in fatal systemic autoimmunity by altering regulatory T cell (Treg) physiology, but actual cellular and molecular mechanisms of disease are unclear, part because Treg surface markers may be unreliable in disease states. We used deep profiling by flow cytometry, population and single-cell RNAseq to analyze Tregs and conventional (Tconv) CD4+ T lymphocytes in cohorts of IPEX patients with a range of genetic lesions, and in Foxp3-deficient mice. In all patients and mice, heterogeneous Treg-like cells with an active FOXP3 locus were observed, some differing very little from normal Tregs, others more distant. Tconv showed no widespread activation or Th bias. The dominant mark was a monomorphic signature equally affecting all CD4+ T cells, unexpectedly dampening tumor-Treg and cytokine-signaling modules. In mixed bone marrow chimeras, WT Tregs exerted dominant suppression, normalizing the states of mutant Treg and Tconv, extinguishing the disease signature, and revealing a small gene cluster truly regulated, cell-intrinsically, by FOXP3. These results suggest a two-step pathogenesis model, with therapeutic implications: limited downregulation of a few core Treg genes de-represses a systemic mediator(s), which imprints the disease signature on all T cells, and further dampens Treg function.Competing Interest StatementThe authors have declared no competing interest.View Full Text

immunology↗