Biology subjects

Kostka, D.

Publications and source records attributed to Kostka, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Microhomology-Driven Genomic Alterations in Cancer Genomes: Patterns, Prevalence, and Clinical Implications.

Homologous recombination deficiency (HRD) can force cancer cells to rely on alternative DNA repair pathways, including microhomology-mediated end joining (MMEJ), an error-prone mechanism that can generate deletions with microhomology at repair junctions. Because such patterns may reflect DNA repair defects with clinical relevance, this study aimed to assess the biological and prognostic significance of microhomology-associated deletions and optimize their detection parameters to improve patient stratification and inform targeted therapeutic strategies, including PARP inhibition. We optimized the microhomology length threshold (Mlt parameter) using signal-to-noise ratios and survival models. We evaluated the utility of whole-exome (WES) versus whole-genome sequencing (WGS). We then investigated how Loss of Function (LoF) alterations affect microhomology-associated deletion burden and assessed their prognostic significance across an ovarian cancer (OV) cohort and a TCGA Pan-Cancer dataset. Applying an Mlt = 2 threshold maximized the precision and statistical reliability of microhomology-associated deletion identification. WGS yielded higher reproducibility, whereas WES proved insufficient for accurate estimation. Inactivation of tumor suppressor genes, including RB1 and CCDC122, was associated with increased microhomology-associated deletion burden. Importantly, higher burden correlated with extended overall survival in ovarian cancer. Furthermore, while baseline microhomology-associated deletion burden varies by tumor type, Pan-Cancer analysis identified candidate gene-level alterations, including FDX1 and PDE8B, whose LoF increased microhomology-associated deletion burden across tumors. Precise parameterization combined with WGS resolution highlights the extent to which specific gene losses affect deletion burden consistent with MMEJ-mediated repair. Identifying these genetic vulnerabilities and the resulting deletion burden may provide a candidate prognostic biomarker and foundation for personalized therapies.

bioinformatics↗

bioRxiv2026

Correction of the cytosine deamination artifacts in FFPE-based sequencing experiments

Formalin-fixed, paraffin-embedded (FFPE) tissues remain an essential resource for molecular studies, yet formalin-induced cytosine deamination introduces characteristic C>T/G>A artifacts that compromise the accuracy of next-generation sequencing (NGS) analyses. Numerous computational methods and enzymatic DNA repair strategies have been proposed to reduce these artifacts, but no systematic comparison across tools and experimental conditions exists. Here, we evaluate the performance of seven computational approaches (SOBDetector, Ideafix, MicroSEC, FFPolish, DeepOmics FFPE/FFPE-PLUS, FFPErase) together with the NEBNext(R) FFPE DNA Repair Mix v2, a multi-enzyme repair system applied during DNA preparation. Using three independent datasets, one based on whole genome sequencing (CGCI-BL) and two on whole exome sequencing (TCGA-PC and SUT-LUAD, the latter containing enzymatically repaired samples), and matched fresh-frozen samples as the gold standard, we assess precision, sensitivity, and artifact reduction efficiency across all methods. We further examine the potential synergy between enzymatic repair and post-sequencing computational filtering. Our results provide practical guidelines for FFPE artifact correction and demonstrate that enzymatic treatment provides the best results, while among the computational methods, FFPErase offers the most robust reduction of cytosine deamination artifacts while maximizing the retention of true somatic variants. KEY MESSAGESO_LIFormalin fixation in FFPE samples introduces artifacts that can significantly affect the accuracy of NGS analyses. C_LIO_LIAmong the evaluated approaches, enzymatic repair using NEBNext(R) FFPE DNA Repair Mix v2 achieves the most effective reduction of sequencing artifacts. C_LIO_LIComputational methods vary in performance, with FFPErase showing the most robust balance between artifact removal and retention of true somatic variants. C_LIO_LICombining enzymatic repair with computational filtering did not lead to consistent improvements in performance across datasets. C_LI

bioinformatics↗

bioRxiv2026

When Less Is Enough: Low-Rank Structure in DNA Sequence-to-Function Models

MotivationThe rapid success of deep learning sequence-to-function (S2F) models has driven a trend toward ever larger architectures for regulatory genomics. While these models achieve meaningful predictive performance, their growing size and computational cost have made them increasingly opaque, difficult to deploy, and impractical for widespread use. As S2F models mature, an open question is how much model capacity is truly necessary to represent regulatory sequence function. ResultsWe show that, within linear layers, much of the predictive power of state-of-the-art S2F models is concentrated in low-rank structure. Using post hoc singular value decomposition, we construct low-rank approximations of the Sei model that reduce model size by up to 90% while preserving over 90% correlation with full-model predictions. Strikingly, extremely low-rank models--including rank 1--can match or exceed full-model performance on several variant-effect benchmarks. This indicates dominant regulatory signals may lie in a surprisingly low-dimensional subspace. Combining low-rank approximation with static model quantization enables practical CPU inference, yielding over 5x speedups relative to the full Sei model and more than 100x faster inference than larger S2F models on comparable tasks. Applying the same approach to Borzoi, Enformer, and AlphaGenome reveals consistent behavior, demonstrating that low-rank structure is a general property of modern S2F architectures, not a model-specific artifact. Availability and ImplementationLow-rank Sei is available at https://github.com/kostkalab/seillra. Contactmchikina@pitt.edu, kostka@pitt.edu

bioinformatics↗

bioRxiv2025

Frustratingly easy domain adaptation for cross-speciestranscription factor binding prediction

MotivationUnderstanding how DNA sequence encodes gene regulation remains a central challenge in genomics. While deep learning models can predict regulatory activity from sequence with high accuracy, their generalizability across species--and thus their ability to capture fundamental biological principles--remains limited. Cross-species prediction provides a powerful test of model robustness and offers a window into conserved regulatory logic, but effectively bridging species-specific genomic differences remains a major barrier. ResultsWe present MORALE, a novel and scalable domain adaptation framework that significantly advances cross-species prediction of transcription factor (TF) binding. By aligning statistical moments of sequence embeddings across species, MORALE enables deep learning models to learn species-invariant regulatory features without requiring adversarial training or complex architectures. Applied to multi-species TF ChIP-seq datasets, MORALE achieves state-of-the-art performance--outperforming both baseline and adversarial approaches across all TFs--while preserving model interpretability and recovering canonical motifs with greater precision. In the five-species transfer setting, MORALE not only improves human prediction accuracy beyond human-only training but also reveals regulatory features conserved across mammals. These results highlight the potential of simple yet powerful domain adaptation techniques to drive generalization and discovery in regulatory genomics. Crucially, MORALE is architecture-agnostic and can be seamlessly integrated into any embedding-based sequence model. AvailabilityCode is available at https://github.com/loudrxiv/frustrating.

genomics↗

bioRxiv2025

Zebrafish optic nerve injury results in systemic retinal ganglion cell dedifferentiation

Retinal ganglion cells (RGCs) are the sole projection neurons connecting the retina to the brain and therefore play a critical role in vision. Death of RGCs during glaucoma, optic neuropathies and after ocular trauma results in irreversible loss of vision as RGCs do not regenerate in the human eye. Moreover, there are no FDA approved therapies that prevent RGC death and/or promote RGC survival in the diseased or injured eye. There is a critical need to better understand the molecular underpinnings of neuroprotection to develop effective therapeutic approaches to preserve damaged RGCs. Unlike in mammals, RGCs in zebrafish are resilient to optic nerve injury, even after complete transection of the optic nerve. Here, we leveraged this unique model and utilized single-cell RNA sequencing to characterize RGC responses to injury and identify putative neuroprotective and regenerative pathways. RGCs are heterogeneous and studies in mice have shown that there is differential resiliency across RGC subtypes. Our results demonstrated that all RGC subtypes are resilient to injury in zebrafish. Quantifying changes in gene expression revealed the upregulation of progenitor and regenerative markers in all RGC subtypes after injury as well as distinct early and late phases to the injury response. This shift in gene expression causes injury-responsive RGCs to resemble RGC subtype 3, a low frequency population of endogenous immature RGCs that are normally maintained in the wild-type, uninjured adult retina. A similar but restricted transcriptomic injury response in RGCs of the uninjured contralateral eye was also detected, highlighting a systemic RGC response to unilateral optic nerve injury. Taken together, these results demonstrate that zebrafish RGCs dedifferentiate in response to injury, and this may be a novel mechanism mediating their unique cell survival and regenerative capabilities. Author SummaryRetinal ganglion cells (RGCs) connect the eye to the brain and are essential for vision. Their death in conditions like glaucoma, affecting over 70 million people worldwide, leads to permanent blindness, with no FDA-approved treatments to prevent it. Unlike mammals, zebrafish RGCs are resilient to optic nerve injury. In this study, we used next-generation sequencing technologies to characterize the RGC response to optic nerve injury at the single-cell level. We discovered that all zebrafish RGCs survive damage by temporarily shifting into a less mature state, resembling a rare population of immature RGCs found in uninjured animals. We identified many genes whose expression changes early or late in the injury response as well as a similar but restricted transcriptomic injury response in the uninjured contralateral RGCs, highlighting the systemic RGC response to optic nerve injury. This work is significant because our detailed characterization of RGC responses to optic nerve injury identifies dedifferentiation as an injury response, possibly important for cell survival and axon regrowth. The genes and pathways we identify are potential therapeutic targets to enable RGC survival in the injured or diseased human eye.

neuroscience↗

bioRxiv2024

tet2 and tet3 regulate cell fate specification and differentiation events during retinal development

AbstractTet enzymes are epigenetic modifiers that impact gene expression via 5mC to 5hmC oxidation. Previous work demonstrated the requirement for Tet and 5hmC during zebrafish retinogenesis. tet2-/-;tet3-/-mutants possessed defects in the formation of differentiated retinal neurons, but the mechanisms underlying these defects are unknown. Here, we leveraged scRNAseq technologies to better understand cell type-specific deficits and molecular signatures underlying the tet2-/-;tet3-/- retinal phenotype. Our results identified defects in the tet2-/-;tet3-/- retinae that included delayed specification of several retinal cell types, reduced maturity across late-stage cones, expansions of immature subpopulations of horizontal and bipolar cells, and altered biases of bipolar cell subtype fates at late differentiation stages. Together, these data highlight the critical role that tet2 and tet3 play as regulators of cell fate specification and terminal differentiation events during retinal development.

developmental biology↗

bioRxiv2024

Ultra-fast variant effect prediction using biophysical transcription factor binding models

Sequence variation within TF binding sites can significantly affect TF-DNA interactions, influencing gene expression and contributing to disease susceptibility or phenotypic traits. Despite recent progress in deep sequence-to-function models that predict functional output from sequence data, these methods perform inadequately on some variant effect prediction tasks, especially with common genetic variants. This limitation underscores the importance of leveraging biophysical models of TF binding to enhance interpretability of variant effect scores and facilitate mechanistic insights. We introduce motifDiff, a novel computational tool designed to quantify variant effects using mono and dinucleotide position weight matrices. motifDiff offers several key advantages, including scalability to score millions of variants within minutes, implementation of statistically rigorous normalization strategy critical for optimal performance, and support for both dinucleotide and mononucleotide models. We demonstrate motifDiff s efficacy by evaluating it across diverse ground truth datasets that quantify the effects of common variants in vivo, thereby establishing robust benchmarks for the predictive value of variant effect calculations. Finally, we show that our tool provides unique insights when interpreting human accelerated regions. motifDiff is available as a standalone Python application at https://github.com/rezwanhosseini/MotifDiff.

bioinformatics↗

bioRxiv2023

Spatial transcriptome profiling uncovers metabolic regulation of left-right patterning

Left-right patterning disturbance can cause severe birth defects, but it remains least understood of the three body axes. We uncovered an unexpected role for metabolic regulation in left-right patterning. Analysis of the first spatial transcriptome profile of left-right patterning revealed global activation of glycolysis, accompanied by right-sided expression of Bmp7 and genes regulating insulin growth factor signaling. Cardiomyocyte differentiation was left-biased, which may underlie the specification of heart looping orientation. This is consistent with known Bmp7 stimulation of glycolysis and glycolysis suppression of cardiomyocyte differentiation. Liver/lung laterality may be specified via similar metabolic regulation of endoderm differentiation. Myo1d, found to be left-sided, was shown to regulate gut looping in mice, zebrafish, and human. Together these findings indicate metabolic regulation of left-right patterning. This could underlie high incidence of heterotaxy-related birth defects in maternal diabetes, and the association of PFKP, allosteric enzyme regulating glycolysis, with heterotaxy. This transcriptome dataset will be invaluable for interrogating birth defects involving laterality disturbance.

developmental biology↗

bioRxiv2023

aenmd: Annotating escape from nonsense-mediated decay for transcripts with protein-truncating variants

DNA changes that cause premature termination codons (PTCs) represent a large fraction of clinically relevant pathogenic genomic variation. Typically, PTCs induce a transcripts degradation by nonsense-mediated mRNA decay (NMD) and render such changes loss-of-function alleles. However, certain PTC-containing transcripts escape NMD and can exert dominant-negative or gain-of-function (DN/GOF) effects. Therefore, systematic identification of human PTC-causing variants and their susceptibility to NMD contributes to the investigation of the role of DN/GOF alleles in human disease. Here we present aenmd, a software for annotating PTC-containing transcript-variant pairs for predicted escape from NMD. aenmd is user-friendly and self-contained. It offers functionality not currently available in other methods and is based on established and experimentally validated rules for NMD escape; the software is designed to work at scale, and to integrate seamlessly with existing analysis workflows. We applied aenmd to variants in the gnomAD, Clinvar, and GWAS catalog databases and report the prevalence of human PTC-causing variants in these databases, and the subset of these that could exert DN/GOF effects via NMD escape. Availability and implementationaenmd is implemented in the R programming language. Code is available on GitHub as an R package (github.com/kostkalab/aenmd.git), and as a containerized command-line interface (github.com/kostkalab/aenmd_cli.git).

bioinformatics↗

bioRxiv2023

An intrinsically interpretable neural network architecture for sequence to function learning

MotivationSequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called tiSFM (totally interpretable sequence to function model). tiSFM improves upon the performance of standard multi-layer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multi-layer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. ResultsWe analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFMs model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. Availability and implementationThe source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python. Contactatb44@pitt.edu

bioinformatics↗

bioRxiv2022

Vaeda computationally annotates doublets in single-cell RNA sequencing data

MotivationSingle-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technologys utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation computational methods to annotate and remove doublets form scRNA-seq datasets are needed. ResultsWe introduce vaeda, a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to sixteen benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. All together, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows. AvailabilityVaeda is available at https://github.com/kostkalab/vaeda Contactkostka@pitt.edukostka@pitt.edu

bioinformatics↗

bioRxiv2021

iPSC modeling shows uncompensated mitochondrial mediated oxidative stress underlies early heart failure in hypoplastic left heart syndrome

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect with 30% mortality from heart failure (HF) in the first year of life, but why only some patients suffer early-HF and its cause remain unknown. Modeling using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) showed early-HF patient iPSC-CM have increased apoptosis, redox stress, and failed antioxidant response. This was associated with mitochondrial permeability transition pore (mPTP) opening, mitochondrial hyperfusion and respiration defects. Whereas iPSC-CM from patients without early-HF had hyper-elevated antioxidant response with increased mitochondrial fission and mitophagy. Single cell transcriptomics showed dichotomization by HF outcome, with mitochondrial dysfunction and endoplasmic reticulum (ER) stress associated with early-HF. Importantly, oxidative stress and apoptosis associated with early HF were rescued by sildenafil inhibition of mPTP opening or TUDCA suppression of ER stress. Together these findings demonstrate a new paradigm for modeling clinical outcome in iPSC-CM, demonstrating uncompensated mitochondrial oxidative stress underlies early HF in HLHS.

cell biology↗

bioRxiv2021

Chromatin accessibility and microRNA expression in nephron progenitor cells during kidney development

Mammalian nephrons originate from a population of nephron progenitor cells (NPCs), and it is known that NPCs transcriptomes change throughout nephrogenesis during healthy kidney development. To characterize chromatin accessibility and microRNA (miRNA) expression throughout this process, we collected NPCs from mouse kidneys at embryonic day 14.5 (E14.5) and postnatal day zero (P0) and assayed cells for transposase-accessible chromatin and small RNA expression. We observe 46,374 genomic regions of accessible chromatin, with 2,103 showing significant changes in accessibility between E14.5 and P0. In addition, we detect 1,104 known microRNAs, with 114 showing significant changes in expression. Genome-wide, changes in DNA accessibility and microRNA expression highlight biological processes like cellular differentiation, cell migration, extracellular matrix interactions, and developmental signaling pathways such as Notch. Furthermore, our data identify novel candidate cis-regulatory elements for Eya1 and Pax8, both genes with a role in NPC differentiation; we also associate expression-changing microRNAs, including let-7-5p, miR-125b-5p, miR-181a-2-3p, and miR-9-3p, with candidate cis-regulatory elements. Overall, our data characterize NPCs during kidney development and point out new candidate regulatory elements for genes and microRNA with key roles in nephrogenesis.

developmental biology↗

bioRxiv2020

Computational profiling of hiPSC-derived heart organoids reveals chamber defects associated with Ebstein's anomaly

Heart organoids have the potential to generate primary heart-like anatomical structures and hold great promise as in vitro models for cardiac disease. However, their properties have not yet been carefully studied, which hinders a wider spread application. Here we report the development of differentiation systems for ventricular and atrial heart organoids, enabling the study of heart disease with chamber defects. We show that our systems generate organoids comprising of major cardiac cell types, and we used single cell RNA sequencing together with sample multiplexing to characterize the cells we generate. To that end, we also developed a machine learning label transfer approach lever-aging cell type, chamber, and laterality annotations available for primary human fetal heart cells. We then used this model to analyze organoid cells from an isogeneic line carrying an Ebsteins anomaly associated genetic variant, and we successfully recapitulated the diseases atrialized ventricular defects. In summary, we have established a workflow integrating heart organoids and computational analysis to model heart development in normal and disease states.

developmental biology↗

bioRxiv2020

Single cell RNA sequencing reveals differential cell cycle activity in key cell populations during nephrogenesis

The kidney is a complex organ composed of more than 30 terminally differentiated cell types that all are required to perform its numerous homeostatic functions. Defects in kidney development are a significant cause of chronic kidney disease in children, which can lead to kidney failure that can only be treated by transplant or dialysis. A better understanding of molecular mechanisms that drive kidney development is important for designing strategies to enhance renal repair and regeneration. In this study, we profiled gene expression in the developing mouse kidney at embryonic day 14.5 at single cell resolution. Consistent with previous studies, clusters with distinct transcriptional signatures clearly identify major compartments and cell types of the developing kidney. Cell cycle activity distinguishes between the "primed" and "self-renewing" sub-populations of nephron progenitors, with increased expression of the cell cycle related genes Birc5, Cdca3, Smc2 and Smc4 in "primed" nephron progenitors. Augmented Birc5 expression was also detected in immature distal tubules and a sub-set of ureteric bud cells, suggesting that Birc5 might be a novel key molecule required for early events of nephron patterning and tubular fusion between the distal nephron and the collecting duct epithelia.

genomics↗

bioRxiv2020

Non-negative Independent Factor Analysis for single cell RNA-seq

MotivationSingle-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, dimensionality reduction should preserve the biological information while discarding the noise. However if the dimensionality reduction is to be used directly to gain biological insight it must also be interpretable - that is the individual dimensions of the reduction should correspond to specific biological variables such as cell-type identity or pathway activity. Maximizing biological interpretability necessitates making assumption about the data structures and the choice of the model is critical. ResultsWe present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that incorporates different interpretability inducing assumptions into a single modeling framework. The key advantage of our NIFA model is that it simultaneously models uni- and multi-modal latent factors, and thus isolates discrete cell-type identity and continuous pathway activity into separate components. We apply our approach to a range of datasets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA, NMF and scCoGAPS (an NMF method designed for single-cell data) in terms of disentangling biological sources of variation. Studying an immunotherapy dataset in detail, we show that NIFA is able to reproduce and refine previous findings in a single analysis framework and enables the discovery of new clinically relevant cell states. AvailabilityNFIA is a R package which is freely available at GitHub (https://github.com/wgmao/NIFA). Contactmchikina@pitt.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Control of cytokinesis by β-adrenergic receptors indicates an approach for regulating cardiomyocyte endowment

ABSTRACT/SUMMARYOne million patients with congenital heart disease (CHD) live in the US. They have a lifelong risk of developing heart failure. Current concepts do not sufficiently address mechanisms of heart failure development specifically for these patients. We show that cardiomyocyte cytokinesis failure is increased in tetralogy of Fallot with pulmonary stenosis (ToF/PS), a common form of CHD. Labeling of a ToF/PS baby with isotope-tagged thymidine showed cytokinesis failure after birth. We used single-cell transcriptional profiling to discover that the underlying mechanism is repression of the cytokinesis gene ECT2, and show that this is downstream of {beta}-adrenergic receptors ({beta}-AR). Inactivation of the {beta}-AR genes and administration of the {beta}-blocker propranolol increased cardiomyocyte division in neonatal mice, which increased the endowment and conferred benefit after myocardial infarction in adults. Propranolol enabled the division of ToF/PS cardiomyocytes. These results suggest that {beta}-blockers should be evaluated for increasing cardiomyocyte division in patients with ToF/PS and other types of CHD.

cell biology↗

bioRxiv2019

scds: Computational Annotation of Doublets in Single Cell RNA Sequencing Data

MotivationSingle cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. Specifically, high-throughput approaches that employ micro-fluidics in combination with unique molecular identifiers (UMIs) are capable of assaying many thousands of cells per experiment and are rapidly becoming commonplace. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a studys conclusions, and therefore computational strategies for the identification of doublets are needed. Here we present single cell doublet scoring (scds), a software tool for the in silico identification of doublets in scRNA-seq data. ResultsWith scds, we propose two new and complementary approaches for doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and employs a binomial model for the co-expression of pairs of genes and yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from the original data. We apply our methods and existing doublet identification approaches to four data sets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, but at comparably little computational cost. We also find appreciable differences between methods and across data sets, that no approach dominates all others, and we believe there is room for improvement in computational doublet identification as more data with experimental annotations becomes available. In the meanwhile, scds presents a scalable, competitive approach that allows for doublet annotations in thousands of cells in a matter of seconds. Availability and Implementationscds is implemented as an R package and freely available at https://github.com/kostkalab/scds. Contactkostka@pitt.edu

bioinformatics↗