Biology subjects

Wrobel, J.

Publications and source records attributed to Wrobel, J..

At least 19 recordsLinked to original sources

bioRxiv2026

multiScaleAC: Cell-Cell interaction with Moran's I as a function of kernel bandwidth

Over the last decade, spatial transcriptomic technology has transformed our understanding of tissue architecture including cell-cell interactions within the tumor immune microenvironment. A specific use-case of increasing interest is leveraging the spatial statistical relationship of genes whose protein products are known to be involved in ligand-receptor interactions. One methodological limitation of this approach has been the requirement to choose one radius around a cell as a parameter that can come with selection biases. Rather, interactions between cells vary in strength across a range of spatial scales that single-radius choice may miss. To fill this gap we developed multiScaleAC to extended Morans I, a correlation measure that accounts for locations of values, by employing a Gaussian kernel applied to locations and varying the bandwidth parameter h. The resulting Morans I(h) then can be compared between samples using functional data analysis. In the current study, we used simulations to show that our framework has well controlled Type I error due to the use of permutations for assessing significant interactions. We also demonstrate that multiScaleAC has high statistical power to identify a significant interaction when a true interaction is simulated (1.00 at bandwidths greater than 5) and increasing power as bandwidth increases when negative interaction is simulated. We found multiScaleAC largely captures similar significant ligand-receptor profiles in 8 Visium samples of colon tissue using the same bandwidth as spatialDM but without removing low-weight spots from the weight matrix (73.7% - 87.2%). Applying the multiScaleAC framework to our previous single-cell spatial transcriptomics data (COL4A1-ITGAV in the stromal compartment of clear cell renal cell carcinoma) followed by functional principal component analysis, we found functional principal component 1 to represent global interaction elevation/depression. Associating functional principal component 1 scores with immunotherapy exposure showed significantly higher scores in stromal tissues exposed to immunotherapy than those naive to immunotherapy, indicating an overall higher interaction of cell expressing COL4A1-ITGAV. These findings recapitulate our previous study while reducing bias in neighbor selections. We believe this is the first study to apply a functional extension of Morans I in combination with functional data analysis to understand cell-cell interaction over spatial scales.

bioinformatics↗

bioRxiv2026

Functional Data Analysis of Spatial Clustering Identifies Prognostic T Cell Patterns in Ovarian Cancer

Spatial proteomic imaging technologies enable the simultaneous assessment of immune cell abundance and spatial organization within the tumor microenvironment. Spatial clustering is commonly summarized using measures such as Ripleys K or nearestOneighbor G-functions at a fixed radius. However, these approaches depend on scale selection and may obscure biologically relevant patterns occurring across spatial ranges. We propose a functional data analysis (FDA) framework to model spatial clustering trajectories derived across a continuum of radii. Functional principal component analysis (FPCA) was used to summarize dominant modes of spatial variation, and resulting scores were incorporated into Cox proportional hazards models as both main effects and interaction with immune cell abundance. The approach was applied to multiplex immunofluorescence data from five ovarian cancer studies, comprising 773 highOgrade ovarian serous tumors. Analyses focused on CD3+ and CD8+ T cell populations within the tumor compartment of the tissue, adjusting for age at diagnosis and cancer stage, with study-specific estimates combined using random-effects meta-analysis. Higher abundance of both T cells and CD8+ T cells was consistently associated with improved overall survival. Beyond abundance, spatial features captured by the leading functional principal component were independently associated with survival, particularly for CD8+ T cells. Interaction models further showed that the prognostic effect of immune infiltration depended on spatial clustering, with tumors characterized by high abundance and low spatial clustering exhibiting the most favorable outcomes. These findings indicate that spatial organization provides complementary prognostic information beyond abundance alone and suggests that more diffuse immune infiltration may reflect more effective anti-tumor activity in ovarian cancer. Overall, FDA offers a flexible and interpretable framework for modeling spatial clustering across scales and identifying prognostic spatial features not captured by fixed-radius or distance analyses.

cancer biology↗

bioRxiv2025

Targeting Impaired Type I Interferon IL-27 Signaling Rescues T Regulatory Cell Suppressive Function in Relapsing-Remitting Multiple Sclerosis

T Regulatory T cells (Tregs) from patients with relapsing-remitting multiple sclerosis (RRMS) exhibit impaired suppressive function, yet the underlying molecular mechanisms remain elusive. Single-cell RNA sequencing (scRNAseq) of ex vivo-sorted Tregs from RRMS patients and matched healthy controls (HCs) revealed down-regulation of type I IFN (IFN) and IL-27 signaling pathways in RRMS Tregs. These Tregs showed reduced expression of IFN-stimulated genes (ISGs) (ISG15, MX1, IFITM1, IFI44L, OAS1), as well as key mediators of Treg suppressive function (LGALS3, CD81, FCRL3, CD7, CSTB), all suggesting a key role of decreased IFN signaling in RRMS Treg dysfunction. To therapeutically target IFN signaling pathways and improve Treg suppressive functions, we used cGAMP-loaded microparticles (MPs) to activate the stimulator of IFN genes (STING) in experimental autoimmune encephalomyelitis (EAE). cGAMP-MP treatment ameliorated EAE via induction of Tregs expressing IL-27R, IL-10, TGF-b, and Granzyme B. This effect was abolished in Treg-specific IL-27R (Treg{Delta}Il27ra) knockout mice, confirming that IL-27 signaling is essential for Treg suppression. In vitro IL-27 stimulation of RRMS-derived Tregs restored expression of IFN pathway genes (IRF1, IFNGR, IFI16) and Treg suppressive genes (ICOS, IKZF3, IL7R, TIGIT). Thus, we propose that IL-27 pre-stimulation may restore their suppressive function and migration (via CCR6, CCR7, S100A11 and S1PR4) to the central nervous system (CNS) in future clinical trials. SignificanceSeveral studies have reported a role for type I IFN and IL-27 signaling in the induction of suppressive Tregs in autoimmune diseases. We report that RRMS Tregs have decreased expression of type I IFN and IL-27 signalling-related genes in comparison to HCs. The animal model of MS (EAE) was successfully treated with cGAMP-MPs, which, via induction of type I IFN, IL-27 and IL-10, restored Treg suppressive function. A scRNAseq study of Tregs from MS patients revealed that IL-27 in vitro stimulation normalized the expression of type I IFN genes and Treg suppressive genes. We propose that IL-27 pre-treatment may enhance Treg suppressive function and migration to the CNS in future clinical trials.

immunology↗

bioRxiv2025

Exact Expectation of Complete Spatial Randomness for Nearest Neighbor G(r): A Scalable Alternative to Permutations

Spatial analysis is becoming increasingly important for studies, from epidemiology to tissue biology, as technologies advance and experimental costs decrease. However, the widespread use of spatial metrics such as Nearest Neighbor G(r) is affected by the fact that biological systems rarely satisfy the assumption of stationarity, which is required to appropriately use theoretical complete spatial randomness (CSR) measures. As a result researchers often use computationally expensive permutations to empirically estimate CSR for subsets of points or cells. Here, we present closed form analytical solutions for both the mean and variance of the sample-specific CSR for Nearest Neighbor G(r) to allow for fast and reproducible calculation without permutations. Using a multiplex immunofluorescence sample of clear cell renal cell carcinoma, we show that the theoretical G(r) for cytotoxic T cells overestimates CSR at low radii (due to spatial constraints between cells) while drastically underestimating CSR at radii between 20 and 90 pixels. In a simulated sample of 30 points, our analytical solution for the mean is identical to the average of G(r) measured on all 142,506 unique combinations of 5 marked (or positive) points. On the real clear cell renal cell carcinoma sample, our exact CSR is similar in speed to estimating CSR with 1000 permutations while our optimized Rcpp implementation is [~]30x faster and consuming [~]20x less memory than 1000 permutations. This permutation-free approach dramatically enhances computational efficiency and reproducibility, enabling scalable and reproducible analysis for studies in epidemiology, multiplex immunofluorescence, spatial transcriptomics, and related fields where accurate, sample-specific null expectations are important for comparisons.

bioinformatics↗

bioRxiv2025

Olfactory receptor 2 drives abdominal aortic aneurysm by promoting CX3CR1-mediated monocyte recruitment

BackgroundAbdominal aortic aneurysms (AAA) are characterized by an intricate interplay of extracellular matrix degradation and inflammation. Macrophages are centrally involved in these processes. The mechanisms underlying macrophage activation in AAA remain incompletely understood. Vascular macrophages have been shown to express olfactory receptor 2 (Olfr2), a G-protein coupled receptor involved in mediating the sense of smelling and regulating inflammatory activity in macrophages. Whether Olfr2 plays a role in modulating macrophage responses in AAA formation remains unknown. Methods & ResultsIn silico micro-array analysis showed increased expression of the human Olfr2 orthologue OR6A2 in AAA tissue compared to healthy aorta. Flow cytometric analysis revealed increased expression of OR6A2 on classical, non-classical, and intermediate monocytes of patients with a large AAA (> 5cm) in comparison to patients with smaller AAA (< 5cm). Up to 30% of vascular macrophages expressed OR6A2 in human AAA and Olfr2 in mouse AAA tissue. Olfr2 expression peaked on major histocompatibility complex II-high (MHCIIhigh) and C-C chemokine receptor type 2-low (CCR2low) aortic monocytes and macrophages on day 7 following experimental AAA initiation and decreased to baseline expression on day 28. Olfr2 gene (Olfr2-/-) deficiency protected mice from AAA formation, which was accompanied by lowered ECM degradation, reduced macrophage infiltration and increased smooth muscle cell content. Conversely, treatment with the Olfr2 agonist octanal exacerbated AAA formation and inflammation, while the antagonist citral reduced AAA formation in comparison to vehicle treated mice. Bulk transcriptome analysis of aortic tissue revealed reduced inflammatory gene expression in Olfr2-/- mice at day 7 following AAA initiation. Spectral flow cytometry resolved 20 aortic immune cell populations, which were largely reduced in quantity by Olfr2-deficiency at day 7 and day 28 post experimental AAA formation, while circulating leukocyte counts and monocyte subset distribution were not altered between Olfr2+/+ and Olfr2-/- mice. Circulating Ly6Chigh-monocytes exhibited reduced expression of the CX3C motif chemokine receptor 1 (CX3CR1) and CCR2 during AAA formation. Transcriptional analysis of monocytes confirmed downregulation of pathways associated with cell adhesion, motility and migration. In vitro, Olfr2-/- monocytes showed impaired migration towards the CX3CR1 ligand CX3CL1. Competitive transfer of Olfr2+/+ and Olfr2-/- monocytes confirmed reduced migratory capacity of Olfr2-/- monocytes into the developing AAA. ConclusionWe demonstrate a critical relevance for Olfr2 in the modulation of the inflammatory response underlying AAA, which is mediated by enhanced monocyte recruitment.

immunology↗

bioRxiv2024

Targeting G9a translational mechanism of SARS-CoV-2 pathogenesis for multifaceted therapeutics of COVID-19 and its sequalae

By largely unknown mechanism(s), SARS-CoV-2 hijacks the host translation apparatus to promote COVID-19 pathogenesis. We report that the histone methyltransferase G9a noncanonically regulates viral hijacking of the translation machinery to bring about COVID-19 symptoms of hyperinflammation, lymphopenia, and blood coagulation. Chemoproteomic analysis of COVID-19 patient peripheral mononuclear blood cells (PBMC) identified enhanced interactions between SARS-CoV-2-upregulated G9a and distinct translation regulators, particularly the N6-methyladenosine (m6A) RNA methylase METTL3. These interactions with translation regulators implicated G9a in translational regulation of COVID-19. Inhibition of G9a activity suppressed SARS-CoV-2 replication in human alveolar epithelial cells. Accordingly, multi-omics analysis of the same alveolar cells identified SARS-CoV-2-induced changes at the transcriptional, m6A-epitranscriptional, translational, and post-translational (phosphorylation or secretion) levels that were reversed by inhibitor treatment. As suggested by the aforesaid chemoproteomic analysis, these multi-omics-correlated changes revealed a G9a-regulated translational mechanism of COVID-19 pathogenesis in which G9a directs translation of viral and host proteins associated with SARS-CoV-2 replication and with dysregulation of host response. Comparison of proteomic analyses of G9a inhibitor-treated, SARS-CoV-2 infected cells, or ex vivo culture of patient PBMCs, with COVID-19 patient data revealed that G9a inhibition reversed the patient proteomic landscape that correlated with COVID-19 pathology/symptoms. These data also indicated that the G9a-regulated, inhibitor-reversed, translational mechanism outperformed G9a-transcriptional suppression to ultimately determine COVID-19 pathogenesis and to define the inhibitor action, from which biomarkers of serve symptom vulnerability were mechanistically derived. This cell line-to-patient conservation of G9a-translated, COVID-19 proteome suggests that G9a inhibitors can be used to treat patients with COVID-19, particularly patients with long-lasting COVID-19 sequelae.

immunology↗

bioRxiv2024

scSpatialSIM: a simulator of spatial single-cell molecular data

BackgroundSpatial molecular data is increasingly being generated in biological tissue studies to increase our understanding of cell infiltration and spatial architecture of tissues. Examples of technologies used to study the spatial contexture of tissues are single-cell protein expression assays and spatial transcriptomics. The increased use of spatial biology technologies has also resulted in an increase in the development of statistical methods to describe the spatial landscape in tissues. Due to the lack of consensus on "gold standard" statistical approaches for assessing the spatial contexture of tissues, we created an R package, scSpatialSIM, to assess different statistical and bioinformatic methods. scSpatialSIM allows users to simulate single-cell molecular data to mimic real tissues at scale, clustering of cell types, and co-clustering / co-localization of two or more cell types. scSpatialSIM also contains functions that give users the ability to simulate quantitative distributions for positive and negative cells (e.g., gene expression, fluorescence intensity). ResultsWe demonstrate that scSpatialSIM allows users to easily simulate various kernel densities of probability distributions used to create the marked point pattern - points distributed in space with either numeric or categorical features. Using scSpatialSIM, we used four univariate spatial simulation scenarios to compare three different measures for spatial clustering (Ripleys K(r), nearest neighbor G(r), and pair correlation g(r)). We found that Ripleys K(r) identifies the most radii with significant clustering in all four scenarios. Nearest neighbor G(r) only identified all samples as significantly clustered at one radius (r = 0.07) in one simulation scenario (high abundance large cluster size). Pair correlation g(r) was better able to detect significant clustering at low radii when abundance was low. ConclusionsVignettes developed for scSpatialSIM cover the creation of single-type and multi-type spatial single-cell molecular data, as well as how these simulated data can be used with other R packages, such as spatialTIME, to derive spatial statistics. Development of this package is crucial for furthering our understanding of the power of existing methods and the development of novel applications to assess the spatial contexture of tissues by providing an objective platform for simulating spatial single-cell molecular data.

cell biology↗

bioRxiv2023

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

MotivationMultiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. ResultsTo address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. Availability and ImplementationThe R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR. ContactPlease address correspondence to jiangmei.xiong@vanderbilt.edu and simon.vandekar@vumc.org. Key PointsO_LIGammaGateR is the first semi-automated marker gating tool for mIF image, and it will help to diminish the inconsistency with manual marker gating. C_LIO_LIWith novel cfGMM, GammaGateR can fit flexibly across slides with different distributions and incorporate biology priors. C_LIO_LIGammaGateR is proven to reveal credible prognostic information, and it can quantify known findings in tumor cell populations. C_LI

bioinformatics↗

bioRxiv2023

Regression and Alignment for Functional Data and Network Topology

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales--from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

neuroscience↗

bioRxiv2023

Neural Signatures of Emotion Regulation

Emotional experience is central to a fulfilling life. Although exposure to negative experiences is inevitable, an individuals emotion regulation response may buffer against psychopathology. Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences. Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response. We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature. We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.5% across three classes. Our results suggest that emotional regulation produces a unique signature that is differentiable from passive viewing of negative imagery.

neuroscience↗

bioRxiv2023

A Video Segmentation Pipeline for Assessing changes in Pupil Response to Light After Cannabis Consumption

Due to long-standing federal restrictions on cannabis-related research, the implications of cannabis legalization on traffic and occupational safety are understudied. Accordingly, there is a need for objective and validated measures of acute cannabis impairment that may be applied in public safety and occupational settings. Pupillary response to light may offer an avenue for detection that outperforms typical sobriety tests and THC concentrations. We developed a video processing and analysis pipeline that extracts pupil sizes during a light stimulus test administered with goggles utilizing infrared videography. The analysis compared pupil size trajectories in response to a light for those with occasional, daily, and no cannabis use before and after smoking. Pupils were segmented using a combination of image pre-processing techniques and segmentation algorithms which were validated using manually segmented data and found to achieve 99% precision and 94% F-score. Features extracted from the pupil size trajectories captured pupil constriction and rebound dilation and were analyzed using generalized estimating equations. We find that acute cannabis use results in less pupil constriction and slower pupil rebound dilation in the light stimulus test.

bioinformatics↗

bioRxiv2022

A Platform-Independent Framework for Phenotyping of Multiplex Tissue Imaging Data

Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-accurate representations of the input data, but exhibit signifi-cantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

bioinformatics↗

bioRxiv2022

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular architectures by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering for the spatial contribution. As such, none of the existing approaches has fully accounted for the heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage the spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular architectures. Then, functional principal component analysis (FPCA) targeting sparse data is applied to estimate FPC scores which are then predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, holding other clinical variables constant. Using an ovarian cancer dataset (n = 114) as a case study, we found that the spatial heterogeneity in the TME immune compositions of CD19+ B cells, CD4+ T cells, CD8+ T cells, and CD68+ macrophages, had a significant non-zero effect on the overall survival (p = 0.027). In the simulations studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

bioinformatics↗

bioRxiv2021

SPF: A Spatial and Functional Data Analytic Approach to cell Imaging data

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subjects clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available Multiplexed Ion beam Imaging (MIBI) triple-negative breast cancer dataset.

bioinformatics↗

bioRxiv2021

DenVar: Density-based Variation analysis of multiplex imaging data

Multiplex immunohistochemistry (mIHC) and multiplexed ion beam imaging (MIBI) platforms have become increasingly popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions, often known as functional markers, in the TME becomes extremely crucial for subject-specific assessment of risks, such as risk of recurrence and risk of death. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular functional marker, and the other to classify the subjects based on the proportion of the positive cells. The selection of the thresholds has a large impact on the results and an arbitrary selection can lead to an incomprehensible conclusion. In light of this problem, we present a threshold-free distance between the subjects based on the probability densities of the functional markers. The distance can be used to classify the subjects into meaningful groups or can be used in a linear mixed model setup for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling an easier but interpretable analysis of these types of data. With the proposed method, we analyze a lung cancer dataset from an mIHC platform, finding the difference in the density of functional marker HLA-DR to be significantly associated with the overall survival. The approach is also applied on an MIBI triple-negative breast cancer dataset to analyze effects of multiple functional markers. Finally, we demonstrate the reliability of our method through extensive simulation studies.

bioinformatics↗

bioRxiv2021

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

MotivationThe multiplexed imaging domain is a nascent single-cell analysis field with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available. ResultsWe implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data, and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Further, we find that dividing this data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in the multiplexed domain. Availability and ImplementationSource code is provided at https://github.com/statimagcoll/MultiplexedNormalization. Contactcoleman.r.harris@vanderbilt.edu Supplementary informationSupplementary information is available online.

bioinformatics↗

bioRxiv2021

TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation

We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates, and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared to other methods. Application to two datasets identified HIV-associated Interferon pathways in xenograft mice and pathways associated with Alzheimers disease progression in humans.

genomics↗

bioRxiv2020

Scale-invariant time registration of 24-hour accelerometric rest-activity profiles and its application to human chronotypes

By collecting data continuously over 24 hours, accelerometers and other wearable devices can provide novel insights into circadian rhythms and their relationship to human health. Existing approaches for analyzing diurnal patterns using these data, including the cosinor model and functional principal components analysis, have revealed and quantified population-level diurnal patterns, but considerable subject-level variability remained uncaptured in features such as wake/sleep times and activity intensity. This remaining informative variability could provide a better understanding of chronotypes, or behavioral manifestations of ones underlying 24-hour rhythm. Curve registration, or alignment, is a technique in functional data analysis that separates "vertical" variability in activity intensity from "horizontal" variability in time-dependent markers like wake and sleep times; this data-driven approach is well-suited to studying chronotypes using accelerometer data. We develop a parametric registration framework for 24-hour accelerometric rest-activity profiles represented as dichotomized into epoch-level states of activity or rest. Specifically, we estimate subject-specific piecewise linear time-warping functions parametrized with a small set of parameters. We apply this method to data from the Baltimore Longitudinal Study of Aging and illustrate how estimated parameters give a more flexible quantification of chronotypes compared to traditional approaches.

physiology↗