Biology subjects

Irizarry, R. A.

Publications and source records attributed to Irizarry, R. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Pan-cancer estimation of prognostic immune biomarkers from histopathology using artificial intelligence

Multiplex immunofluorescence (mIF) assays such as ImmunoProfile provide standardized quantification of prognostic immune markers but are costly and not routinely used in clinical settings. We developed a weakly-supervised AI model, HistoProfile, to estimate intratumoral cell densities of key immune biomarkers (CD8, FOXP3, PD-1, and PD-L1) and the PD-L1 tumor proportion score from routine H&E-stained whole-slide images. Trained on a large pan-cancer cohort with paired H&E and mIF data, HistoProfile discriminated biomarker-high from biomarker-low tumors in held-out samples and tumor-specific subgroups (AUROCs of 0.78-0.89 across biomarkers). Critically, the estimated biomarkers preserved prognostic stratification in downstream survival analyses, and showed cancer-specific survival associations across TCGA cohorts where mIF is unavailable. These findings support scalable H&E-based estimation of key immune biomarker measurements for large-scale immune profiling and risk stratification.

bioinformatics↗

bioRxiv2025

Unique molecular identifiers don't need to be unique: a collision-aware estimator for RNA-seq quantification

RNA-sequencing (RNA-seq) relies on Unique Molecular Identifiers (UMIs) to accurately quantify gene expression after PCR amplification. Longer UMIs minimize collisions--where two distinct transcripts are assigned the same UMI--at the expense of increased sequencing and synthesis costs. However, it is not clear how long UMIs need to be in practice, especially given the nonuniformity of the empirical UMI distribution. In this work, we develop a method-of-moments estimator that accounts for UMI collisions, accurately quantifying gene expression and preserving downstream biological insights. We show that UMIs need not be unique: shorter UMIs can be used with a more sophisticated estimator.

bioinformatics↗

bioRxiv2025

An image-based transcriptomics atlas reveals the regional and microbiota-dependent molecular, cellular, and spatial structure of the murine gut

The gastrointestinal environment is home to a massive diversity of diet-, host-, and microbiota-derived small molecules, collectively sensed by a remarkable variety of cells. To explore the cellular and spatial organization of sensation, we used MERFISH to profile receptor expression across 2.1 million cells in multiple regions of the murine gut under specific-pathogen-free (SPF) and germ-free (GF) conditions. This atlas revealed expected and novel cell types--including a candidate murine homolog of human BEST4 enterocytes--demonstrated cell-type regional specialization, discovered extensive location-dependent spatial fine-tuning in mucosal cell expression, and suggested cell-type specific mediators of the effects of microbiota-derived small molecules. In addition, this atlas revealed that, aside from immune cell abundance, many aspects of the murine gut are host-intrinsic and modified only modestly in the absence of a microbiota. Collectively, this atlas provides a valuable resource for understanding the cellular and spatial organization underlying small molecule sensation in the gut.

genomics↗

bioRxiv2024

Identifying spatially variable genes by projecting to morphologically relevant curves

Spatial transcriptomics enables high-resolution gene expression measurements while preserving the two-dimensional spatial organization of the biological sample. A common objective in spatial transcriptomics data analysis is to identify spatially variable genes within predefined cell types or regions within the tissue. However, these regions are often implicitly one-dimensional, making standard two-dimensional coordinate-based methods less effective as they overlook the underlying tissue organization. Here we introduce a methodology grounded in spectral graph theory to elucidate a one-dimensional curve that effectively approximates the spatial coordinates of the examined sample. This curve is then used to establish a new coordinate system that reflects tissue morphology. We then develop a generalized additive model (GAM) to estimate spatial patterns which permits the detection of genes with variable expression in the new morphologically relevant coordinate system. Our approach directly models gene counts, thereby eliminating the need for normalization or transformations to satisfy normality assumptions. A second important advantage over existing hypothesis-testing approaches is that our method not only improves performance but also accurately estimates gene expression patterns and precisely pinpoints spatial loci where deviations from constant expression occur. We validate our approach through extensive simulation and by analyzing experimental data from multiple platforms such as Slide-seq and MERFISH. As an example of its ability to enable biological discovery, we demonstrate how our methodology enables the identification of novel interferon-related subpopulations in the mouse mucosa, as well as markers of inflammation-associated fibroblasts in a multi-sample spatial transcriptomic dataset.

bioinformatics↗

bioRxiv2023

DNA binding analysis of rare variants in homeodomains reveals novel homeodomain specificity-determining residues

Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and play important roles in regulating development, body patterning, and cellular differentiation. Here, we analyzed 92 human HD mutants, including disease-associated variants and variants of unknown significance (VUSs), for their effects on DNA binding activity. Many of the variants altered DNA binding affinity and/or specificity. Biochemical analysis and structural modeling identified 14 novel specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibited different effects on DNA binding. Variant effect prediction tools perform moderately well in distinguishing variants with altered binding affinity, but poorly in identifying those with altered specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and development of clinical diagnostics and precision therapies.

genetics↗

bioRxiv2023

Robust identification of perturbed cell types in single-cell RNA-seq data

Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.

bioinformatics↗

bioRxiv2022

Significance Analysis for Clustering with Single-Cell RNA-Sequencing Data

AO_SCPLOWBSTRACTC_SCPLOWUnsupervised clustering of single-cell RNA-sequencing data enables the identification and discovery of distinct cell populations. However, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty. Many popular pipelines use clustering stability methods to assess the algorithms output and decide on the number of clusters. However, we find that by not addressing known sources of variability in a statistically rigorous manner, these analyses lead to overconfidence in the discovery of novel cell-types. We extend a previous method for Gaussian data, Significance of Hierarchical Clustering (SHC), to propose a model-based hypothesis testing approach that incorporates significance analysis into the clustering algorithm and permits statistical evaluation of clusters as distinct cell populations. We also adapt this approach to permit statistical assessment on the clusters reported by any algorithm. We benchmarked our approach on real-world datasets against popular clustering workflows, demonstrating improved performance. To show its practical utility, we applied it to the Human Lung Cell Atlas and an atlas of the mouse cerebellar cortex. We identified several cases of over-clustering, leading to false discoveries, as well as under-clustering, resulting in the failure to identify new subpopulations that our method was able to detect.

bioinformatics↗

bioRxiv2021

Cell type-specific differential expression for spatial transcriptomics

Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed (DE) within specific cell types across tissue context. We show that current approaches cannot learn cell type-specific DE due to changes in cell type composition across space and the fact that measurement units often detect transcripts from more than one cell type. Here, we introduce a statistical method, Cell type-Specific Inference of Differential Expression (C-SIDE), that identifies cell type-specific patterns of differential gene expression while accounting for localization of other cell types. We model spatial transcriptomics gene expression as an additive mixture across cell types of general log-linear cell type-specific expression functions. This approach provides a unified framework for defining and identifying gene expression changes in a wide-range of relevant contexts: changes due to pathology, anatomical regions, physical proximity to specific cell types, and cellular microenvironment. Furthermore, our approach enables statistical inference across multiple samples and replicates when such data is available. We demonstrate, through simulations and validation experiments on Slide-seq and MER-FISH datasets, that our approach accurately identifies cell type-specific differential gene expression and provides valid uncertainty quantification. Lastly, we apply our method to characterize spatially-localized tissue changes in the context of disease. In an Alzheimers mouse model Slide-seq dataset, we identify plaque-dependent patterns of cellular immune activity. We also find a putative interaction between tumor cells and myeloid immune cells in a Slide-seq tumor dataset. We make our C-SIDE method publicly available as part of the open source R package https://github.com/dmcable/spacexr.

genomics↗

bioRxiv2021

Detection of allele-specific expression in spatial transcriptomics with spASE

Allele-specific expression (ASE), or the preferential expression of one allele, can be observed in transcriptomics data from early development throughout the lifespan. However, the prevalence of spatial and cell type-specific ASE variation remains unclear. Spatial transcriptomics technologies permit the study of spatial ASE patterns genome-wide at near-single-cell resolution. However, the data are highly sparse, and confounding between cell type and spatial location present further statistical challenges. Here, we introduce spASE (https://github.com/lulizou/spase), a computational framework for detecting spatial patterns in ASE within and across cell types from spatial transcriptomics data. To tackle the challenge presented by the low signal to noise ratio due to the sparsity of the data, we implement a spatial smoothing approach that greatly improves statistical power. We generated Slide-seqV2 data from the mouse hippocampus and detected ASE in X-chromosome genes, both within and across cell type, validating our ability to recover known ASE patterns. We demonstrate that our method can also identify cell type-specific effects, which we find can explain the majority of the spatial signal for autosomal genes. The findings facilitated by our method provide new insight into the uncharacterized landscape of spatial and cell type-specific ASE in the mouse hippocampus.

genomics↗

bioRxiv2021

Differential richness inference for 16S rRNA marker gene surveys

Individual and environmental health outcomes are frequently linked to changes in the diversity of associated microbial communities. This makes deriving health indicators based on microbiome diversity measures essential. While microbiome data generated using high throughput 16S rRNA marker gene surveys are appealing for this purpose, 16S surveys also generate a plethora of spurious microbial taxa. When this artificial inflation in the observed number of taxa (i.e., richness, a diversity measure) is ignored, we find that changes in the abundance of detected taxa confound current methods for inferring differences in richness. Here we argue that the evidence of our own experiments, theory guided exploratory data analyses and existing literature, support the conclusion that most sub-genus discoveries are spurious artifacts of clustering 16S sequencing reads. We proceed based on this finding to model a 16S surveys systematic patterns of sub-genus taxa generation as a function of genus abundance to derive a robust control for false taxa accumulation. Such controls unlock classical regression approaches for highly flexible differential richness inference at various levels of the surveyed microbial assemblage: from sample groups to specific taxa collections. The proposed methodology for differential richness inference is available through an R package, Prokounter. Package availability: https://github.com/mskb01/prokounter

bioinformatics↗

bioRxiv2020

Vaginal Microbiome Topic Modelling of Laboring Ugandan Women With and Without Fever

The composition of the maternal vaginal microbiome may influence the duration of pregnancy, onset of labor and even neonatal outcomes. Maternal microbiome research in sub Saharan-Africa has focused on non-pregnant and postpartum composition of the vaginal microbiome. We examined the vaginal microbiome composition of 99 laboring Ugandan women using routine microbiology and 16S ribosomal DNA sequencing from two hypervariable regions (V1-V2 and V3-V4), using standard hierarchical methods. We then introduce Grades of Membership (GoM) modeling for the vaginal microbiome, a method often used in the text mining machine learning literature. Leveraging GoM models, we create a basis composed of a small number of microbial topics whose linear combination optimally represents each patient yielding more accurate associations. We identified relationships between defined communities and the presentation or absence of intrapartum fever. Using a random forest model we showed that by including novel microbial topic models we improved upon clinical variables to predict maternal fever. We also show by integrating clinical variables with a microbial topic model into this model found young maternal age, fever report earlier in the current pregnancy, and longer labors, as well as a more diverse, less Lactobacillus dominated microbiome were features of labor associated with intrapartum fever. These results better define relationships between presentation or absence of intrapartum fever, demographics, peripartum course, and vaginal microbial communities, and improve our understanding of the impact of the microbiome on maternal and neonatal infection risk.

genomics↗

bioRxiv2020

Robust decomposition of cell type mixtures in spatial transcriptomics

Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in cell type assignment in Slide-seq data by accurately reproducing known cell type and subtype localization patterns in the cerebellum and hippocampus. We further show the advantages of RCTD by its ability to detect mixtures and identify cell types on an assessment dataset. Finally, we show how RCTDs recovery of cell type localization uniquely enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD has the potential to enable the definition of spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue.

genomics↗

bioRxiv2020

Probabilistic gene expression signatures identify cell-types from single cell RNA-seq data

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA sequencing (scRNA-seq) quantifies gene expression for individual cells in a sample, which allows distinct cell-type populations to be identified and characterized. An important step in many scRNA-seq analysis pipelines is the annotation of cells into known cell-types. While this can be achieved using experimental techniques, such as fluorescence-activated cell sorting, these approaches are impractical for large numbers of cells. This motivates the development of data-driven cell-type annotation methods. We find limitations with current approaches due to the reliance on known marker genes or from overfitting because of systematic differences between studies or batch effects. Here, we present a statistical approach that leverages public datasets to combine information across thousands of genes, uses a latent variable model to define cell-type-specific barcodes and account for batch effect variation, and probabilistically annotates cell-type identity. The barcoding approach also provides a new way to discover marker genes. Using a range of datasets, including those generated to represent imperfect real-world reference data, we demonstrate that our approach substantially outperforms current reference-based methods, in particular when predicting across studies. Our approach also demonstrates that current approaches based on unsupervised clustering lead to false discoveries related to novel cell-types.

genomics↗

bioRxiv2019

Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than alternatives such as census counts. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.

genomics↗

bioRxiv2019

Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model

Single cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero-inflation. Current normalization pro-cedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We pro-pose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform current practice in a downstream clustering assessment using ground-truth datasets.

genomics↗

bioRxiv2018

Interpretable Convolution Methods for Learning Genomic Sequence Motifs

The first-layer filters employed in convolutional neural networks tend to learn, or extract, spatial features from the data. Within their application to genomic sequence data, these learned features are often visualized and interpreted by converting them to sequence logos; an information-based representation of the consensus nucleotide motif. The process to obtain such motifs, however, is done through post-training procedures which often discard the filter weights themselves and instead rely upon finding those sequences maximally correlated with the given filter. Moreover, the filters collectively learn motifs with high redundancy, often simply shifted representations of the same sequence. We propose a schema to learn sequence motifs directly through weight constraints and transformations such that the individual weights comprising the filter are directly interpretable as either position weight matrices (PWMs) or information gain matrices (IGMs). We additionally leverage regularization to encourage learning highly-representative motifs with low inter-filter redundancy. Through learning PWMs and IGMs directly we present preliminary results showcasing how our method is capable of incorporating previously-annotated database motifs along with learning motifs de novo and then outline a pipeline for how these tools may be used jointly in a data application.

bioinformatics↗

bioRxiv2018

Post-Hurricane Vital Statistics Expose Fragility of Puerto Rico’s Health System

ImportanceHurricane Maria made landfall in Puerto Rico on September 20, 2017. As recently as May of this year (2018), the official death count was 64. After a study describing a household survey reported a much higher death count estimate, as well as evidence of population displacement, extensive loss of services, and a prolonged death rate the government released death registry data. These newly released data will permit a better understanding of the effects of this hurricane.\n\nObjectiveProvide a detailed description of the effects on mortality of Hurricane Maria and compare to other hurricanes.\n\nDesignWe fit a statistical model to mortality data that accounts for seasonal and non-hurricane related yearly effects. We then estimated the deviation from the expected death rate as a function of time.\n\nSettingWe fit this model to 1985-2018 Puerto Rico daily data, which includes the dates of hurricanes Hugo, Georges, and Maria, 2015-2018 Florida daily data, which includes the dates of Hurricane Irma, 2002-2004 Louisiana monthly data, which includes the date of Hurricane Katrina, and 2000-2016 New Jersey monthly data, which includes the date of Hurricane Sandy.\n\nResultsWe find a prolonged increase in death rate after Maria and Katrina, lasting at least 207 and 125 days, resulting in excess deaths estimates of 3,400 (95% CI, 3,100-3,700), and 1,800 (95% CI, 1,600-2100) respectively, showing that Maria had a more long term damaging impact. Surprisingly, we also find that in 1998, Georges had a comparable impact to Katrinas with a prolonged increase of 106 days resulting in 1,400 (95% CI, 1,200-1,700) excess deaths. For Hurricane Maria, we find sharp increases in a small number of causes of deaths, including diseases of the circulatory, endocrine and respiratory system, as well as bacterial infections and suicides.\n\nConclusion and RelevanceOur analysis suggests that since at least 1998, Puerto Ricos health system has been in a precarious state. Without a substantial intervention, it appears that if hit with another strong hurricane, Puerto Ricans will suffer the unnecessary death of hundreds of its citizens.\n\nKey PointsQuestion: How does the effect of Hurricane Maria on mortality in Puerto Rico compare to the effect of other hurricanes in Puerto Rico and other United States jurisdictions?\n\nFindings: We estimate about 3,000 excess deaths after Maria, a higher toll than Katrina. Only other comparable effect was after Georges, also in Puerto Rico. For Georges and Maria, we observe a prolonged death rate increase of more than 10% lasting several months. The causes of death that increased after Maria are consistent with a collapsed health system\n\nMeaning: Puerto Ricos health system does not appear to be ready to withstand another strong hurricane.

epidemiology↗

bioRxiv2018

Genome-wide repressive capacity of promoter DNA methylation is revealed through epigenomic manipulation

The scientific community is increasingly embracing open science. This growing commitment to open science should be applauded and encouraged, especially when it occurs voluntarily and prior to peer review. Thanks to other researchers dedication to open science, we have had the privilege of conducting a reanalysis of a landmark experiment published as a preprint with data made available in a public repository. The study in question found that promoter DNA methylation is frequently insufficient to induce transcriptional repression, which appears to contradict a large body of observational studies showing a strong association between DNA methylation and gene expression. This study was the first to evaluate whether forcibly methylating thousands of DNA promoter regions is sufficient to suppress gene expression. The authors data analysis did not find a strong relationship between promoter methylation and transcriptional repression. However, their analyses did not make full use of statistical inference and applied a normalization technique that removes global differences that are representative of the actual biological system. Here we reanalyze the data with an approach that includes statistical inference of differentially methylated regions, as well as a normalization technique that accounts for global expression differences. We find that forced DNA methylation of thousands of promoters overwhelmingly represses gene expression. In addition, we show that complementary epigenetic marks of active transcription are reduced as a result of DNA methylation. Finally, by studying whether these associations are sensitive to the CG density of promoters, we find no substantial differences in the association between promoters with and without a CG island. The code needed to reproduce are analysis is included in the public GitHub repository github.com/kdkorthauer/repressivecapacity.

genomics↗