Biology subjects

Irizarry, R.

Publications and source records attributed to Irizarry, R..

5 recordsLinked to original sources

bioRxiv2026

A statistical framework for system-level synthesis

Simultaneous profiling of thousands of biological features is now routine. Yet transforming the data into interpretable models of system behavior remains a fundamental bottleneck as current analyses generally identify feature-wise associations or extract latent structure without anchoring it to the variables that define the scientific question. Here we propose a framework for system-level synthesis that learns and predicts overarching processes, and reliably identifies participating features without data reuse. We develop Smoothing Spline Analysis of Variance-Singular Value Decomposition to learn overarching processes as smooth, flexible functions of nominal, ordinal and continuous covariates and their interactions. This unlocks prediction from covariates alone, while reserving measurements for conditional inference of feature participation. In applications, the framework recovered established biology while generating predictions and testable hypotheses. By linking high-dimensional variation to covariates, the framework advances analysis beyond feature-wise associations and unsupervised patterns, toward interpretable system-level models that vary with biological and experimental context.

systems biology↗

bioRxiv2026

Spurious correlation inflates performance in single-cell perturbation prediction

The increasing number of computational methods designed to predict the effects of genetic perturbations on cellular gene expression profiles has led to a need for rigorous evaluation metrics. Recent benchmarking studies rely on correlation or cosine similarity of differential expression relative to a shared population of control cells. We show that these metrics are systematically inflated by statistical bias induced by reusing the same control population to define both quantities being compared. As a result, even non-informative methods can appear to perform well, particularly in datasets with limited numbers of control cells. Reanalysis of published datasets using a simple control-splitting procedure that removes this bias leads to a substantial reduction in performance previously attributed to biological signal.

bioinformatics↗

bioRxiv2018

A gene expression atlas of embryonic neurogenesis in Drosophila reveals complex spatiotemporal regulation of lncRNAs.

Cell type specification during early nervous system development in Drosophila melanogaster requires precise regulation of gene expression in time and space. Resolving the programs driving neurogenesis has been a major challenge owing to the complexity and rapidity with which distinct cell populations arise. To resolve the cell type-specific gene expression dynamics in early nervous system development, we have sequenced the transcriptomes of purified neurogenic cell types across consecutive time points covering critical events in neurogenesis. The resulting gene expression atlas comprises a detailed resource of global transcriptome dynamics that permits systematic analysis of how cells in the nervous system acquire distinct fates. We resolve known gene expression dynamics and uncover novel expression signatures for hundreds of genes among diverse neurogenic cell types, most of which remain unstudied. We also identified a set of conserved and processed long-noncoding RNAs (lncRNAs) that exhibit spatiotemporal expression during neurogenesis with exquisite specificity. LncRNA expression is highly dynamic and demarcates specific subpopulations within neurogenic cell types. Our spatiotemporal transcriptome atlas provides a comprehensive resource to investigate the function of coding genes and noncoding RNAs during critical stages of early neurogenesis.\n\nSummary statementWe present a spatiotemporal transcriptome during early Drosophila embryonic nervous system development, revealing a complex cell type-specific network of mRNAs and IncRNAs.

developmental biology↗

bioRxiv2017

Challenges And Emerging Directions In Single-Cell Analysis

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances, and look to future opportunities.

genomics↗

bioRxiv2017

Correcting For Cell-Type Heterogeneity In Epigenome-Wide Association Studies: Premature Analyses And Conclusions

Recently, a study by Rahmani et al [1] claimed that a reference-free cell-type deconvolution method, called ReFACTor, leads to improved power and improved estimates of cell-type composition compared to competing reference-free and reference-based methods in the context of Epigenome-Wide Association Studies (EWAS). However, we identified many critical flaws (both conceptual and statistical in nature), which seriously question the validity of their claims. We outlined constructive criticism in a recent correspondence letter, Zheng et al [2]. The purpose of this letter is two-fold. First, to present additional analyses, which demonstrate that our original criticism is statistically sound. Second, to highlight additional serious concerns, which Rahmani et al have not yet addressed. In summary, we find that ReFACTor has not been demonstrated to outperform state-of-the-art reference-free methods such as SVA or RefFreeEWAS, nor state-of-the-art reference-based methods. Thus, the claim by Rahmani et al (a claim reiterated in their recent response letter [3]) that ReFACT or represents an advance over the state-of-the-art is not supported by an objective and rigorous statistical analysis of the data.

bioinformatics↗