Biology subjects

Clauset, A.

Publications and source records attributed to Clauset, A..

11 recordsLinked to original sources

bioRxiv2026

Cross-assay RNA modeling reveals cancer biomarkers

The clinical heterogeneity of cancer poses a major challenge for precision medicine. Limited cohort sizes across evolving assay platforms impede reliable biomarker discovery. Here, we systematically evaluate how to integrate data from four transcriptomics platforms: bulk and single-cell (sc) RNA sequencing (RNA-seq), NanoString, and microarray for predictive modeling in cancer. We use high-grade serous carcinoma (HGSC) of tube-ovarian origin as a model system, as it is highly heterogeneous in both biology and assay data. We find that using fold-change of gene expression in patients with matched pre- and post-neoadjuvant chemotherapy samples reduces inter-patient and inter-assay variability but is insufficient to overcome platform-specific biases. Microarray and scRNA-seq data exhibit systematic biases, while RNA-seq and NanoString show the most promise for combination into a single training cohort. To mitigate inter-assay limitations, we generate a new data set of HGSC tumor samples profiled with both RNA-seq and NanoString, and use it to identify the limits of detection and optimal harmonization strategies. Our approaches enable integration of cohorts for separate and combined RNA-seq and NanoString predictive models of disease recurrence (test-set AUROCs > 0.8), validated in external microarray cohorts. We leverage single-cell and bulk RNA-seq network-based analyses to provide mechanistic context for genes in the predictive models. Our models indicate that GBP4 expression is a key predictor of recurrence and marks immune remodeling towards cytotoxicity. We provide an interactive web portal to facilitate exploration of data and results. These findings guide cross-assay harmonization of transcriptomic data and enable improved predictive modeling in heterogeneous cancers. Statement of SignificanceWe present a framework for integrating RNA-seq, NanoString, microarray, and single-cell transcriptomic data for predictive modeling, enabling robust biomarker discovery in heterogeneous cancers and identifying GBP4 as a marker of immune remodeling.

bioinformatics↗

bioRxiv2026

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data

In the treatment of high grade serous ovarian cancer (HGSC), patients initially diagnosed with unresectable tumors are first treated with neoadjuvant chemotherapy (NACT) to reduce tumor burden prior to surgery. Analysis of matched pre- and post-NACT samples from the same patients enables the investigation of chemotherapy impacts and the biomarkers of progression. Although the tumor immune microenvironment (TIME) has increasingly been recognized as critical in shaping the development and progression of HGSC, we lack a comprehensive understanding of how chemotherapy remodels the TIME. Previous studies have found evidence for a general inflammatory response post-NACT, despite inconsistencies regarding which differentially expressed genes and pathways are implicated. We combine matched NanoString gene expression data from multiple sources to create a large dataset of matched pre- and post- NACT samples (N=83, with 29 novel to this study) and investigate reproducibility. Further, we use machine learning methods to investigate whether patient progression-free survival (PFS) can be predicted from the observed impact of chemotherapy on the TIME as represented by the comprehensive set of NanoString features. We find overall low predictability of PFS from all NanoString features, suggesting that previous results may have been limited by small sample size effects and that larger datasets are needed to identify more generalizable and translatable findings. We identify a set of differential expression features that are the most important for predicting patient outcomes that can be validated in future computational and biological studies. Author summaryA subset of patients with high grade serous ovarian cancer are treated with chemotherapy before surgery to reduce tumor burden. We investigate a large dataset of samples taken before and after chemotherapy. These matched samples enable an investigation of how the environment around tumors, for example immune cell infiltration, reacts to chemotherapy, providing insights into biomarkers for treatment response and treatments that could complement chemotherapy. This larger dataset only partially replicates results from previous studies, while also providing new insights. Machine learning models designed to predict the time to patient recurrence from available biomarkers indicate that they are not strongly predictive of patient outcomes, in contrast to past studies. These results suggest that larger datasets are needed. We identify a set of genes that change with chemotherapy and are indicative of and potentially useful for predicting time to disease recurrence and can be further investigated.

cancer biology↗

bioRxiv2026

Understanding and addressing under-sampling in plant-pollinator networks using stacked models for missing link prediction

Pollinator diversity and pollination are under threat from anthropogenic disturbances. Plant-pollinator networks are useful tools to study the consequences of these disturbances, but their links are often under-sampled, potentially biasing conclusions. We introduce a computational method that can be used to predict which unobserved links in a plant-pollinator network dataset are missing. This method extends a state-of-the-art machine learning approach to missing link prediction based on stacked generalization, which has been promising in other contexts, to the setting of bipartite plant-pollinator networks with species traits treated as node attributes. This approach predicts missing links using an ensemble of predictors based on both observed network topology and species traits. We demonstrate this approach on synthetic and empirical plant-pollinator networks. We take advantage of a unique empirical plant-pollinator dataset which samples from three sites using (a) records of observed visits of pollinators to plant species and (b) pollen analysis with detailed species trait annotations for both plant and pollinator nodes. Visit-based sampling under-samples the links in these networks, whereas pollen-based sampling identifies additional links, allowing us to investigate missing link prediction from realistic patterns of field under-sampling. We show that full plant-pollinator networks can be partially reconstructed from the under-sampled networks using our approach. Further, we show that this method can be used to identify which features are the most important for predicting missing links, which allows for investigation into mechanisms driving plant-pollinator link formation and observation. This method is broadly applicable to under-sampled plant-pollinator networks.

ecology↗

bioRxiv2024

Predicting missing links in food webs using stacked models and species traits

Networks are a powerful way to represent the complexity of complex ecological systems. However, most ecological networks are incompletely observed, e.g., food webs typically contain only partial lists of species interactions. Computational methods for inferring such missing links from observed networks can facilitate field work and investigations of the ecological processes that shape food webs. Here, we describe a stacked generalization approach to predicting missing links in food webs that can learn to optimally combine both structural and trait-based predictions, while accounting for link direction and ecological assumptions. Tests of this method on synthetic food webs show that it performs very well on networks with strong group structure, strong trait structure, and various combinations thereof. Applied to a global database of 290 food webs, the method often achieves near-perfect performance for missing link prediction, and performs better when it can exploit both species traits and patterns in connectivity. Furthermore, we find that link predictability varies with ecosystem type, correlates with certain network characteristics like size, and is principally driven by a subset of ecologically-interpretable predictors. These results indicate broad applicability of stacked generalization for studying ecological interactions and understanding the processes that drive link formation in food webs.

ecology↗

bioRxiv2024

STIX: Long-reads based Accurate Structural Variation Annotation at Population Scale

The prioritization of Structural Variants (SV), which is needed to rank and identify potential pathogenic alleles, is still in its infancy. This is exemplified over gnomAD only being able to annotate 33.5% of GIAB SVs. To overcome this, we present the first long-read based annotation resource for both GRCh38 and CHM13-T2T reference using STIX. In contrast to previous methods, STIX indexes SV-informative long-reads themselves, can thus be easily extended and accurately annotate all SV types including insertions. STIX successfully annotated 95.9% of GIAB Tier1 SVs. STIX further improved cancer based SV prioritization by highlighting 3,563 SV from COSMIC being common in the population. We further showcase that mosaic SV can be independently gained and may be widely spread throughout the population. This highlights the need for accurate SV population frequency annotation to further facilitate the adoption of SV via long-read sequencing in medical research and clinical applications.

bioinformatics↗

bioRxiv2024

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors -- one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled -- and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

cancer biology↗

bioRxiv2023

Gendered hiring and attrition on the path to parity for academic faculty

Despite long-running efforts to increase gender diversity among tenured and tenure track faculty in the U.S., women remain underrepresented in most academic fields, sometimes dramatically so. Here we quantify the relative importance of faculty hiring and faculty attrition for both past and future faculty gender diversity using comprehensive data on the training and employment of 268,769 tenured and tenure-track faculty rostered at 12,112 U.S. PhD-granting departments, spanning 111 academic fields between 2011-2020. Over this time, we find that hiring had a far greater impact on womens representation among faculty than attrition in the majority (90.1%) of academic fields, even as academia loses a higher share of women faculty relative to men at every career stage. Finally, we model the impact of five specific policy interventions on womens representation, and project that eliminating attrition differences between women and men only leads to a marginal increase in womens overall representation--in most fields, successful interventions will need to make substantial and sustained changes to hiring in order to reach gender parity.

scientific communication and education↗

bioRxiv2021

Optimizing Polymerase Chain Reaction (PCR) using Machine Learning

Despite substantial standardization, polymerase chain reaction (PCR) experiments frequently fail. Troubleshooting failed PCRs can be costly in both time and money. Using a crowdsourced data set spanning 290 real PCRs from six active research laboratories, we investigate the degree to which PCR success rates can be improved by machine learning. While human designed PCRs succeed at a rate of 55-63%, we find that a machine learning model can accurately predict reaction outcome 81% of the time. We validate this level of improvement by then using the model to guide the design and predict the outcome of 39 new PCR experiments. In addition to improving outcomes, the model identifies 15 features of PCRs that researchers did not optimize well compared to the learned model. These results suggest that PCR success rates can easily be improved by 17-26%, potentially saving millions of dollars and thousands of hours of researcher time each year across the scientific community. Other common laboratory methods may benefit from similar data-driven optimization effort.

bioinformatics↗

bioRxiv2020

Denoising large-scale biological data using network filters

Large-scale biological data sets, e.g., transcriptomic, proteomic, or ecological, are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation. Here we describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or "filtered" to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 58% compared to using unfiltered data. These results indicate the broad potential utility of network-based filters to applications in systems biology. Author SummarySystem-wide measurements of many biological signals, whether derived from molecules, cells, or entire organisms, are often noisy. Removing or mitigating this noise prior to analysis can improve our understanding and predictions of biological phenomena. We describe a general way to denoise biological data that can account for both correlation and anti-correlation between different measurements. These "network filters" take as input a set of biological measurements, e.g., metabolite concentration, animal traits, neuron activity, or gene expression, and a network of how those measurements are biologically related, e.g., a metabolic network, food web, brain connectome, or protein-protein interaction network. Measurements are then "filtered" for correlated or anti-correlated noise using a set of other measurements that are identified using the network. We investigate the accuracy of these filters in synthetic and real-world data sets, and find that they can substantially reduce noise of different levels and structure. By denoising large-scale biological data sets, network filters have the potential to improve the analysis of many types of biological data.

systems biology↗

bioRxiv2016

The Evolution of Primate Body Size: Left-skewness, Maximum Size, and Cope’s Rule

Body size is a key physiological, ecological, and evolutionary characteristic of species. Within most major clades, body size distributions follow a right-skewed pattern where most species are relatively small while a few are orders of magnitude larger than the median size. Using a novel database of 742 extant and extinct primate species sizes over the past 66 million years, we find that primates exhibit the opposite pattern: a left-skewed distribution. We investigate the long-term evolution of this distribution, first showing that the initial size radiation is consistent with plesiadapiformes (an extinct group with an uncertain ancestral relationship to primates) being ancestral to modern primates. We calculate the strength of Copes Rule, showing an initial tendency for descendants to increase in size relative to ancestors until the trend reverses 40 million years ago. We explore when the primate size distribution becomes left-skewed and study correlations between body size patterns and climactic trends, showing that across Old and New World radiations the body size distribution initially exhibits a right-skewed pattern. Left-skewness emerged early in Old World primates in a manner consistent with a previously unidentified possible maximum body size, which may be mechanistically related to primates encephalization and complex social groups.

evolutionary biology↗

bioRxiv2016

Revisiting the effect of red on competition in humans

Bright red coloration is a signal of male competitive ability in animal species across a range of taxa, including non-human primates. Does the effect of red on competition extend to humans? A landmark study in evolutionary psychology established such an effect through analysis of data for four combat sports at the 2004 Athens Olympics [1]. Here we show that the results do not replicate in an equivalent, independent dataset for the 2008 Beijing Olympics, and that there is substantial variation in the fraction of wins by red across sports in both years. We uncover a number of shortcomings with the research design, analysis, and interpretation underlying the original results. For example, the variation observed in the data may reflect bias towards wins by one color over the other, linked to specific features of the tournament structure for the sports analysed. Reanalysis of the data to address these shortcomings indicates that there is no evidence for an effect of red on the outcomes of Olympic combat sports. Our results refute past claims based on analysis of this system, challenging the related notion that any effect of red in human competition is an evolved response shaped by sexual selection.

evolutionary biology↗