Biology subjects

Chen, Y. A.

Publications and source records attributed to Chen, Y. A..

6 recordsLinked to original sources

bioRxiv2026

Correcting trend bias from preferential sampling in fishery-dependent indices

Fishery-dependent indices of abundance are often used to inform stock assessments, but fishing effort is generally not randomly distributed. Fishers concentrate sampling in locations expected to be profitable, leading to a form of preferential sampling that can bias model-based indices of abundance. We show that when the strength of preferential sampling is constant over time, it biases only the magnitude of an index, which is absorbed by estimated catchability parameters in most stock assessments. A more interesting case arises when the strength of preferential sampling changes over time; in these cases the index trend can be biased in a way that conventional catchability estimation cannot correct. Using simulations structured on long running trawl survey data from the west coast of the USA for shortspine thornyhead (Sebastolobus alascanus), we evaluate two structurally different methods for correcting time-varying trend bias: a joint spatiotemporal model of the density and sampling processes, and a discrete-choice model of fisher location choice. Our simulations include two mechanisms generating preferential biases: effort tracking local density, and effort responding to a spatially structured, but unobserved anomaly that also inflates catch. We show that the ability of the estimation methods to correct bias depends on the mechanism responsible for concentrating effort; while the joint model is robust to both data-generating mechanisms, the discrete choice model is only able to correct biases when the mechanism is anomaly-driven. As a real-world case study, we apply these corrections to simulated preferentially sampled data and include indices in the shortspine thornyhead stock assessment model. We found that uncorrected time-varying preferential sampling positively biased estimates of a major recruitment event and, to a lesser extent, unfished recruitment. This bias propagated through to bias in overfishing limits that grew as the population was projected forward. While bias-corrected indices still produced some positive biases in overfishing limits, these were substantially reduced compared to the naive approach. As the mechanism of preferential sampling is rarely known in advance, our results suggest that the joint model represents a robust default for correcting fishery-dependent indices of abundance.

ecology↗

bioRxiv2024

Goliath clades and in vivo tracking of clonal dynamics show three phases of UV-induced skin carcinogenesis

While the genetic paradigm of cancer etiology has proven powerful, it remains incomplete as evidenced by the widening spectrum of non-cancer cell-autonomous "hallmarks" of cancer. Studies have demonstrated the commonplace presence of high oncogenic mutational burdens in homeostatically-stable epithelia. Hence, the presence of driver mutations alone does not result in cancer. Here, we reveal additional forces governing the eco-evolutionary dynamics of carcinogenesis. Using a UV-driven mouse model of cutaneous squamous cell carcinoma, we tested our central hypothesis that cancer initiation occurs in three phases: 1) tissue disruption and the emergence of unusually large "goliath" clades (ecological driver), 2) clonal selection within a subset of these goliaths as evidenced by the presence of areas of unusually high local densities of cells (termed "micro-lumps") with higher mutational burdens (evolutionary driver), and 3) emergence of macroscopic lesions. We tracked these tissue level ecological and evolutionary drivers of cancer initiation via in-vivo serial imaging and 3-D reconstruction of fluorescently labeled keratinocyte clades, yielding 25,085 clade measurements from 14 mice over 3 months, and 14,525 clades from 4 of these mice over 6 months. While median and mean clade sizes differed little between UV and non-UV exposure (cc. 65,000 {micro}m3), our ecological survey revealed the emergence of large goliath clades (> cc. 4.2x106 {micro}m3), almost exclusively within UV-exposed skin. Goliath clades emerged by month 2, increased dramatically in number by months 3-4, and plateaued between months 5-6. Goliath clades arose as serpentine structures, intercalating between adjacent keratinocytes. Unexpectedly, targeted DNA sequencing revealed mutations with very low variant allele frequencies within clades, but substantial differences among clades, suggesting that positive selection for these mutations is superfluous to the development of goliaths early in carcinogenesis. scRNAseq analysis of both bulk skin and sorted clades revealed epidermal de-differentiation and immune suppression as early events. By month 7, mutational burdens were significantly larger in goliath clades, particularly for those with micro-lumps. Finally, lesions began to emerge between months 6 and 7, only in UV-exposed skin. To confirm that goliath clades are orders of magnitude more likely to spawn lesions, we randomly selected 21 goliath clades at months 6-7, prior to the time of the emergence of detectable lesions, and followed them over time. Remarkably, 2 of these developed into macroscopic lesions. Our adaptation of the Drake equation estimated the probability of this to be <10-6. Taken together, our results support the presence of three phases of cancer initiation, the earliest of which presage the acquisition of driver mutations and also explains why cancers are relatively rare in relation to the degree of somatic mosaicism present in UV-exposed skin.

cancer biology↗

bioRxiv2024

iModMix: Integrative Module Analysis for Multi-omics Data

SummaryIntegrative Module Analysis for Multi-omics Data (iModMix) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead of relying on pathway annotations or prior biological knowledge, iModMix constructs data-driven modules using graphical lasso to estimate sparse networks from omics features. These modules are summarized into eigenfeatures and correlated across datasets for horizontal integration, while preserving the distinct feature sets and interpretability of each omics type. iModMix operates directly on matrices containing expression or abundances for a wide range of features, including but not limited to genes, proteins, and metabolites. Because it does not rely on annotations (e.g., KEGG identifiers), it can seamlessly incorporate both identified and unidentified metabolites, addressing a key limitation of many existing metabolomics tools. iModMix is available as a user-friendly R Shiny application requiring no programming expertise (https://imodmix.moffitt.org), and as an R package for advanced users (https://github.com/biodatalab/iModMix). The tool includes several public and in-house datasets to illustrate its utility in identifying novel multi-omics relationships in diverse biological contexts. Availability and implementationShiny application: https://imodmix.moffitt.org. The R package and source code: https://github.com/biodatalab/iModMix.

bioinformatics↗

bioRxiv2023

Branched-chain keto acids promote an immune-suppressive and neurodegenerative microenvironment in leptomeningeal disease

Leptomeningeal disease (LMD) occurs when tumors seed into the leptomeningeal space and cerebrospinal fluid (CSF), leading to severe neurological deterioration and poor survival outcomes. We utilized comprehensive multi-omics analyses of CSF from patients with lymphoma LMD to demonstrate an immunosuppressive cellular microenvironment and identified dysregulations in proteins and lipids indicating neurodegenerative processes. Strikingly, we found a significant accumulation of toxic branched-chain keto acids (BCKA) in the CSF of patients with LMD. The BCKA accumulation was found to be a pan-cancer occurrence, evident in lymphoma, breast cancer, and melanoma LMD patients. Functionally, BCKA disrupted the viability and function of endogenous T lymphocytes, chimeric antigen receptor (CAR) T cells, neurons, and meningeal cells. Treatment of LMD mice with BCKA-reducing sodium phenylbutyrate significantly improved neurological function, survival outcomes, and efficacy of anti-CD19 CAR T cell therapy. This is the first report of BCKA accumulation in LMD and provides preclinical evidence that targeting these toxic metabolites improves outcomes.

cancer biology↗

bioRxiv2023

BERLIN: Basic Explorer for single-cell RNAseq analysis and cell Lineage Determination.

Single-cell RNA sequencing has revolutionized the study of immuno-oncology, cancer biology, and developmental biology by enabling the joint characterization of gene expression and cellular heterogeneity in a single platform. As of July 2023, the Gene Expression Omnibus now contains over 4000 published single-cell data sets, providing an invaluable opportunity for reanalysis to identify new cell types or cellular states as well as their defining transcriptional programs. To facilitate the reprocessing of these public datasets, we have devised a single-cell RNA sequencing analysis framework for data retrieval, quality control, expression normalization, dimension reduction, cell clustering, and data integration. Additionally, we have developed a Shiny App visualization platform that enables the exploration of gene expression, cell type annotations, and cell lineages through a user interface. We performed a re-analysis of single-cell RNAseq data generated from acute myeloid leukemia and tumor-reactive lymphocytes and found our pipeline to faithfully recapitulated the cell type assignment as well as expected lineage trajectories. Altogether, we present BERLIN, a single-cell RNAseq analysis pipeline that facilitates the integration and public dissemination of results from the reanalysis.

bioinformatics↗

bioRxiv2019

DNA methylation-based immune cell deconvolution in solid tumors

Understanding of the tumor microenvironment (TME) structure is likely to have a profound and immediate impact on therapeutic interventions as well as the development of signatures for diagnostic and prognostic evaluations. DNA methylation arrays represent one of the most reproducible molecular assays across replicates and studies, but its value of profiling tumor-infiltrating immune lymphocytes (TILs) hasnt been intensively investigated. Here we report a model-based evaluation of tumor TIL levels using DNA methylation profiles. By employing a hybrid method of stability selection and elastic net, we show that methylation array data in ten TCGA cancer types provide a strikingly accurate prediction of immune cell abundance, in particular the levels of T cells, B cells and cytotoxic cells in skin cutaneous melanoma (SKCM). The immune-informative CpG sites showed significant prognostic values, representing important candidates for further functional validation. Further, we present regression models each using only ten CpG sites to estimate the levels of infiltrated immune cell types in melanoma. To validate these models, we performed matched methylation EPIC array and RNA-seq on 30 new melanoma samples. We observed high concordance on methylation and gene expression predicted tumor immune infiltration levels in our new dataset. Our study demonstrated that DNA methylation data is a valuable resource in reliably evaluating tumor immune responses. The selected methylation panels provide candidate targets for future clinical researches. Our prediction models are easy to implement and will provide reference for future clinical practices.

bioinformatics↗