Yijiang Chen

All Publications

• Mesoscopic structure graphs for interpreting uncertainty in non-linear embeddings. Computers in biology and medicine Zhao, J., Liu, X., Tang, H., Wang, X., Yang, S., Liu, D., Chen, Y., Chen, Y. V. 2024; 182: 109105

  Abstract

  Probabilistic-based non-linear dimensionality reduction (PB-NL-DR) methods, such as t-SNE and UMAP, are effective in unfolding complex high-dimensional manifolds, allowing users to explore and understand the structural patterns of data. However, due to the trade-off between global and local structure preservation and the randomness during computation, these methods may introduce false neighborhood relationships, known as distortion errors and misleading visualizations. To address this issue, we first conduct a detailed survey to illustrate the design space of prior layout enrichment visualizations for interpreting DR results, and then propose a node-link visualization technique, ManiGraph. This technique rethinks the neighborhood fidelity between the high- and low-dimensional spaces by constructing dynamic mesoscopic structure graphs and measuring region-adapted trustworthiness. ManiGraph also addresses the overplotting issue in scatterplot visualization for large-scale datasets and supports examining in unsupervised scenarios. We demonstrate the effectiveness of ManiGraph in different analytical cases, including generic machine learning using 3D toy data illustrations and fashion-MNIST, a computational biology study using a single-cell RNA sequencing dataset, and a deep learning-enabled colorectal cancer study with histopathology-MNIST.

  View details for DOI 10.1016/j.compbiomed.2024.109105

  View details for PubMedID 39265479

• Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS. medRxiv : the preprint server for health sciences Fan, F., Liu, Q., Zee, J., Ozeki, T., Demeke, D., Yang, Y., Farris, A. B., Wang, B., Shah, M., Jacobs, J., Mariani, L., Lafata, K., Rubin, J., Chen, Y., Holzman, L., Hodgin, J. B., Madabhushi, A., Barisoni, L., Janowczyk, A. 2024

  Abstract

  Background: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis.Methods: Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset.Results: N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA.Conclusions: Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.

  View details for DOI 10.1101/2024.07.19.24310619

  View details for PubMedID 39072032

• The chromatin landscape of healthy and injured cell types in the human kidney NATURE COMMUNICATIONS Gisch, D. L., Brennan, M., Lake, B. B., Basta, J., Keller, M. S., Melo Ferreira, R., Akilesh, S., Ghag, R., Lu, C., Cheng, Y., Collins, K. S., Parikh, S. V., Rovin, B. H., Robbins, L., Stout, L., Conklin, K. Y., Diep, D., Zhang, B., Knoten, A., Barwinska, D., Asghari, M., Sabo, A. R., Ferkowicz, M. J., Sutton, T. A., Kelly, K. J., De Boer, I. H., Rosas, S. E., Kiryluk, K., Hodgin, J. B., Alakwaa, F., Winfree, S., Jefferson, N., Tuerkmen, A., Gaut, J. P., Gehlenborg, N., Phillips, C. L., El-Achkar, T. M., Dagher, P. C., Hato, T., Zhang, K., Himmelfarb, J., Kretzler, M., Mollah, S., Lake, B., Morales, A., Stillman, I., Lecker, S., Bogen, S., Verma, A., Yu, G., Schmidt, I., Henderson, J., Beck, L., Yadati, P., Waikar, S., Amodu, A. A., Maikhor, S., Ilori, T., Colona, M. R., Weins, A., Mcmahon, G., Hacohen, N., Greka, A., Marshall, J. L., Hoover, P. J., Viswanathan, V. S., Crawford, D., Aulisio, M., Bush, W., Chen, Y., Madabhushi, A., O'Malley, C., Gadegbeku, C., Sendrey, D., Poggio, E., O'Toole, J., Sedor, J., Taliercio, J., Bush, L., Herlitz, L., Palmer, E., Nguyen, J., Spates-Harden, K., Cooperman, L., Jolly, S., Vinovskis, C., Bomback, A., Barasch, J., Kiryluk, K., Appelbaum, P., D'Agati, V., Berrouet, C., Mehl, K., Sabatello, M., Shang, N., Balderes, O., Canetta, P. A., Kudose, S., de Pinho Goncalves, J., Migas, L., van de Plas, R., Lardenoije, R., Barisoni, L., Rennke, H., Verdoes, A., Sabo, A., Gisch, D., Williams, J., Kelly, K., Dunn, K., Eadon, M., Ferkowicz, M., Dagher, P., Winfree, S., Bledsoe, S., Wofford, S., (El-Achkar), T., Sutton, T., Bowen, W., Slade, A., Record, E., Cheng, Y., Jain, Y., Herr, B., Quardokus, E., Wang, A., Villalobos, C., Parikh, C., Atta, M., Menez, S., Wen, Y., Xu, A., Bernard, L., Johansen, C., Chen, S., Rosas, S., Donohoe, I., Sun, J., Knight, R., Shpigel, A., Bebiak, J., Saul, J., Ardayfio, J., Koewler, R., Pinkeney, R., Campbell, T., Azeloglu, E., Nadkarni, G., He, J., Tokita, J., Campbell, K., Patel, M., Lefferts, S., Iyengar, S., Ward, S., Coca, S., He, C., Xiong, Y., Prasad, P., Rovin, B., Shapiro, J. P., Parikh, S., Madhavan, S. M., Lukowski, J., Velickovic, D., Pasa-Tolic, L., Oliver, G., Troyanskaya, O., Sealfon, R., Mao, W., Wong, A., Pollack, A., Goltsev, Y., Ginley, B., Lutnick, B., Nolan, G., Anjani, K., Mukatash, T., Laszik, Z. G., Campos, B., Thajudeen, B., Beyda, D., Bracamonte, E., Brosius, F., Woodhead, G., Mendoza, K., Marquez, N., Scott, R., Tsosie, R., Saunders, M., Rike, A., Woodle, E., Lee, P. J., Alloway, R. R., Shi, T., Hsieh, E., Kendrick, J., Thurman, J., Wrobel, J., Pyle, L., Bjornstad, P., Lucarelli, N., Sarder, P., Renteria, A., Ricardo, A., Srivastava, A., Redmond, D., Carmona-Powell, E., Bui, J., Lash, J., Fox, M., Meza, N., Gaba, R., Setty, S., Kelly, T., Lienczewski, C., Demeke, D., Otto, E., Ascani, H., Hodgin, J., Schaub, J., Hartman, J., Mariani, L., Bitzer, M., Rose, M., Bonevich, N., Conser, N., Mccown, P., Dull, R., Menon, R., Reamy, R., Eddy, S., Balis, U., Blanc, V., Nair, V., He, Y., Wright, Z., Steck, B., Luo, J., Frey, R., Coleman, A., Henderson-Brown, D., Berge, J., Caramori, M., Adeyi, O., Nachman, P., Safadi, S., Flanagan, S., Ma, S., Klett, S., Wolf, S., Harindhanavudhi, T., Rao, V., Mottl, A., Froment, A., Zeitler, E., Bream, P., Kelley, S., Rosengart, M., Elder, M., Palevsky, P., Murugan, R., Hall, D. E., Bender, F., Winters, J., Kellum, J. A., Gilliam, M., Tublin, M., Tan, R., Zhang, G., Sharma, K., Venkatachalam, M., Hendricks, A., Kermani, A., Torrealba, J., Vazquez, M., Wang, N., Cai, Q., Miller, R., Ma, S., Hedayati, S., Hoofnagle, A., Wangperawong, A., Berglund, A., Dighe, A. L., Young, B., Larson, B., Berry, B., Alpers, C., Limonte, C., Stutzke, C., Roberts, G., de Boer, I., Snyder, J., Phuong, J., Carson, J., Rezaei, K., Tuttle, K., Brown, K., Blank, K., Sarkisova, N., Jefferson, N., Mcclelland, R., Mooney, S., Nam, Y., Wilcox, A., Park, C., Dowd, F., Williams, K., Grewenow, S. M., Daniel, S., Shankland, S., Pamreddy, A., Ye, H., Montellano, R., Bansal, S., Pillai, A., Zhang, D., Park, H., Patel, J., Sambandam, K., Basit, M., Wen, N., Moe, O. W., Toto, R. D., Lee, S. C., Sharman, K., Caprioli, R. M., Fogo, A., Allen, J., Spraggins, J., Djambazova, K., de Caestecker, M., Dufresne, M., Farrow, M., Vijayan, A., Minor, B., Nwanne, G., Gaut, J., Conlon, K., Kaushal, M., Diettman, S. M., Victoria Castro, A. M., Moledina, D., Wilson, F. P., Moeckel, G., Cantley, L., Shaw, M., Kakade, V., Arora, T., Jain, S., Rauchman, M., Eadon, M. T. 2024; 15 (1): 433

  Abstract

  There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

  View details for DOI 10.1038/s41467-023-44467-6

  View details for Web of Science ID 001141040600003

  View details for PubMedID 38199997

  View details for PubMedCentralID PMC10781985