Searching journal content for articles similar to Lakkis et al. 31 (10): 1753.

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  1. Method: STCC enhances spatial domain detection through consensus clustering of spatial transcriptomics data
    • Congcong Hu,
    • Nana Wei,
    • Jiyuan Yang,
    • Hua-Jun Wu,
    • and Xiaoqi Zheng
    Genome Res. June 2025 35: 1415-1428; Published in Advance May 12, 2025, doi:10.1101/gr.280031.124
    ..., such as the joint embedding from deep learning–based methods. This could potentially enhance the performance and robustness of the consensus clustering. Second, the current version of our consensus framework only incorporates seven tools, which is relatively limited considering the vast number of clustering tools...
  2. Method: Unified integration of spatial transcriptomics across platforms with LLOKI
    • Ellie Haber,
    • Ajinkya Deshpande,
    • Jian Ma,
    • and Spencer Krieger
    Genome Res. December 2025 35: 2722-2733; Published in Advance November 13, 2025, doi:10.1101/gr.280803.125
    ...alignment across data sets. Optimal transport-guided feature propagation adjusts data sparsity to match scRNA-seq references through graph-based imputation, enabling single-cell foundation models such as scGPT to generate unified features. Batch alignment then refines scGPT-transformed embeddings...
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  3. Editorial: New frontiers in single-cell genomics
    • Nicholas E. Navin,
    • Orit Rozenblatt-Rosen,
    • and Nancy R. Zhang
    Genome Res. October 2021 31: ix-x; doi:10.1101/gr.276129.121
    ...: 1831 – 1842 . 10.1101/gr.260893.120 Lakkis J , Wang D , Zhang Y , Hu G , Wang K , Pan H , Ungar L , Reilly MP , Li X , Li M . 2021 . A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics . Genome Res (this issue) 31 : 1753...
    OPEN ACCESS ARTICLE
  4. Perspective: Applications of single-cell genomics and computational strategies to study common disease and population-level variation
    • Benjamin J. Auerbach,
    • Jian Hu,
    • Muredach P. Reilly,
    • and Mingyao Li
    Genome Res. October 2021 31: 1728-1741; doi:10.1101/gr.275430.121
    ...workflow. The typical workflow involves data preprocessing, combination of multiple single-cell data sets into a combined data set, clustering and cell type annotation, differential expression analysis, trajectory inference, and pseudotime analysis.Correction of batch effectsLarge-scale single-cell data...
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  5. Method: Likelihood-based deconvolution of bulk gene expression data using single-cell references
    • Dan D. Erdmann-Pham,
    • Jonathan Fischer,
    • Justin Hong,
    • and Yun S. Song
    Genome Res. October 2021 31: 1794-1806; Published in Advance July 22, 2021, doi:10.1101/gr.272344.120
    ...to measure gene expression in individual cells, achieving higher resolution at the expense of increased noise. If carefully incorporated, such single-cell data can be used to deconvolve bulk samples to yield accurate estimates of the true cell type proportions, thus enabling one to disentangle the effects...
  6. Method: spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
    • Tianyu Liu,
    • Tinglin Huang,
    • Wengong Jin,
    • Tinyi Chu,
    • Rex Ying,
    • and Hongyu Zhao
    Genome Res. February 3, 2026 ; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language models to jointly denoise and impute spatial transcriptomic data. Our results demonstrate that sp...
    OPEN ACCESS ARTICLE
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