ArticleGenome biology2024
SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- FlashDeconv reveals resolution horizons in atlas-scale spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Defining the role of natural killer cells in acute myeloid leukemia through the lens of single-cell omics.Frontiers in immunology · 2026Review
- A practical guide for choosing an optimal spatial transcriptomics technology from seven major commercially available options.BMC genomics · 2025Review
- Spall: accurate and robust unveiling cellular landscapes from spatially resolved transcriptomics data using a decomposition network.BMC bioinformatics · 2024Article
- Deconvolution of spatial transcriptomics data via graph contrastive learning and partial least square regression.Briefings in bioinformatics · 2024Article
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Abstract
Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER's superior accuracy and robustness over existing methods.
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