Language
English
Publication Date
10-14-2024
Journal
Genome Biology
DOI
10.1186/s13059-024-03416-2
PMID
39402626
PMCID
PMC11475911
PubMedCentral® Posted Date
10-14-2024
PubMedCentral® Full Text Version
Post-print
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.
Keywords
Machine Learning, Single-Cell Analysis, Transcriptome, Humans, Gene Expression Profiling, Sequence Analysis, RNA, Software, Animals, Regression Analysis, RNA-Seq
Published Open-Access
yes
Recommended Citation
Liu, Yunqing; Li, Ningshan; Qi, Ji; et al., "SDePER: A Hybrid Machine Learning and Regression Method for Cell-Type Deconvolution of Spatial Barcoding-Based Transcriptomic Data" (2024). Faculty and Staff Publications. 1757.
https://digitalcommons.library.tmc.edu/baylor_docs/1757
Included in
Biological Phenomena, Cell Phenomena, and Immunity Commons, Critical Care Commons, Internal Medicine Commons, Medical Cell Biology Commons, Pulmonology Commons, Sleep Medicine Commons