NLSDeconv：一种用于空间转录组学数据的高效细胞类型反卷积方法。

NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data.

作者信息

Chen Yunlu, Ruan Feng, Wang Ji-Ping

机构信息

Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208, United States.

出版信息

Bioinformatics. 2024 Dec 26;41(1). doi: 10.1093/bioinformatics/btae747.

DOI:10.1093/bioinformatics/btae747

PMID:39705170

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11696698/

Abstract

SUMMARY

Spatial transcriptomics (ST) allows gene expression profiling within intact tissue samples but lacks single-cell resolution. This necessitates computational deconvolution methods to estimate the contributions of distinct cell types. This article introduces NLSDeconv, a novel cell-type deconvolution method based on non-negative least squares, along with an accompanying Python package. Benchmarking against 18 existing deconvolution methods on various ST datasets demonstrates NLSDeconv's competitive statistical performance and superior computational efficiency.

AVAILABILITY AND IMPLEMENTATION

NLSDeconv is freely available at https://github.com/tinachentc/NLSDeconv as a Python package.

摘要

空间转录组学（ST）能够在完整的组织样本中进行基因表达谱分析，但缺乏单细胞分辨率。这就需要计算反卷积方法来估计不同细胞类型的贡献。本文介绍了一种基于非负最小二乘法的新型细胞类型反卷积方法NLSDeconv，以及一个配套的Python包。在各种ST数据集上与18种现有的反卷积方法进行基准测试，结果表明NLSDeconv具有具有竞争力的统计性能和卓越的计算效率。