NLSDeconv: An efficient cell-Type deconvolution method for spatial transcriptomics data

Yunlu Chen; Feng Ruan; Ji Ping Wang

doi:10.1093/bioinformatics/btae747

NLSDeconv: An efficient cell-Type deconvolution method for spatial transcriptomics data

Yunlu Chen^*, Feng Ruan, Ji Ping Wang

^*Corresponding author for this work

Statistics and Data Science

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Article number	btae747
Journal	Bioinformatics
Volume	41
Issue number	1
DOIs	https://doi.org/10.1093/bioinformatics/btae747
State	Published - Jan 1 2025

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btae747

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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.",

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AB - 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.

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NLSDeconv: An efficient cell-Type deconvolution method for spatial transcriptomics data

Abstract

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