Year: 2024
Type: article
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.
Source: Bioinformatics
Authors: Yunlu Chen, Feng Ruan, Ji-Ping Wang
Institution: Northwestern University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,188
OpenAlex ID: W4405622545