NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39705170.
- Also identified by DOI 10.1093/bioinformatics/btae747 and PMC identifier 11696698.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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. NLSDeconv is freely available at https://github.com/tinachentc/NLSDeconv as a Python package.
Medical subject headings
- Software
- Gene Expression Profiling