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

Avatar
Poster
Voices Powered byElevenlabs logo
Connected to paperThis paper is a preprint and has not been certified by peer review

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

Authors

Chen, Y.; Ruan, F.; Wang, J.-P.

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 paper 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.

Follow Us on

0 comments

Add comment