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Published on: May 22, 2017
NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data
Yunlu Chen1, Feng Ruan1, Ji-Ping Wang1
1Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208, United States.
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.
