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- W2912284931 abstract "Abstract Quantifying the differences in gene expression and cellular composition between single-cell RNA-sequencing (scRNA-seq) data sets presents an analytical challenge due to biological and technical noise. To facilitate analysis of multiple scRNA-seq experiments, we developed MELD: Manifold Enhancement of Latent Dimensions. MELD leverages tools from graph signal processing to learn the underlying, or latent, dimension within the data corre-sponding to the differences between data sets. We call this dimension the Enhanced Experimental Signal (EES). MELD learns the EES by filtering the categorical experimental label in the graph frequency domain to recover a smooth signal with continuous values. We also present a novel clustering algorithm combing the graph Fourier transform with the EES to identify cells that are transcriptionally similar and exhibit uniform response to a pertur-bation via an adapted vertex frequency clustering. Together, these methods can be used to identify signature genes that vary strongly between conditions, characterize clusters of cells with similar responses to the experiment, and quantify the degree to which each cluster is affected by a given perturbation. We demonstrate the advantages of MELD analysis using a combination of biological and synthetic data sets. MELD is implemented in Python and code is available at https://github.com/KrishnaswamyLab/MELD." @default.
- W2912284931 created "2019-02-21" @default.
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- W2912284931 date "2019-03-24" @default.
- W2912284931 modified "2023-10-01" @default.
- W2912284931 title "Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing" @default.
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