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Compound-SNE: comparative alignment of t-SNEs for multiple single-cell omics data visualization.

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Bibliographic Details
Title: Compound-SNE: comparative alignment of t-SNEs for multiple single-cell omics data visualization.
Authors: Cess, Colin G, Haghverdi, Laleh
Source: Bioinformatics; Jul2024, Vol. 40 Issue 7, p1-5, 5p
Subject Terms: RESEARCH personnel, DATA visualization, GENE expression, DATA analysis, PYTHON programming language
Abstract: Summary One of the first steps in single-cell omics data analysis is visualization, which allows researchers to see how well-separated cell-types are from each other. When visualizing multiple datasets at once, data integration/batch correction methods are used to merge the datasets. While needed for downstream analyses, these methods modify features space (e.g. gene expression)/PCA space in order to mix cell-types between batches as well as possible. This obscures sample-specific features and breaks down local embedding structures that can be seen when a sample is embedded alone. Therefore, in order to improve in visual comparisons between large numbers of samples (e.g. multiple patients, omic modalities, different time points), we introduce Compound-SNE, which performs what we term a soft alignment of samples in embedding space. We show that Compound-SNE is able to align cell-types in embedding space across samples, while preserving local embedding structures from when samples are embedded independently. Availability and implementation Python code for Compound-SNE is available for download at https://github.com/HaghverdiLab/Compound-SNE. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Summary One of the first steps in single-cell omics data analysis is visualization, which allows researchers to see how well-separated cell-types are from each other. When visualizing multiple datasets at once, data integration/batch correction methods are used to merge the datasets. While needed for downstream analyses, these methods modify features space (e.g. gene expression)/PCA space in order to mix cell-types between batches as well as possible. This obscures sample-specific features and breaks down local embedding structures that can be seen when a sample is embedded alone. Therefore, in order to improve in visual comparisons between large numbers of samples (e.g. multiple patients, omic modalities, different time points), we introduce Compound-SNE, which performs what we term a soft alignment of samples in embedding space. We show that Compound-SNE is able to align cell-types in embedding space across samples, while preserving local embedding structures from when samples are embedded independently. Availability and implementation Python code for Compound-SNE is available for download at https://github.com/HaghverdiLab/Compound-SNE. [ABSTRACT FROM AUTHOR]
ISSN:13674803
DOI:10.1093/bioinformatics/btae471