Compound-SNE: Comparative alignment of t-SNEs for multiple single-cell omics data visualisation.
Bioinformatics
; 2024 Jul 25.
Article
en En
| MEDLINE
| ID: mdl-39052868
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. SUPPLEMENTARY INFORMATION Available online. Provides algorithmic details and additional tests.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania