Adjustments to the reference dataset design improve cell type label transfer

Mölbert, Carla; Haghverdi, Laleh

doi:10.3389/fbinf.2023.1150099

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Although data integration is still important for other analyses (e.g. such as cell type label transfer tasks (10)), we propose here an alternative method for visualizing multiple single-cell samples. Compound-SNE performs what we term a soft alignment, aiming to maximize the alignment of multiple embeddings while minimizing the local structural differences from the samples’ independent embeddings.…”

Section: Introductionmentioning

confidence: 99%

Compound-SNE: Comparative alignment of t-SNEs for multiple single-cell omics data visualisation

Cess,

Haghverdi

2024

Preprint

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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, 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 and data modalities, while preserving local embedding structures from when samples are embedded independently.

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Section: Introductionmentioning

confidence: 99%

Compound-SNE: Comparative alignment of t-SNEs for multiple single-cell omics data visualisation

Cess,

Haghverdi

2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although data integration is still important for other analyses [e.g. such as cell type label transfer tasks ( Mölbert and Haghverdi 2023 )], we propose here an alternative method for visualizing multiple single-cell samples. Compound-SNE performs what we term a soft alignment, aiming to maximize the alignment of multiple embeddings while minimizing the local structural differences from the samples’ independent embeddings.…”

Section: Introductionmentioning

confidence: 99%

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

Cess,

Haghverdi

2024

Bioinformatics

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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.

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All the single cells: Single‐cell transcriptomics/epigenomics experimental design and analysis considerations for glial biologists

Prater,

Lin

2024

Glia

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Single‐cell transcriptomics, epigenomics, and other ‘omics applied at single‐cell resolution can significantly advance hypotheses and understanding of glial biology. Omics technologies are revealing a large and growing number of new glial cell subtypes, defined by their gene expression profile. These subtypes have significant implications for understanding glial cell function, cell–cell communications, and glia‐specific changes between homeostasis and conditions such as neurological disease. For many, the training in how to analyze, interpret, and understand these large datasets has been through reading and understanding literature from other fields like biostatistics. Here, we provide a primer for glial biologists on experimental design and analysis of single‐cell RNA‐seq datasets. Our goal is to further the understanding of why decisions are made about datasets and to enhance biologists’ ability to interpret and critique their work and the work of others. We review the steps involved in single‐cell analysis with a focus on decision points and particular notes for glia. The goal of this primer is to ensure that single‐cell ‘omics experiments continue to advance glial biology in a rigorous and replicable way.

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Adjustments to the reference dataset design improve cell type label transfer

Cited by 3 publications

References 17 publications

Compound-SNE: Comparative alignment of t-SNEs for multiple single-cell omics data visualisation

Compound-SNE: Comparative alignment of t-SNEs for multiple single-cell omics data visualisation

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

All the single cells: Single‐cell transcriptomics/epigenomics experimental design and analysis considerations for glial biologists

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