Robust parametric UMAP for the analysis of single-cell data
Xu, B.; Zhang, G.
Show abstract
The increasing throughput of single-cell technologies and the pace of data generation are enhancing the resolution at which we observe cell state transitions. The characterization and visualization of these transitions rely on the construction of a low dimensional embedding, which is usually done via non-parametric methods such as t-SNE or UMAP. However, existing approaches become more and more inefficient as the size of the data gets larger and larger. Here, we test the viability of using parametric methods for the fact that they can be trained with a small subset of the data and be applied to future data when needed. We observed that the recently developed parametric version of UMAP is generalizable and robust to dropout. Additionally, to certify the robustness of the model, we use the theoretical upper and lower bounds of the mapped coordinates in the UMAP space to regularize the training process.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep feature extraction of single-cell transcriptomes by generative adversarial network 96%
- ARTEMIS integrates autoencoders and schrodinger bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell data 96%
- Securing diagonal integration of multimodal single-cell data against ambiguous mapping 96%
Similar papers in this journal
- Single-Cell Multi-Modal GAN (scMMGAN) reveals spatial patterns in single-cell data from triple negative breast cancer 97%
- Generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inference 96%
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis 95%
Similar papers in this journal
- Learning the rules of cell competition without prior scientific knowledge 95%
- Recurrent neural networks learn robust representations by dynamically balancing compression and expansion 95%
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.