Name
Abstract | ||
|---|---|---|
ABSTRACTIntegrating single-cell measurements that capture different properties of the genome is vital to extending our understanding of genome biology. This task is challenging due to the lack of a shared axis across datasets obtained from different types of single-cell experiments. For most such datasets, we lack corresponding information among the cells (samples) and the measurements (features). In this scenario, unsupervised algorithms that are capable of aligning single-cell experiments are critical to learning an in silico co-assay that can help draw correspondences among the cells. Maximum mean discrepancy-based manifold alignment (MMD-MA) is such an unsupervised algorithm. Without requiring correspondence information, it can align single-cell datasets from different modalities in a common shared latent space, showing promising results on simulations and a small-scale single-cell experiment with 61 cells. However, it is essential to explore the applicability of this method to larger single-cell experiments with thousands of cells so that it can be of practical interest to the community. In this paper, we apply MMD-MA to two recent datasets that measure transcriptome and chromatin accessibility in ~2000 single cells. To scale the runtime of MMD-MA to a more substantial number of cells, we extend the original implementation to run on GPUs. We also introduce a method to automatically select one of the user-defined parameters, thus reducing the hyperparameter search space. We demonstrate that the proposed extensions allow MMD-MA to accurately align state-of-the-art single-cell experiments. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3388440.3412410 | BCB |
Keywords | DocType | Volume |
manifold alignment,single cells,unsupervised learning | Conference | 2020 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
11 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ritambhara Singh | 1 | 40 | 6.95 |
| Pinar Demetci | 2 | 0 | 1.01 |
| Giancarlo Bonora | 3 | 0 | 0.68 |
| Vijay Ramani | 4 | 0 | 0.34 |
| Choli Lee | 5 | 1 | 1.16 |
| He Fang | 6 | 0 | 0.34 |
| Zhi-jun Duan | 7 | 2 | 1.52 |
| Xinxian Deng | 8 | 0 | 0.34 |
| Jay Shendure | 9 | 9 | 3.52 |
| Christine Disteche | 10 | 0 | 0.34 |
| William Stafford Noble | 11 | 0 | 1.01 |