Nature MethodsBioinformaticsPeer-reviewed journal article
MultiVI: deep generative model for the integration of multi-modal data
Tal AshuachiD, Mariano I. GabittoiD, Rohan V. KoodliiD, Giuseppe-Antonio SaldiiD, Michael I. JordaniD, Nir YosefiD
Abstract
Jointly profiling the transcriptional and chromatin accessibility landscapes of single-cells is a powerful technique to characterize cellular populations. Here we present MultiVI, a probabilistic model to analyze such multiomic data and integrate it with single modality datasets. MultiVI creates a joint representation that accurately reflects both chromatin and transcriptional properties of the cells even when one modality is missing. It also imputes missing data, corrects for batch effects and is available in the scvi-tools framework: https://docs.scvi-tools.org/.
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: batch correction / integration
- research focus: imputation
- research focus: dimensionality reduction
- approach: deep learning
- Citations
- 386
- FWCI
- 32.90
- Field percentile
- top 1%
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