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scVI-Tools |

DestVI

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Omics
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Step 1: Upload your data

Single Cell RNA-Seq Data

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  • Your file can be in the following formats:csv, mtx, h5ad, h5
    • Data should be in .h5, .h5ad, .mtx, or .csv format
or
Don’t have a file?
Use our demo data to run
Use Demo Data
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Spatial Transcriptomic Data

Drag your file(s) or upload
  • Your file can be in the following formats:csv, mtx, h5ad, h5
    • Data should have an .h5, .h5ad, .mtx, or .csv extension, and should include spatial transcriptomic data
or
Don’t have a file?
Use our demo data to run
Use Demo Data
View example data
Step 2: Set Parameters
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Step 3: Complete run profile

DestVI (Deconvolution of Spatial Transcriptomics profiles using Variational Inference) posits a conditional generative model of spatial transcriptomics down to the sub-cell-type variation level which can be used to explore the spatial organization of a tissue and understanding gene expression variation between tissues and conditions.
The advantages of DestVI are: (i) Can stratify cells into discrete cell types and model continuous sub-cell-type variation, (ii) Scalable to very large datasets (>1 million cells).

Example use case: Deconvolution of, for example, 10x Visium spatial transcriptomics profiles using an accompanying single-cell RNA sequencing data.

Limitations: Effectively requires a GPU for fast inference.

Citation:
Romain Lopez, Baoguo Li, Hadas Keren-Shaul, Pierre Boyeau, Merav Kedmi, David Pilzer, Adam Jelinski, Ido Yofe, Eyal David, Allon Wagner, Can Ergen, Yoseph Addadi, Ofra Golani, Franca Ronchese, Michael I Jordan, Ido Amit, Nir Yosef (2022). DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology (in press)
Released:
Dec-08-2022
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