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scVI-Tools |

CITE-seq

2
2
Omics
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Step 1: Upload your data

Single Cell RNA-Seq Data

Drag your file(s) or upload
  • Your file can be in the following formats:csv, mtx, h5ad, h5
    • Data should be in .h5, .h5ad, .mtx, or .csv format
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Step 2: Set Parameters
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Normal distribution
Negative binomial distribution
Step 3: Complete run profile

totalVI (total Variational Inference) provides a flexible generative model of CITE-seq RNA and protein data that can be used for many common downstream tasks.

The advantages of totalVI are: (i) Comprehensive in capabilities. (ii) Scalable to very large datasets (>1 million cells).

Data should include the protein expression matrix, with one row per observation, and one column per protein.

Example use case: Train a generative model of CITE-seq RNA and protein data that can be used for many common downstream tasks.

Limitations: Effectively requires a GPU for fast inference. And, difficult to understand the balance between RNA and protein data in the low-dimensional representation of cells.

Citation:
Adam Gayoso*, Zoë Steier*, Romain Lopez, Jeffrey Regier, Kristopher L Nazor, Aaron Streets, Nir Yosef (2021), Joint probabilistic modeling of single-cell multi-omic data with totalVI, Nature Methods.
Released:
Dec-08-2022
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