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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
or
Don’t have a file?
Use our demo data to run
Use Demo Data
Step 2: Set Parameters
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Normal distribution
Negative binomial distribution
Step 3: Complete run profile
Where to save results?

Default. Save in a secure and encrypted S3 bucket in our infra.

Mounting lets you access files from your S3 for running jobs and save job results directly to it.

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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