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scVI-Tools |

Single Cell Assign: Annotation

4
3
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, h5ad, h5
    • Data should be in .h5ad format. Columns for covariate comparisons should be included in the dataset.
or
Don’t have a file?
Use our demo data to run
Use Demo Data
View example data

Gene Marker Matrix

Drag your file(s) or upload
  • Your file can be in the following formats:csv, tsv, txt
    • Data should be in .csv or .tsv format. Binary 1's and 0's should indicate which genes are associated with which cell types.
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

CellAssign is a simple yet, efficient approach for annotating scRNA-seq data in the scenario in which cell-type-specific gene markers are known. The method also allows users to control for nuisance covariates like batch or donor. The scvi-tools implementation of CellAssign uses stochastic inference, such that CellAssign will scale to very large datasets.

The advantages of CellAssign are: (i) Lightweight model that can be fit quickly, (ii) Ability to control for nuisance factors.

The limitations of CellAssign include: (i) Requirement for a cell types by gene markers binary matrix, (ii) The simple linear model may not handle non-linear batch effects.

Citation:
Allen W. Zhang, Ciara O’Flanagan, Elizabeth A. Chavez, Jamie LP Lim, Nicholas Ceglia, Andrew McPherson, Matt Wiens et al. (2019), Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling, Nature Methods.
Released:
Nov-30-2022
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