Comments (2)
I strongly recommend going over supplementary information 2. There is a long discussion there about low correlations and unknowns. We use different measures for confidence and it is possible for the user to use them, however, at least as a first pass it is not recommended.
SingleR is not meant to be viewed solely by the colors in the tSNE plots, this is only a first step in understanding the complexity of the data. We provide different tools and visualizations for further analyses which can assist in this endeavor. For example, one of the strengths of SingleR is to help detect previously uncharacterized cells. You can see in the paper our journey to identify a new macrophage state which SingleR identified as associated with two previously characterized states.
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I found the supplementary information in the folder named "vignettes" and it answers my question.
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Related Issues (20)
- Timeout error HOT 2
- Download failed HOT 1
- can't find any of the functions related to reference data sets HOT 1
- Adding annotations HOT 2
- how to create a new reference data set HOT 4
- plotDeltaDistribution not found HOT 1
- Seurat object as input and normalization HOT 1
- Cell labeling using SingleR in seurat HOT 1
- Retrieving a list of marker genes which were used assign cell type labels to clusters HOT 1
- How to annotation cluster by singleR 1.2.4 HOT 2
- can i annotate cells in specific cell types?
- 'assay(<SingleCellExperiment>, i="character", ...)' invalid subscript 'i' 'dimnames' applied to non-array
- the Seurat3.2.2 load and SingleR 1.0.1 conflict error HOT 1
- Can't download MouseRNAseqData
- Error in is.null(rownames(x)) && nrow(x) : invalid 'y' type in 'x && y' HOT 4
- create custom reference dataset HOT 2
- Error in rowAnyNAs(DelayedArray(x)) HOT 1
- SingleR() error HOT 2
- SingleR Performance Issue
- issue with input?
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