Comments (1)
No, you can be flexible with how the input data looks like. The only thing you absolutely need is a list of genes affected in your receiver cell due to intercellular communication with a sender cell (this list can be obtained in several ways, eg via DE analysis on your receiver cell data). Preferably, you also have expression data of the sender cell to reduce the list of potential ligands from all ligands in the database to only ligands expressed in the sender cell.
In this vignette https://github.com/saeyslab/nichenetr/blob/master/vignettes/ligand_activity_geneset.md we used an expression matrix, a metadata table indicating the celltype of each cell, and a list with affected genes for which we wanted to predict upstream ligands. For the vignettes dealing with seurat objects, we just used a seurat object and in the metadata we added a column indicating the cluster/celltype and condition. DE analysis to define the affected genes is done there automatically.
If you would still be in doubt about how the data should look like, you can just download the example R objects and explore in R how they look like.
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Related Issues (20)
- I can't install nichenet HOT 1
- Condition_colname is not used for subsetting in nichenet_seuratobj_aggregate HOT 2
- `make_line_plot` bugs
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- Error in `generate_info_tables`
- Warning message in `predict_ligand_activities`
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- Error when passing the recorrect_umi argument in get_lfc_celltype HOT 1
- Error in WhichCells.Seurat(object = object, idents = ident.2) : Cannot find the following identities in the object: Adjacent HOT 1
- Error in `Idents<-`: ! 'value' must be a factor or vector HOT 3
- Different results running. the same. code in different version. of HOT 6
- Protein complex HOT 1
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- Low AUPR values in analyses HOT 4
- RankActiveLigands Error
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- Parallelization error when optimizing parameters for NicheNet HOT 4
- How is the Ligand-Target-Matrix generated? HOT 2
- Receiver cells in differential analysis HOT 1
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