Comments (1)
ora
(docs) works by first sorting the genes of each cell based on their gene expression values and selecting an arbitrary amount of top expressed genes. By default it is the top 5%, meaning that if you have 100 genes in your mat
you select only the top expressed 5 genes per cell, but this value can be set manually to be any number (n_up
). The function also allows to select bottom genes (n_bottom
) in cause you are working with gene contrast statistics obtained after DEG. In the end, you have a collection of genes per cell, that will be tested for over-representation using a one tailed fisher exact test against the gene sets stored in the database of your choice. ora
also has a version that allows to run enrichment just by a list of genes with get_ora_df
(docs), for when you don't have gene level statistics available to do the sorting.
Hope this is clear, please let me know if you have more questions.
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Related Issues (20)
- run_ora_df HOT 2
- Loading resources for mouse is not working HOT 9
- Differential expression error in pseudo-bulk step HOT 4
- shuffle_nets function produces networks with repeated edges HOT 1
- Module request: UCell signatures HOT 2
- use of the run_gsva method : format of the net argument HOT 1
- Switching to conda forge HOT 7
- Problems running decoupleR with Compressed Sparse Column (csc) count matrix HOT 2
- Error downlaoding progeny model for mouse species HOT 3
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- method run_gsea() error : SystemError: CPUDispatcher(<function nb_gsea at 0x7f7477d3b9c0>) returned a result with an exception set HOT 6
- Pseudobulk for each sample HOT 2
- Method dc.run_gsva error HOT 2
- Get gene markers used to annotate the cell type HOT 2
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- Functional PB Tutorial fails at dc.plot_associations HOT 5
- dc.get_collectri() does not work HOT 2
- Announcement: some Galaxy modules for some decoupler functionalities HOT 1
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