Comments (9)
Hey Tom,
Interesting idea - do you have an example of what you have in mind?
Thanks,
Nick
from escape.
For ex: out of a set of 20 genes supplied to the enrichit function, perhaps only 4-5 genes play an overwhelmingly important role in the enrichit score output. If this was the case, would it be possible identify the relative contribution of the individual genes to the score?
from escape.
hi - I am also wondering this. Any updates?
from escape.
Hey thanks for the question - working on a major overhaul of the package that will have the enrichment plot function. I do not have a timeline yet though.
from escape.
This took quite awhile (actually a lot harder than I thought), but the newest dev version of escape has enrichmentPlot()
a function to examine the distribution of ranked gene order across single-cell groups.
https://ncborcherding.github.io/vignettes/escape_vignette.html#55_6_Enrichment_Plots
As of right now it just shows the mean rank across the group - I will work on adding the enrichment score and p-value calculation directly to the plot.
from escape.
from escape.
Hey Tom,
Absolutely - I think there are two approaches right now in the field for gene set for single-cell data, 1) pseudobulk and 2) true single-cell enrichment. There are some pluses/minuses to both approaches - I think pseudobulk produces more robust enrichment results for example. The calculation of enrichment at a single-cell level is probably better for the analysis of heterogeneity. You can use escape for either approach.
Let me know if you have any other questions.
Nick
from escape.
from escape.
Hey Tom,
Both the "ssGSEA" and the "UCell" method use raw count data. No need for normalization before running.
Nick
from escape.
Related Issues (20)
- Visualization of Enrichment HOT 1
- Improve computational time for EnrichIt(), performPCA(), and masterPCAplot HOT 1
- Median of getSignificance and multi_dittoPlot() don't match HOT 2
- masterPCAplot bug HOT 1
- Saving heatmap HOT 1
- enrichmentPlot did not finish HOT 3
- enrichIT values-negative scores HOT 3
- Differentially expressed genes in enrichment pathways HOT 3
- getSignificance very memory intensive when fit = ANOVA HOT 3
- about using a subset of groups in visualization HOT 8
- Batch effect correction HOT 2
- About ANOVA results viz HOT 3
- How to add significant differences between groups of variables in a figure HOT 1
- enrichIT fails due to " file names with spaces do not work properly on this platform" HOT 1
- Leading edge subset
- Installing an older version of escape HOT 2
- enrichIt results in various aspects in layered form. HOT 1
- Normalization in enrichIt using 'UCell' HOT 1
- Prepare for upcoming Seurat v5 release
- How to find Enriched pathways per seurat_cluster? HOT 1
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