Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with normalization
Search options answers only
not deleted
user 3415
The tricky art of scaling quantitative data across libraries, typically to account for differences in sequencing depth. This can also be about scaling for read source length, like transcript or gene length, in order to enable comparisons across genes.
3
votes
Discordance in gene signature behavior between bulk and single-cell RNASeq
One explanation could be that your mapping of clusters to timepoints is not accurate. There are other methods you could look at for doing this, for example scMap, scPred, or Seurat v3 (disclosure: I a …
- 684
3
votes
How to normalise scRNASeq data for differential expression analysis
I would suggest using a likelihood ratio test for differential expression using logistic regression with batch as a latent variable. In Seurat you can do:
markers <- FindAllMarkers(object, test.use = …
- 684