Comments (1)
Hi Kai,
If the SE matrix is all zero, then the cor.genes
and cor.cells
functions will be identical to taking cor(t(x))
and cor(x)
for expression matrix x
. If you want to estimate the uncertainty of the imputation from the other software, you can set mu
in the saver
function to the imputed values (see "Using other predictions" of the tutorial). For example, out <- saver(x, mu = other_impute)
and then run cor.genes
or cor.cells
on out
.
from saver.
Related Issues (20)
- SAVER keeps crashing due to non-finite values HOT 2
- SAVER fails when using multi-threading. HOT 11
- SAVER vs Seurat normalization HOT 7
- Difference in output while using all genes vs using pred.genes HOT 1
- Input and Output HOT 1
- Error in lm.fit ... 0 (non-NA) cases HOT 3
- questions about prior variance optimization step HOT 6
- problem too large error in rowMeans inside saver() HOT 4
- Saver in docker HOT 2
- Error reading from connection HOT 2
- Error in checkForRemoteErrors(lapply(cl, recvResult)) HOT 2
- Error in is.data.frame(x) : object 'mu' not found HOT 4
- Error in serialize(data, node$con) : error writing to connection HOT 1
- Problem running SAVER on large dataset HOT 4
- Error when I use SAVER with size.factor =1 HOT 3
- nFeatureRNA value became zero for all cells HOT 1
- Can't install this module HOT 4
- Tutorial doesn't work HOT 3
- Error in class() examination while using SAVER HOT 2
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from saver.