svaseq - over adjustment
0
0
Entering edit mode
flippy23 • 0
@flippy23-18925
Last seen 2.2 years ago

Hi,

I had a question about over adjustment with SVA. I used the svaseq() on my dataset and ended up with 11 SV's. I had problems with DE analysis because my matrix was "computationally singular". It seems that the first couple of SV's in our dataset are highly correlated > 0.7 with cell type proportions. After reducing to 3 SV's, based on the "be" method (the leek method recommended 65), my dataset was no longer computationally singular. Were the 11 SV's just over adjusting and capturing all the heterogeneity in our sample (does this mean the data is of poor quality - given that all heterogeneity was able to be captured in 11 SVs)? Also, what gives rise to such a large discrepancy in the number of SV's proposed by the "be" and "leek" methods? Do you suggest adding cell type proportions to our svaseq modelmatrix formula and then also adjusting in downstream analysis?

Thank you

svaseq sva • 310 views
ADD COMMENT
1
Entering edit mode

Unless you show some code you are asking people to make wild guesses about what you might have done, which isn't particularly helpful for anybody involved.

ADD REPLY

Login before adding your answer.

Traffic: 484 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6