In SVA, the percent of biological variable of interest didnot change after adding SV1
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dhwani2410 • 0
@dhwani2410-14626
Last seen 2.8 years ago

SVM plots

https://ibb.co/jyQdgjR "Pre SVM"

https://ibb.co/BnNnNL1 "Post SVM"

I used SVM to find hidden confounding variable.The percentage of residuals were reduced after addition of new SV1 (can be seen in Post SVM image). However the percent of contribution of biological variable of interest("Pheno") still didnt go up.

Ideally the SV1 should increase the percentage of biological variable of interest. In such case should we add the SV1 as covariate while Differential expression analysis?

Also if i were to put this confounding variable in Combat function to get final matrix. what is the correct way to do it?

pheno_file=structure(list(Pheno = c("Vehicle", "Vehicle", "Vehicle", "Vehicle",
"D0", "D0", "D0", "D0", "D2", "D2", "D2", "D2", "D2", "D4", "D4",
"D4", "D4"), date = c("17", "17", "31", "31", "17", "31", "17",
"31", "17", "17", "17", "31", "31", "17", "17", "31", "31"),
sv1_cat = c(1, 4, 1, 3, 3, 4, 3, 1, 2, 3, 4, 2, 2, 1, 4,
2, 1)), class = "data.frame", row.names = c("VN-170_(MTA-1_0).CEL",
"VN-171_(MTA-1_0).CEL", "VN-172_(MTA-1_0).CEL", "VN-173_(MTA-1_0).CEL",
"VN-174_(MTA-1_0).CEL", "VN-175_(MTA-1_0).CEL", "VN-176_(MTA-1_0).CEL",
"VN-177_(MTA-1_0).CEL", "VN-178_(MTA-1_0).CEL", "VN-179_(MTA-1_0).CEL",
"VN-180_(MTA-1_0).CEL", "VN-181_(MTA-1_0).CEL", "VN-182_(MTA-1_0).CEL",
"VN-183_(MTA-1_0).CEL", "VN-184_(MTA-1_0).CEL", "VN-185_(MTA-1_0).CEL",
"VN-186_(MTA-1_0).CEL"))

modcombat = model.matrix(~1, data=pheno_file)
combat_edata = ComBat(dat=exprs(rma_Data), batch=pheno_file\$Pheno, mod=modcombat,
par.prior=TRUE, prior.plots=FALSE)


Does it anywhere use the SV1 in giving the output?. Can someone please clarify this?

SVA combat r microarray covariates • 478 views
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