method for removing consistent technical bias?
0
0
Entering edit mode
k. brand ▴ 420
@k-brand-1874
Last seen 7.7 years ago
Dear BioCers, I have a consistent, reproducible technical discrepancy resulting from two different hybridisations. Two biological replicates for Hyb A used expired arrays, and has significantly lower intensities than the two biological replicates of hyb B, which behave normally. I thus have 4 biological replicates of three different tissues which cluster (K-means) more strongly by hyb. than by tissue. RMA does a courageous job normalising the discrepancy (see summary of normalised and unnormalised data below), but if any one has experience or suggestions they care to share on getting the most out of this flawed dataset, id be very grateful. Of note- since the 3 tissues are 'paired', ie come from the same animal, i was also considering a paired ANOVA of the 3 tissues, reducing, if not eliminating the need to overcome inter-hyb variation, but lack the experience to know what/if there is an appropriate R implementation. Furthermore i have no idea how to use the 2/4 replicates to increase the statistical power with such an approach. Any guidance appreciated. thanks in advance, Karl #Unnormalized data: > dat <- ReadAffy() > dat.rma <- rma(dat, normalize=FALSE) Background correcting Calculating Expression > apply(exprs(dat.rma),2,summary) Tco1A.CEL Tco2A.CEL Tco3B.CEL Tco4B.CEL Tmi1A.CEL Tmi2A.CEL Min. 1.633 1.713 1.869 2.431 2.027 1.736 1st Qu. 2.627 2.751 3.234 3.554 2.831 2.882 Median 3.329 3.320 4.933 4.952 3.433 3.588 Mean 4.153 3.902 5.572 5.741 4.330 4.261 3rd Qu. 5.147 4.541 7.534 7.556 5.254 5.151 Max. 14.180 13.640 14.370 14.280 14.240 13.880 Tmi3B.CEL Tmi4B.CEL Tsh1A.CEL Tsh2A.CEL Tsh3B.CEL Tsh4B.CEL Min. 1.771 2.575 1.607 1.902 1.771 2.360 1st Qu. 3.280 3.716 2.541 2.850 3.197 3.940 Median 4.986 4.978 3.183 3.357 4.833 5.488 Mean 5.629 5.762 3.983 4.032 5.493 6.092 3rd Qu. 7.606 7.449 4.882 4.629 7.426 7.892 Max. 14.300 14.180 14.070 13.990 14.230 14.340 #RMA normalized data: > eset <- justRMA(filenames=list.celfiles()) Background correcting Normalizing Calculating Expression > apply(exprs(eset),2,summary) Tco1A.CEL Tco2A.CEL Tco3B.CEL Tco4B.CEL Tmi1A.CEL Tmi2A.CEL Min. 1.945 2.077 2.014 2.070 1.970 2.050 1st Qu. 3.394 3.574 3.114 3.135 3.381 3.483 Median 4.469 4.533 4.552 4.435 4.474 4.472 Mean 5.191 5.110 5.221 5.192 5.199 5.133 3rd Qu. 6.539 6.114 6.947 6.866 6.589 6.260 Max. 14.180 14.170 14.120 14.170 14.170 14.180 Tmi3B.CEL Tmi4B.CEL Tsh1A.CEL Tsh2A.CEL Tsh3B.CEL Tsh4B.CEL Min. 1.930 2.056 2.015 2.028 1.815 1.919 1st Qu. 3.106 3.190 3.436 3.524 3.132 3.134 Median 4.520 4.426 4.497 4.484 4.548 4.450 Mean 5.219 5.194 5.203 5.140 5.224 5.198 3rd Qu. 6.949 6.824 6.537 6.241 6.938 6.887 Max. 14.130 14.070 14.180 14.130 14.110 14.150 > sessionInfo() Version 2.3.0 (2006-04-24) i386-pc-mingw32 attached base packages: [1] "tools" "methods" "stats" "graphics" "grDevices" "utils" "datasets" "base" other attached packages: affyPLM gcrma matchprobes affydata mouse4302cdf vsn limma affy affyio Biobase "1.8.0" "2.4.1" "1.4.0" "1.8.0" "1.12.0" "1.10.0" "2.7.3" "1.10.0" "1.0.0" "1.10.0" -- Karl Brand <k.brand at="" erasmusmc.nl=""> Department of Cell Biology and Genetics Erasmus MC Dr Molewaterplein 50 3015 GE Rotterdam lab +31 (0)10 408 7409 fax +31 (0)10 408 9468
affy affydata vsn limma gcrma matchprobes affyPLM affyio affy affydata vsn limma gcrma • 609 views
ADD COMMENT

Login before adding your answer.

Traffic: 248 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