Hi, I am using limma for background correction and normalization of two-color arrays. I have encountered a problem that I do not quite understand: Using > RGb<-backgroundCorrect(RG, method="normexp", offset=50) I get the warning message: NaNs produced in: pnorm(q, mean, sd, lower.tail, log.p) However > which(is.nan(RGb$R) == TRUE) > which(is.nan(RGb$R) == TRUE) does not indicate NANs in the result. The numbers pruduced look strange as well: > RGb$G[1:5,] array1 array2 [1,] 2.481014e+79 2.361013e+74 [2,] 2.481014e+79 2.361013e+74 [3,] 2.481014e+79 2.361013e+74 [4,] 2.481014e+79 2.361013e+74 [5,] 2.481014e+79 2.361013e+74 array3 array4 [1,] 2099.87545 1.577257e+79 [2,] 53.75734 1.577257e+79 [3,] 118.30759 1.577257e+79 [4,] 63.07401 1.577257e+79 [5,] 339.25502 1.577257e+79 array5 array6 [1,] 1.153553e+85 1644.20247 [2,] 1.153553e+85 51.08563 [3,] 1.153553e+85 138.33239 [4,] 1.153553e+85 62.48045 [5,] 1.153553e+85 612.04439 My original foreground and background data however look decent (I think) > RG$G[1:5,] array1 array2 [1,] 1827.41975 1744.73457 [2,] 55.50345 55.98621 [3,] 110.92857 128.59155 [4,] 60.77931 62.81119 [5,] 81.86429 128.24161 array3 array4 [1,] 2102.72500 1573.95541 [2,] 55.60690 53.40936 [3,] 120.15714 96.55072 [4,] 66.92357 60.33103 [5,] 341.10458 262.14966 array5 array6 [1,] 2279.76687 1645.17073 [2,] 54.75172 52.96226 [3,] 130.19728 139.30065 [4,] 70.24615 64.44872 [5,] 709.73718 614.01266 > RG$Gb[1:5,] array1 array2 [1,] 44 45 [2,] 44 44 [3,] 44 45 [4,] 45 45 [5,] 44 45 array3 array4 [1,] 48 46 [2,] 47 47 [3,] 47 46 [4,] 49 46 [5,] 47 46 array5 array6 [1,] 47 46 [2,] 47 47 [3,] 46 46 [4,] 47 47 [5,] 46 47 Other subtraction methods (eg. "subtract") work well. I am running limma 2.4.4 on R 2.2.1 Any idea what is going wrong here? Best, Georg

Hi, We just finished an initial inverstigation (50000-gene Affymetrix, 15 cancered people and 10 normal people). 40 genes' RNA expressional levels were found significantly different between the two groups (by two sample t tests, p values corrected). We are now planning a second-stage experiment to validate this finding. We want to do power analysis and sample size calculation, especially want to know how many peoples should be included in the second-stage experiment. Besides the function Power.t.test(), is there any other functions in any packages availabe in bioConductor for this kind of experimantal design problems? Thanks, Qunyuan Zhang