Hi I am trying to analyze a data set consisting of data run on two different times a few months a part. The data set consists of 8 groups with 3 biological replicates in each, and Illumina Human WG6 v3 arrays have been used. I am using the probe profile file in the analysis. After the quality control it looks like the data is separated into the different groups (8), but I can also slightly see the arrays separate them self into the two groups based on when they were run. To try to block the effect caused by the two lab periods I thought of using duplicateCorrelation. Unfortunately I can not get it to work this time, This is my design matrix: > designMa S0_s S18_s S1_s S4_s T0_s T18_s T1_s T4_s S_0h 1 0 0 0 0 0 0 0 S_0h 1 0 0 0 0 0 0 0 S_0h 1 0 0 0 0 0 0 0 T_0h 0 0 0 0 1 0 0 0 T_0h 0 0 0 0 1 0 0 0 T_0h 0 0 0 0 1 0 0 0 S_1h 0 0 1 0 0 0 0 0 S_1h 0 0 1 0 0 0 0 0 S_1h 0 0 1 0 0 0 0 0 T_1h 0 0 0 0 0 0 1 0 T_1h 0 0 0 0 0 0 1 0 T_1h 0 0 0 0 0 0 1 0 S_4h 0 0 0 1 0 0 0 0 S_4h 0 0 0 1 0 0 0 0 S_4h 0 0 0 1 0 0 0 0 T_4h 0 0 0 0 0 0 0 1 T_4h 0 0 0 0 0 0 0 1 T_4h 0 0 0 0 0 0 0 1 S_18h 0 1 0 0 0 0 0 0 S_18h 0 1 0 0 0 0 0 0 S_18h 0 1 0 0 0 0 0 0 T_18h 0 0 0 0 0 1 0 0 T_18h 0 0 0 0 0 1 0 0 T_18h 0 0 0 0 0 1 0 0 S0, T0 and S1 are in the first run and the rest in the second. dataSet_Norm_exp_log2_ordnet is my normalized expression data as a matrix and blokk looks like this: > blokk [1] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 > corfit <- duplicateCorrelation(dataSet_Norm_exp_log2_ordnet, design = designMa, ndups = 1, block = as.factor(blokk)) There were 50 or more warnings (use warnings() to see the first 50) > warnings() Warning messages: 1: In sqrt(dfitted.values) ... : NaNs produced 2: In sqrt(dfitted.values) ... : NaNs produced 3: In sqrt(dfitted.values) ... : NaNs produced 4: In sqrt(dfitted.values) ... : NaNs produced 5: In sqrt(dfitted.values) ... : NaNs produced 6: In sqrt(dfitted.values) ... : NaNs produced 7: In sqrt(dfitted.values) ... : NaNs produced 8: In sqrt(dfitted.values) ... : NaNs produced > fitDesMa <- lmFit(dataSet_Norm_exp_log2_ordnet,design = designMa,block = as.factor(blokk),cor = corfit$consensus) Error in chol.default(V) : the leading minor of order 2 is not positive definite > corfit $consensus.correlation [1] 1 $cor [1] 1 $atanh.correlations [1] Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf [39] Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf [77] Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf [115] Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf [153] Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf [191] Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Inf Does any one have any suggestions for why I get all the Inf? Maybe duplicateCorrelation is not the best thing? Regards, Ingrid [[alternative HTML version deleted]]