Dear Limma users, Your suggestions,comments, thoughts appreciated on this posting. Here is an experimental design with 2 sites (D or Z), 2 years (08 or 10), 2 seasons (S or F), and treated/control. Samples receiving various combinations of these factors are placed in either Cy5 or Cy3. There are 4 types of controls, one for each season-year: S_08_time0, F_08_time0, S_10_time0, and F_10_time0. Some of control samples are technical duplicates and marked as *_dup. See target file below. One school of thought considers this as a factorial design while another regards it as a nested anova (i.e., all other factors nested under one of the two sites). My belief is I can do neither because of a lack of common reference control across ALL 8 treatment combinations. While the four types of controls are basically the same strain/species of untreated organisms, they came however from different batch of lab organisms in different season/year so are likely different to some extent. Here are my thoughts: 1. analyze each treatment group against its own control separately, namely: D_F_08 and Z_F_08 against F_08_time0; D_S_08 and Z_S_08 against S_08_time0; D_F_10 and Z_F_10 against F_10_time0; D_S_10 and Z_S_10 against S_10_time0 This way, the impact of season, year, and site on treated/control can only be inferred indirectly. 2. technical duplicates in the controls should be excluded, while all treated ones are unique biological samples and should be kept. This would make sample size unbalanced within each of eight treatment groups. Does this mean I have to analyze my data in single channels in order to exclude the technical duplicate controls from either Cy5 or Cy3? The limma user guide (page 88) gave an example involving a composite design (reference and direct comparison) where all treated samples were compared to the pooled control only. The authors used log ratio in that case. 3. How should we account for Cy5 vs Cy3 correlation if we are going to compare a few treated samples from either Cy5 or Cy3 against control samples also from either channel? These samples are not necessarily from the same arrays. Thanks for your feedback! Name Cy3 Cy5 Cy3SampleName Cy5SampleName 330_1_2 D-F-08 F-08_Time0_dup 9_22_08_D_L7 9_22_08_T0_L6 464_1_2 D-F-08 F-08_Time0 9_22_08_D_L6 9_22_08_T0_L3 331_2_2 F-08_Time0 D-F-08 9_22_08_T0_L2 9_22_08_D_L2 422_2_2 F-08_Time0_dup D-F-08 9_22_08_T0_L3 9_22_08_D_L3 423_2_4 F-08_Time0 D-F-08 9_22_08_T0_L6 9_22_08_D_L4 328_2_4 D-F-10 F-10_Time0_dup 9_14_10_D_L1 9_14_10_T0_L1 329_1_4 D-F-10 F-10_Time0 9_14_10_D_L2 9_14_10_T0_L2 330_1_1 D-F-10 F-10_Time0 9_14_10_D_L3 9_14_10_T0_L3 422_1_4 F-10_Time0_dup D-F-10 9_14_10_T0_L2 9_14_10_D_L5 423_1_3 F-10_Time0_dup D-F-10 9_14_10_T0_L3 9_14_10_D_L6 464_2_1 F-10_Time0 D-F-10 9_14_10_T0_L1 9_14_10_D_L4 328_1_1 D-S-08 S-08_Time0_dup 6_3_08_D_L2 6_3_08_T0_L1 464_1_3 D-S-08 S-08_Time0 6_3_08_D_L4 6_3_08_T0_L6 464_1_4 D-S-08 S-08_Time0 6_3_08_D_L5 6_3_08_T0_L8 331_1_4 S-08_Time0 D-S-08 6_3_08_T0_L1 6_3_08_D_L6 423_2_1 S-08_Time0_dup D-S-08 6_3_08_T0_L8 6_3_08_D_L8 329_2_2 D-S-10 S-10_Time0_dup 5_25_10_D_L2 5_25_10_T0_L3 330_1_3 D-S-10 S-10_Time0 5_25_10_D_L3 5_25_10_T0_L4 464_1_1 D-S-10 S-10_Time0 5_25_10_D_L1 5_25_10_T0_L1 331_2_4 S-10_Time0_dup D-S-10 5_25_10_T0_L1 5_25_10_D_L4 422_2_3 S-10_Time0 D-S-10 5_25_10_T0_L3 5_25_10_D_L5 423_2_3 S-10_Time0_dup D-S-10 5_25_10_T0_L4 5_25_10_D_L6 331_1_3 F-08_Time0_dup Z-F-08 9_15_08_T0_L1 9_22_08_Z_L4 422_2_1 F-08_Time0 Z-F-08 9_15_08_T0_L2 9_22_08_Z_L5 423_1_2 F-08_Time0 Z-F-08 9_15_08_T0_L3 9_22_08_Z_L7 328_1_3 Z-F-08 F-08_Time0 9_22_08_Z_L1 9_15_08_T0_L1 329_1_2 Z-F-08 F-08_Time0_dup 9_22_08_Z_L2 9_15_08_T0_L2 330_2_1 Z-F-08 F-08_Time0_dup 9_22_08_Z_L3 9_15_08_T0_L3 331_1_2 F-10_Time0_dup Z-F-10 9_7_10_T0_L4 9_14_10_Z_L4 422_2_4 F-10_Time0 Z-F-10 9_7_10_T0_L5 9_14_10_Z_L5 423_2_2 F-10_Time0_dup Z-F-10 9_7_10_T0_L6 9_14_10_Z_L6 328_2_1 Z-F-10 F-10_Time0 9_14_10_Z_L1 9_7_10_T0_L4 329_2_3 Z-F-10 F-10_Time0_dup 9_14_10_Z_L2 9_7_10_T0_L5 330_2_4 Z-F-10 F-10_Time0 9_14_10_Z_L3 9_7_10_T0_L6 331_2_1 S-08_Time0_dup Z-S-08 5_27_08_T0_L6 6_3_08_Z_L6 422_1_1 S-08_Time0 Z-S-08 5_27_08_T0_L7 6_3_08_Z_L7 328_2_3 Z-S-08 S-08_Time0 6_3_08_Z_L1 5_27_08_T0_L6 329_1_1 Z-S-08 S-08_Time0_dup 6_3_08_Z_L2 5_27_08_T0_L7 330_1_4 Z-S-08 S-08_Time0 6_3_08_Z_L3 5_27_08_T0_L8 423_1_1 S-10_Time0_dup Z-S-10 5_18_10_T0_L6 5_25_10_Z_L7 464_2_2 S-10_Time0 Z-S-10 5_18_10_T0_L3 5_25_10_Z_L5 464_2_4 S-10_Time0 Z-S-10 5_18_10_T0_L4 5_25_10_Z_L6 328_1_2 Z-S-10 S-10_Time0_dup 5_25_10_Z_L2 5_18_10_T0_L3 329_2_4 Z-S-10 S-10_Time0_dup 5_25_10_Z_L3 5_18_10_T0_L4 330_2_3 Z-S-10 S-10_Time0 5_25_10_Z_L4 5_18_10_T0_L6 -- Rong-Lin Wang National Exposure Lab Cincinnati, Ohio, USA [[alternative HTML version deleted]]

If you're not sure how to proceed with the full experiment, I recommend that you start with a reduced design and figure out how to model it, and then progressively add complexity little by little. For your data, I would start by ignoring site, year, and season, and just figure out how to do a simple comparison of treatment vs control while dealing with the dye effects. Once you have a reasonable design for that, you can start figuring out how to add in the other experimental variables. Unfortunately I can't give you any more specific advice because I have no experience with modelling two-color arrays with limma. -Ryan Thompson On 02/14/2013 07:39 PM, RLW wrote: > Dear Limma users, > > Your suggestions,comments, thoughts appreciated on this posting. > > Here is an experimental design with 2 sites (D or Z), 2 years (08 or 10), 2 > seasons (S or F), and treated/control. Samples receiving various > combinations of these factors are placed in either Cy5 or Cy3. There are 4 > types of controls, one for each season-year: S_08_time0, F_08_time0, > S_10_time0, and F_10_time0. Some of control samples are technical > duplicates and marked as *_dup. See target file below. > > One school of thought considers this as a factorial design while another > regards it as a nested anova (i.e., all other factors nested under one of > the two sites). My belief is I can do neither because of a lack of common > reference control across ALL 8 treatment combinations. While the four > types of controls are basically the same strain/species of untreated > organisms, they came however from different batch of lab organisms in > different season/year so are likely different to some extent. > > Here are my thoughts: > 1. analyze each treatment group against its own control separately, namely: > D_F_08 and Z_F_08 against F_08_time0; > D_S_08 and Z_S_08 against S_08_time0; > D_F_10 and Z_F_10 against F_10_time0; > D_S_10 and Z_S_10 against S_10_time0 > > This way, the impact of season, year, and site on treated/control can only > be inferred indirectly. > > 2. technical duplicates in the controls should be excluded, while all > treated ones are unique biological samples and should be kept. This would > make sample size unbalanced within each of eight treatment groups. Does > this mean I have to analyze my data in single channels in order to exclude > the technical duplicate controls from either Cy5 or Cy3? The limma user > guide (page 88) gave an example involving a composite design (reference and > direct comparison) where all treated samples were compared to the pooled > control only. The authors used log ratio in that case. > > 3. How should we account for Cy5 vs Cy3 correlation if we are going to > compare a few treated samples from either Cy5 or Cy3 against control > samples also from either channel? These samples are not necessarily from > the same arrays. > > Thanks for your feedback! > > > Name Cy3 Cy5 Cy3SampleName Cy5SampleName > 330_1_2 D-F-08 F-08_Time0_dup 9_22_08_D_L7 9_22_08_T0_L6 > 464_1_2 D-F-08 F-08_Time0 9_22_08_D_L6 9_22_08_T0_L3 > 331_2_2 F-08_Time0 D-F-08 9_22_08_T0_L2 9_22_08_D_L2 > 422_2_2 F-08_Time0_dup D-F-08 9_22_08_T0_L3 9_22_08_D_L3 > 423_2_4 F-08_Time0 D-F-08 9_22_08_T0_L6 9_22_08_D_L4 > 328_2_4 D-F-10 F-10_Time0_dup 9_14_10_D_L1 9_14_10_T0_L1 > 329_1_4 D-F-10 F-10_Time0 9_14_10_D_L2 9_14_10_T0_L2 > 330_1_1 D-F-10 F-10_Time0 9_14_10_D_L3 9_14_10_T0_L3 > 422_1_4 F-10_Time0_dup D-F-10 9_14_10_T0_L2 9_14_10_D_L5 > 423_1_3 F-10_Time0_dup D-F-10 9_14_10_T0_L3 9_14_10_D_L6 > 464_2_1 F-10_Time0 D-F-10 9_14_10_T0_L1 9_14_10_D_L4 > 328_1_1 D-S-08 S-08_Time0_dup 6_3_08_D_L2 6_3_08_T0_L1 > 464_1_3 D-S-08 S-08_Time0 6_3_08_D_L4 6_3_08_T0_L6 > 464_1_4 D-S-08 S-08_Time0 6_3_08_D_L5 6_3_08_T0_L8 > 331_1_4 S-08_Time0 D-S-08 6_3_08_T0_L1 6_3_08_D_L6 > 423_2_1 S-08_Time0_dup D-S-08 6_3_08_T0_L8 6_3_08_D_L8 > 329_2_2 D-S-10 S-10_Time0_dup 5_25_10_D_L2 5_25_10_T0_L3 > 330_1_3 D-S-10 S-10_Time0 5_25_10_D_L3 5_25_10_T0_L4 > 464_1_1 D-S-10 S-10_Time0 5_25_10_D_L1 5_25_10_T0_L1 > 331_2_4 S-10_Time0_dup D-S-10 5_25_10_T0_L1 5_25_10_D_L4 > 422_2_3 S-10_Time0 D-S-10 5_25_10_T0_L3 5_25_10_D_L5 > 423_2_3 S-10_Time0_dup D-S-10 5_25_10_T0_L4 5_25_10_D_L6 > 331_1_3 F-08_Time0_dup Z-F-08 9_15_08_T0_L1 9_22_08_Z_L4 > 422_2_1 F-08_Time0 Z-F-08 9_15_08_T0_L2 9_22_08_Z_L5 > 423_1_2 F-08_Time0 Z-F-08 9_15_08_T0_L3 9_22_08_Z_L7 > 328_1_3 Z-F-08 F-08_Time0 9_22_08_Z_L1 9_15_08_T0_L1 > 329_1_2 Z-F-08 F-08_Time0_dup 9_22_08_Z_L2 9_15_08_T0_L2 > 330_2_1 Z-F-08 F-08_Time0_dup 9_22_08_Z_L3 9_15_08_T0_L3 > 331_1_2 F-10_Time0_dup Z-F-10 9_7_10_T0_L4 9_14_10_Z_L4 > 422_2_4 F-10_Time0 Z-F-10 9_7_10_T0_L5 9_14_10_Z_L5 > 423_2_2 F-10_Time0_dup Z-F-10 9_7_10_T0_L6 9_14_10_Z_L6 > 328_2_1 Z-F-10 F-10_Time0 9_14_10_Z_L1 9_7_10_T0_L4 > 329_2_3 Z-F-10 F-10_Time0_dup 9_14_10_Z_L2 9_7_10_T0_L5 > 330_2_4 Z-F-10 F-10_Time0 9_14_10_Z_L3 9_7_10_T0_L6 > 331_2_1 S-08_Time0_dup Z-S-08 5_27_08_T0_L6 6_3_08_Z_L6 > 422_1_1 S-08_Time0 Z-S-08 5_27_08_T0_L7 6_3_08_Z_L7 > 328_2_3 Z-S-08 S-08_Time0 6_3_08_Z_L1 5_27_08_T0_L6 > 329_1_1 Z-S-08 S-08_Time0_dup 6_3_08_Z_L2 5_27_08_T0_L7 > 330_1_4 Z-S-08 S-08_Time0 6_3_08_Z_L3 5_27_08_T0_L8 > 423_1_1 S-10_Time0_dup Z-S-10 5_18_10_T0_L6 5_25_10_Z_L7 > 464_2_2 S-10_Time0 Z-S-10 5_18_10_T0_L3 5_25_10_Z_L5 > 464_2_4 S-10_Time0 Z-S-10 5_18_10_T0_L4 5_25_10_Z_L6 > 328_1_2 Z-S-10 S-10_Time0_dup 5_25_10_Z_L2 5_18_10_T0_L3 > 329_2_4 Z-S-10 S-10_Time0_dup 5_25_10_Z_L3 5_18_10_T0_L4 > 330_2_3 Z-S-10 S-10_Time0 5_25_10_Z_L4 5_18_10_T0_L6 >