Dear Bioconductor users, I'm looking to use linear modelling in Limma (all new to me) to identify genes responding differently to infection in two different cattle breeds, and would welcome advice on the correct model to use. Our experiment took eight animals from each of two genetically distinct breeds of cattle varying in susceptibility to a parasite and infected them with the parasite. RNA was isolated pre-infection and at five time-points post-infection and hybridised to two-colour oligo arrays. A common reference RNA obtained by pooling samples from both breeds and all timepoints was labelled with the second dye and hybridised to each array. We're interested in seeing which genes respond differently to infection in the different breeds. I believe the model we should be using is something like Gene expression level = intercept + (breed effect) + (time effect) + (time * breed interaction effect) Assuming this is the appropriate model, I would like to correct for innate pre-infection differences between breeds and effects due to infection that are common to both breeds so that I can find those genes that respond differently to infection between breeds, either at a given time-point or across the entire timecourse. I'd like to know if I ought to set up a different design matrix from that generated automatically using the modelMatrix function on my targets and identifying the reference i.e. modelMatrix(targets,ref="Ref") which generates T0_B1 T0_B2 ... T0_B2 ... T0_B1_A1 1 0 ... 0 ... T0_B1_A2 1 0 ... 0 ... ... T1_B1_A1 0 1 ... 0 ... ... T0_B2_A9 0 0 ... 1 ... .... where T=time, B=breed, A=animal If this is OK, am I right in thinking that contrasts (T1_B1 - T0_B1) - (T1_B2 - T0_B2) will remove both the breed component of the model for each breed (within brackets) and the component due to infection common to both breeds, leaving me with the interaction component? Alternatively, should the design matrix contain additional coefficients to account for time, breed and interaction components? I'd be very grateful for any advice you could offer. Thanks, Stephen ------------------------------------------------------ Dr. Stephen Park Animal Genomics Lab School of Agriculture, Food Science & Vet. Medicine University College Dublin Dublin 4 Ireland Tel: +353 (0)1 716 7767 Fax: +353 (0)1 716 1103 Mob: +353 (0)87 7666850 E-mail: stephen.park at ucd.ie Web: http://animalgenomics.ucd.ie/sdepark/
[Bioc-devel] Appropriate linear model analysis for microarray time course comparison
2 messages · Stephen Park, Sean Davis
Stephen, I think this probably belongs on the bioconductor users list, rather than the development list. I am cross-posting to there. Sean
On 6/1/06 5:28 PM, "Stephen Park" <stephen.park at ucd.ie> wrote:
Dear Bioconductor users, I'm looking to use linear modelling in Limma (all new to me) to identify genes responding differently to infection in two different cattle breeds, and would welcome advice on the correct model to use. Our experiment took eight animals from each of two genetically distinct breeds of cattle varying in susceptibility to a parasite and infected them with the parasite. RNA was isolated pre-infection and at five time-points post-infection and hybridised to two-colour oligo arrays. A common reference RNA obtained by pooling samples from both breeds and all timepoints was labelled with the second dye and hybridised to each array. We're interested in seeing which genes respond differently to infection in the different breeds. I believe the model we should be using is something like Gene expression level = intercept + (breed effect) + (time effect) + (time * breed interaction effect) Assuming this is the appropriate model, I would like to correct for innate pre-infection differences between breeds and effects due to infection that are common to both breeds so that I can find those genes that respond differently to infection between breeds, either at a given time-point or across the entire timecourse. I'd like to know if I ought to set up a different design matrix from that generated automatically using the modelMatrix function on my targets and identifying the reference i.e. modelMatrix(targets,ref="Ref") which generates T0_B1 T0_B2 ... T0_B2 ... T0_B1_A1 1 0 ... 0 ... T0_B1_A2 1 0 ... 0 ... ... T1_B1_A1 0 1 ... 0 ... ... T0_B2_A9 0 0 ... 1 ... .... where T=time, B=breed, A=animal If this is OK, am I right in thinking that contrasts (T1_B1 - T0_B1) - (T1_B2 - T0_B2) will remove both the breed component of the model for each breed (within brackets) and the component due to infection common to both breeds, leaving me with the interaction component? Alternatively, should the design matrix contain additional coefficients to account for time, breed and interaction components? I'd be very grateful for any advice you could offer. Thanks, Stephen ------------------------------------------------------ Dr. Stephen Park Animal Genomics Lab School of Agriculture, Food Science & Vet. Medicine University College Dublin Dublin 4 Ireland Tel: +353 (0)1 716 7767 Fax: +353 (0)1 716 1103 Mob: +353 (0)87 7666850 E-mail: stephen.park at ucd.ie Web: http://animalgenomics.ucd.ie/sdepark/
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