Question: Design matrix for time course analysis with maSigPro
0
9.4 years ago by
Matthias Boeck10 wrote:
Hello, I'm working on the analysis of time series data (MAS5) which consists of two experiments (expA, expB) on two cell lines (clA, clB) (but a similar if not same behavior is expected and therefore they might be used as replicates). Each experiment consists of four measurements at different points in time (6h, 24h, 72h and 144h) and for each of this measurements a control exits too. The controls are cell cultures of the same cell line which are untreated but still can show some activity. At the moment I try to find the differences and especially similarities between the two experiments in their reaction on the treatments and wanted to use maSigPro (if you have another suggestion I would be glad for any further advice). I already did some calculations with the package but I'm not sure if I got the design of the design matrix right and maybe you could be so kind to take a look at my matrix. Replicates are within the experiments and I used four dummy variables for the different experiments and cell lines: Time Replicate Control expB_clA expB_clB expA_clA expA_clB clA_6hr_expA_ctr 6 1 1 0 0 0 0 clA_6hr_expA 6 2 0 0 0 1 0 clA_24hr_expA_ctr 24 3 1 0 0 0 0 clA_24hr_expA 24 4 0 0 0 1 0 clA_day3_expA_ctr 72 5 1 0 0 0 0 clA_day3_expA 72 6 0 0 0 1 0 clA_day6_expA_ctrl 144 7 1 0 0 0 0 clA_day6_expA 144 8 0 0 0 1 0 clB_6hr_expA_ctr 6 1 1 0 0 0 0 clB_6hr_expA 6 2 0 0 0 0 1 clB_24hr_expA_ctr 24 3 1 0 0 0 0 clB_24hr_expA 24 4 0 0 0 0 1 clB_day3_expA_ctr 72 5 1 0 0 0 0 clB_day3_expA 72 6 0 0 0 0 1 clB_day6_expA_ctr 144 7 1 0 0 0 0 clB_day6_expA 144 8 0 0 0 0 1 clA_6hr_expB_ctr 6 9 1 0 0 0 0 clA_6hr_expB 6 10 0 1 0 0 0 clA_24hr_expB_ctr 24 11 1 0 0 0 0 clA_24hr_expB 24 12 0 1 0 0 0 clA_day3_expB_ctr 72 13 1 0 0 0 0 clA_day3_expB 72 14 0 1 0 0 0 clA_day6_expB_ctr 144 15 1 0 0 0 0 clA_day6_expB 144 16 0 1 0 0 0 clB_6hr_expB_ctr 6 9 1 0 0 0 0 clB_6hr_expB 6 10 0 0 1 0 0 clB_24hr_expB_ctr 24 11 1 0 0 0 0 clB_24hr_expB 24 12 0 0 1 0 0 clB_day3_expB_ctr 72 13 1 0 0 0 0 clB_day3_expB 72 14 0 0 1 0 0 clB_day6_expB_ctr 144 15 1 0 0 0 0 clB_day6_expB 144 16 0 0 1 0 0 By using this design I end up with about 1179 probes after the first regression step (p.vector() with q-value of 0.0001). I'm not sure if this is a realistic amount or if it is because of the design or the lack of further replicates (array quality checks have already been performed on the data). Would a non specific filtering make sense before the analysis? I also considered changing the replicates column and grouped the controls according to the cell lines but this didn't seem to alter the results. Does the algorithm take the mean/median over all given controls without considering the replicate grouping? Or could this be a hint that the controls are quite similar and could also be combined? If the controls are grouped together in the replicates, is maSigPro taking the median over those for the calculation or is this just for the see.genes() visualization? I'm sorry for all these questions but I haven't worked before with the time series packages in R and I'm not sure if I use the methods correctly. I would be glad for any help! Best wishes, Matthias
glad masigpro • 755 views
modified 9.4 years ago by Wolfgang Huber13k • written 9.4 years ago by Matthias Boeck10
Answer: Design matrix for time course analysis with maSigPro
0
9.4 years ago by
EMBL European Molecular Biology Laboratory
Wolfgang Huber13k wrote:
Hi Matthias as a comment on a slightly generic level, with time course data (as otherwise) I have often found it useful to explore the data using heatmaps, clustering, parallel coordinate plots (see e.g. Fig. 4 in PMID 18615017) before embarking on formal testing. Best wishes Wolfgang On 27/05/10 12:04, Matthias Boeck wrote: > Hello, > > I'm working on the analysis of time series data (MAS5) which consists of > two experiments (expA, expB) on two cell lines (clA, clB) (but a similar > if not same behavior is expected and therefore they might be used as > replicates). Each experiment consists of four measurements at different > points in time (6h, 24h, 72h and 144h) and for each of this measurements > a control exits too. The controls are cell cultures of the same cell > line which are untreated but still can show some activity. > > At the moment I try to find the differences and especially similarities > between the two experiments in their reaction on the treatments and > wanted to use maSigPro (if you have another suggestion I would be glad > for any further advice). > I already did some calculations with the package but I'm not sure if I > got the design of the design matrix right and maybe you could be so kind > to take a look at my matrix. Replicates are within the experiments and I > used four dummy variables for the different experiments and cell lines: > > > Time Replicate Control expB_clA expB_clB expA_clA > expA_clB > clA_6hr_expA_ctr 6 1 1 0 0 0 > 0 > clA_6hr_expA 6 2 0 0 0 1 > 0 > clA_24hr_expA_ctr 24 3 1 0 0 0 > 0 > clA_24hr_expA 24 4 0 0 0 1 > 0 > clA_day3_expA_ctr 72 5 1 0 0 0 > 0 > clA_day3_expA 72 6 0 0 0 1 > 0 > clA_day6_expA_ctrl 144 7 1 0 0 0 > 0 > clA_day6_expA 144 8 0 0 0 1 > 0 > clB_6hr_expA_ctr 6 1 1 0 0 0 > 0 > clB_6hr_expA 6 2 0 0 0 0 > 1 > clB_24hr_expA_ctr 24 3 1 0 0 0 > 0 > clB_24hr_expA 24 4 0 0 0 0 > 1 > clB_day3_expA_ctr 72 5 1 0 0 0 > 0 > clB_day3_expA 72 6 0 0 0 0 > 1 > clB_day6_expA_ctr 144 7 1 0 0 0 > 0 > clB_day6_expA 144 8 0 0 0 0 > 1 > clA_6hr_expB_ctr 6 9 1 0 0 0 > 0 > clA_6hr_expB 6 10 0 1 0 0 > 0 > clA_24hr_expB_ctr 24 11 1 0 0 0 > 0 > clA_24hr_expB 24 12 0 1 0 0 > 0 > clA_day3_expB_ctr 72 13 1 0 0 0 > 0 > clA_day3_expB 72 14 0 1 0 0 > 0 > clA_day6_expB_ctr 144 15 1 0 0 0 > 0 > clA_day6_expB 144 16 0 1 0 0 > 0 > clB_6hr_expB_ctr 6 9 1 0 0 0 > 0 > clB_6hr_expB 6 10 0 0 1 0 > 0 > clB_24hr_expB_ctr 24 11 1 0 0 0 > 0 > clB_24hr_expB 24 12 0 0 1 0 > 0 > clB_day3_expB_ctr 72 13 1 0 0 0 > 0 > clB_day3_expB 72 14 0 0 1 0 > 0 > clB_day6_expB_ctr 144 15 1 0 0 0 > 0 > clB_day6_expB 144 16 0 0 1 0 > 0 > > > By using this design I end up with about 1179 probes after the first > regression step (p.vector() with q-value of 0.0001). I'm not sure if > this is a realistic amount or if it is because of the design or the lack > of further replicates (array quality checks have already been performed > on the data). Would a non specific filtering make sense before the > analysis? > I also considered changing the replicates column and grouped the > controls according to the cell lines but this didn't seem to alter the > results. Does the algorithm take the mean/median over all given controls > without considering the replicate grouping? Or could this be a hint that > the controls are quite similar and could also be combined? If the > controls are grouped together in the replicates, is maSigPro taking the > median over those for the calculation or is this just for the > see.genes() visualization? > > > I'm sorry for all these questions but I haven't worked before with the > time series packages in R and I'm not sure if I use the methods > correctly. > I would be glad for any help! > > > Best wishes, > Matthias > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber