Hi, That sounds great. I am not sure exactly how you can do it and whether it is applicable to the experiment. Could you provide a simple example? The experiment information is below and I am interested in the PC3M vs knockdown comparison Targets file: SlideNumber ArrayNumber FileName Name Cy3 Cy5 1 1 Input/1_1.txt 1_1 Scramble Knockdown 1 2 Input/1_2.txt 1_2 Knockdown PC3M 1 3 Input/1_3.txt 1_3 PNT2 PC3M 1 4 Input/1_4.txt 1_4 Pooled PNT2 2 2 Input/2_2.txt 2_2 PC3M Scramble 2 3 Input/2_3.txt 2_3 PNT2 Scramble 3 1 Input/3_1.txt 3_1 PC3M Pooled 3 2 Input/3_2.txt 3_2 Pooled Knockdown 3 3 Input/3_3.txt 3_3 Scramble Pooled 3 4 Input/3_4.txt 3_4 Knockdown PNT2 PC3M = the control cell line Knockdown = PC3M with an siRNA knockdown vector Scramble = PC3M with a vector with a scrambled sequence PNT2 = Another cell line (not of interest here) Pooled = poll of knockdowns before you get specific clone, intermediate between PCM3 and knockdown - a hetrogenious group (not considered here) > design Knockdown PNT2 Pooled Scramble [1,] 1 0 0 -1 [2,] -1 0 0 0 [3,] 0 -1 0 0 [4,] 0 1 -1 0 [5,] 0 0 0 1 [6,] 0 -1 0 1 [7,] 0 0 1 0 [8,] 1 0 -1 0 [9,] 0 0 1 -1 [10,] -1 1 0 0 Thanks Dan Yannick Wurm wrote: > Hi Dan, > > for this kind of thing, I'll fit another limma model just to obtain > estimates of what needs to be visualized... > In one case, I needed to separately visualize expression levels from > each biological replicate, but variability was such that I had grouped > them together in my model. To estimate expression levels for each > biological replicate, I recreated a targets file, separating each > biological replicate by name. Then calculated a fit, and asked for > contrasts between each sample and one RNA which I chose as reference. > (centering expression levels within each gene afterwards works too) > > Despite a complex design it was thus possible to generate a heatmap > where each of the 8 biological replicated RNAs from 3 different > conditions where represented separately. > > hope this helps, > > yannick > > > > On Nov 10, 2008, at 17:33 , Daniel Brewer wrote: > >> Dear all, >> >> I am doing some work on a two-colour microarray (Agilent) experiment and >> I have used limma to do some differential analysis. The person I am >> doing this work was keen to have a heatmap of the differentially >> expressed genes expression levels. Unfortunately, the design is rather >> complex and random (closer to a loop design than a common reference) so >> its not possible to produce a traditional heatmap. I was wondering if >> anyone had any suggestions of a colourful way to show that the >> expression of the two groups are different? >> >> In particular I was thinking that there must be estimates of the >> expression and error in each group by the linear model, but couldn't >> work out how to find these. >> >> Thanks >> >> Dan -- ************************************************************** Daniel Brewer Institute of Cancer Research Molecular Carcinogenesis MUCRC 15 Cotswold Road Sutton, Surrey SM2 5NG United Kingdom Tel: +44 (0) 20 8722 4109 Fax: +44 (0) 20 8722 4141 Email: daniel.brewer at icr.ac.uk ************************************************************** The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP. This e-mail message is confidential and for use by the a...{{dropped:2}}