1. Quantitative analysis of 2D gels Generalities

2. Applications Mutant / wild type Physiological conditions Tissue specific expression Disease / normal state Drug effects 2 images (or image groups) comparison Expression over time Multiple conditions analysis serial analysis

3. Labelling method quantification Reproducibility of migration matching Image analysis requirements Quality of separation spot detection Signal / noise accuracy

4. 2 images comparison Statistical analysis unusable Only for important quantitative variations Essential to confirm

5. 2 sets comparison Mimimum number of images is 3 Maximum is not limited ! Allows detection of smaller variations T test is allowed

6. Serial analysis Quantitative evolution of each spot Need to group the spots according to their behaviour (clustering) Use of Michael Eisen’s software package (http://rana.lbl.gov/EisenSoftware.htm)  Cluster  TreeView The most frequent question is to find sets of proteins that have correlated expression profiles

7. Results 2212,3203528,2224741,694642,7140445,913959,03617 231,517472--0,33774-2,084134 390,7793280,723411-0,6893360,1814750,690404 497,001287,7193616,67924,1516044,0671756,699134 577,191367,6360051,161962,113,3370058,074404 58112,505184128,83860636,3050438,77255258,723175115,912186 7114,2528329,1929762,958726,1676524,7653210,66988 785,46485,825989-3,8571643,104295,22418 812,3189761,7832232,075521,5362841,212681,83677 8414,15779211,39595613,518489,4532563,96469518,083416 8988,52025647,38788614,655632,085319,74234579,558908 920,8015040,684710,585120,507476-0,53534 943,0349443,3253094,36082,0801322,9527054,779294 1015,06883,5664461,802281,844582,8246052,929602 1034,865,56103614,031846,8258122,24815511,64826 1071,99584-2,4842,0489561,7015952,228122 11180,61926483,05532346,6246852,29427639,87112571,909084 1221,3368961,3545351,716721,1483161,041881,04299 1368,3381767,87714211,9839210,0507966,691096,90404 14712,25065612,12234422,2235213,66894414,5692413,093678 15229,88374425,50693635,9379625,44134818,27773524,516726 155--21,4479620,14662414,261813,915148 157228,105504209,229514222,41184183,07115,83229176,724964 1596,431045,0192228,29384,7526083,181155,813054 1603,155328--3,1574362,1542152,10444 1615,5344961,7981089,303964,6261722,950575,665374 16326,53516813,14643243,8481224,65848414,6759929,318172 17810,2801610,92856716,4661610,2239968,834638,190702 1791,190,6132621,769160,630448-1,022684 1823,8586244,67686711,829364,1550684,6521655,375552 1958,0879044,08742110,634284,096188,5122455,154032 2018,0118726,61787120,0320810,7054927,502398,644818 207--0,637560,350,772870,400582 2144,2577925,90636810,269966,2750364,116284,450706 216--1,266840,97858-0,843622 Making sense of the data

8. Quantitative analysis of 2D gels Practical tips

9. 2 sets comparison Image normalisation to obtain comparable spot volumes Using the matched spots Using a single spot Data analysis Using the analysis program Using Excel

10. Serial analysis Image normalisation  input data Find clusters of genes According to the method, the number of clusters will be fixed from the beginning (K-means) or determined after the analysis (hierarchical clustering)

11. Hierarchical clustering 5 24 13 3 1 4 2 5 The length of the branch = the distance between joined genes or clusters Dendrogram The dendrogram induces a linear ordering of the data points

12. Hierarchical clustering Two parameters must be defined: measures how similar two series of number are. it is based on Pearson correlation coefficient. 1- The similarity between two genes: Centered correlation Uncentered correlation Absolute correlation Euclidean... a matrix of distances between all pairs of items is computed. agglomerative hierarchical clustering is performed by joining by a branch the two closest items. 2- The distance between the new cluster and the others: Average Linkage: distance between cluster centers Single Linkage: distance between closest pair Complete Linkage: distance between farthest pair it is measured by different methods. 3- The weight of each serie: it is possible to give a different weight to a particular experiment.

13. K-means - centroid method iteration = 0 start with random position of K centroids iteration = n iterate until centroids are stable iteration = 1 move centroids to center of assign points assign points to centroids

14. K-means - centroid method 1.The user chooses the number of cluster 2.The result varies with each run  compare several runs