Quantitative analysis of 2D gels Generalities. Applications Mutant / wild type Physiological conditions Tissue specific expression Disease / normal state.

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Presentation transcript:

Quantitative analysis of 2D gels Generalities

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

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

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

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

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

Results 2212, , ,694642, ,913959, , , , , , , , , ,001287, ,67924, , , ,191367, ,161962,113, , , , , , , , , , ,958726, , , ,46485, , ,104295, , , ,075521, ,212681, , , ,518489, , , , , ,655632,085319, , , ,684710,585120, , , , ,36082, , , ,06883, ,802281,844582, , ,865, ,031846, , , , ,4842, , , , , , , , , , , ,716721, ,041881, , , , , ,691096, , , , , , , , , , , , , , , ,261813, , , , ,07115, , ,431045, ,29384, ,181155, , , , , , , ,303964, ,950575, , , , , , , , , , , ,834638, ,190, ,769160, , , , ,829364, , , , , ,634284,096188, , , , , , ,502398, ,637560,350,772870, , , ,269966, ,116284, ,266840, , Making sense of the data

Quantitative analysis of 2D gels Practical tips

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

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)

Hierarchical clustering The length of the branch = the distance between joined genes or clusters Dendrogram The dendrogram induces a linear ordering of the data points

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.

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

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