Quantitative analysis of 2D gels Generalities

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

(http://rana.lbl.gov/EisenSoftware.htm)

 Cluster

 TreeView

The most frequent question is to find sets of

proteins that have correlated expression profiles

Results

22 12,320352 8,222474 1,69464 2,714044 5,91395 9,03617

23 1,517472 - - 0,33774 - 2,084134

39 0,779328 0,723411 - 0,689336 0,181475 0,690404

49 7,00128 7,719361 6,6792 4,151604 4,067175 6,699134

57 7,19136 7,636005 1,16196 2,11 3,337005 8,074404

58 112,505184 128,838606 36,30504 38,772552 58,723175 115,912186

71 14,252832 9,192976 2,95872 6,167652 4,76532 10,66988

78 5,4648 5,825989 - 3,857164 3,10429 5,22418

81 2,318976 1,783223 2,07552 1,536284 1,21268 1,83677

84 14,157792 11,395956 13,51848 9,453256 3,964695 18,083416

89 88,520256 47,387886 14,6556 32,0853 19,742345 79,558908

92 0,801504 0,68471 0,58512 0,507476 - 0,53534

94 3,034944 3,325309 4,3608 2,080132 2,952705 4,779294

101 5,0688 3,566446 1,80228 1,84458 2,824605 2,929602

103 4,86 5,561036 14,03184 6,825812 2,248155 11,64826

107 1,99584 - 2,484 2,048956 1,701595 2,228122

111 80,619264 83,055323 46,62468 52,294276 39,871125 71,909084

122 1,336896 1,354535 1,71672 1,148316 1,04188 1,04299

136 8,338176 7,877142 11,98392 10,050796 6,69109 6,90404

147 12,250656 12,122344 22,22352 13,668944 14,56924 13,093678

152 29,883744 25,506936 35,93796 25,441348 18,277735 24,516726

155 - - 21,44796 20,146624 14,2618 13,915148

157 228,105504 209,229514 222,41184 183,07 115,83229 176,724964

159 6,43104 5,019222 8,2938 4,752608 3,18115 5,813054

160 3,155328 - - 3,157436 2,154215 2,10444

161 5,534496 1,798108 9,30396 4,626172 2,95057 5,665374

163 26,535168 13,146432 43,84812 24,658484 14,67599 29,318172

178 10,28016 10,928567 16,46616 10,223996 8,83463 8,190702

179 1,19 0,613262 1,76916 0,630448 - 1,022684

182 3,858624 4,676867 11,82936 4,155068 4,652165 5,375552

195 8,087904 4,087421 10,63428 4,09618 8,512245 5,154032

201 8,011872 6,617871 20,03208 10,705492 7,50239 8,644818

207 - - 0,63756 0,35 0,77287 0,400582

214 4,257792 5,906368 10,26996 6,275036 4,11628 4,450706

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

5

2 4

1 3

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

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

• it is measured by different methods.

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