Login

Join for Free!
114004 members
table of contents table of contents

An increasingly common application of gene expression profile data is the reverse …


Biology Articles » Bioengineering » Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks » Figures

Figures
- Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks

mcith_btm201f1.gif Figure 1  Flowchart for the comparative analysis of normalization procedures. Arrows in the chart show the flow of the data sets (blue: data set with replicate samples, green: randomized data set, red: B-cell data set).

(Click image to enlarge)

mcith_btm201f2.gif Figure 2  Comparison of Spearman rank correlation between arrays. Each box plot represents a distribution of 45 points of correlation coefficients in (a) replicate data set, and (b) randomized data set. RMA and GCRMA are both significantly deviate from zero in (b), with P-values 3 x 10–17 and 0 (below MATLAB computational precision), respectively.

(Click image to enlarge)

mcith_btm201f3.gif Figure 3 Histogram of the correlation coefficients between gene expression profiles in the data sets produced by four different normalization procedures. X-axis corresponds to the Spearman correlation coefficient of 20 equal-size bins and y-axis corresponds to the count of each bin as a fraction of the total number of all possible pairs.

(Click image to enlarge)

mcith_btm201f4.gif Figure 4 Fitting of the networks connectivity to a power-law distribution.

(Click image to enlarge)

mcith_btm201f5.gif Figure 5 Fraction of the highly correlated gene pairs sharing the same GO biological process. Gene pairs are ranked by mutual information.

(Click image to enlarge)

mcith_btm201f6.gif Figure 6 Likelihood ratio of PPI for various ranges of the gene-pair correlation.

(Click image to enlarge)

mcith_btm201f7.gif Figure 7 A hypothetical case explaining the cause of spurious correlation in GCRMA-normalized data set. (A) Intensity profiles, and (B) intensity ranks, for three probes before (left) and after (right) GSB adjustment. Before GSB adjustment, probe 1 and 2 have the lowest intensities, m = 1, and the lowest ranks in the data set. If probe 1 and 2 were adjusted for the same value due to their similarity in probe affinity, and probe 3 was adjusted for a different value such that the intensity profile crosses over the other two profiles, the expression ranks of p1 and p2 change over the samples. Pairwise rank correlation between p1 and p2 is then tremendously increased. The effect of probe 3 is overly simplified in this hypothetical case and the actual data should contain a combinatorial effect of many other possible probes in the array.

(Click image to enlarge)

mcith_btm201f8.gif Figure 8 Comparison of the GCRMA default (def) normalization procedure, GCRMA alternative (alt) implementation and MAS5 in terms of (A) fitness of network connectivity to a power-law distribution, (B) fraction of gene pairs sharing a common GO biological process annotation and (C) likelihood ratio of PPI.

(Click image to enlarge)

 


rating: 0.00 from 0 votes | updated on: 10 Oct 2008 | views: 11682 |

Rate article:







excellent!bad…