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Gene Expression across Normal and Tumor tissue
 
 
 
 
 
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 • Overview
  - Introduction
- DB description
- Method
- Outlier detection
- Analysis of
   Laboratory Effects

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  Overview

 

O ur database is composed of publicly available data sets in which laboratory effects are known to be strong. We assessed the impact of these effects following Lukk et al. (Lukk et al., 2010)’s analyses. We selected biological groups (with ten replicates or more) which contain at least two different laboratories. For U133A data sets, we selected 5,089 samples of 92 biological groups produced in 93 laboratories.

F or each of the biological groups, we computed the average correlation coefficient between the assays from different laboratories within the same group. We also computed the average correlation coefficient between assays from the same laboratory but belonging to different biological groups. The comparison of the two similarity distributions showed that, as in Lukk et al. reported (Lukk et al., 2010), the biological effects were stronger than the laboratory effects (Figure 1). We got similar results with the U133Plus2 data sets, too.

  Figure 1. Pearson correlation coefficients between samples. Distribution of all pairwise correlations between the samples in the dataset (black), distribution of average similarities between the sample subgroups from different laboratories within the same biological group (green), distribution of average similarities between the sample subgroups from different biological groups within the same laboratory (red).

Data Summary
 • Data sets
  - U133Plus2 : 306
- U133A : 241
 • Samples
  - U133Plus2 : 24,300
- U133A : 16,400
 • Probes
  - U133Plus2 : 54,613
- U133A : 22,215
 
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