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Table 2 Correlations (log2 fold change) between Illumina and Affymetrix mouse "target matches" for selected normalisations.

From: Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes

   Affymetrix normalisations
Filter (app. N) Illumina normalisations Scale – Avgdiff Quantile-median polish MAS 5.0 Li-Wong RMA GC-RMA vsn
5% (560) Quantile 0.936 0.946 0.936 0.941 0.949 0.949 0.946
  Loess 0.940 0.945 0.944 0.944 0.949 0.949 0.954
  Rank 0.941 0.947 0.944 0.945 0.951 0.951 0.953
  vsn 0.940 0.947 0.945 0.947 0.952 0.952 0.953
10% (1125) Quantile 0.922 0.936 0.920 0.932 0.940 0.944 0.939
  Loess 0.922 0.932 0.925 0.933 0.939 0.943 0.944
  Rank 0.924 0.935 0.925 0.934 0.941 0.944 0.944
  vsn 0.921 0.933 0.926 0.937 0.943 0.946 0.942
25% (2700) Quantile 0.843 0.875 0.836 0.857 0.893 0.887 0.883
  Loess 0.834 0.868 0.834 0.854 0.888 0.884 0.882
  Rank 0.843 0.874 0.838 0.859 0.894 0.887 0.886
  vsn 0.826 0.860 0.844 0.863 0.897 0.890 0.873
50% (4600) Quantile 0.760 0.807 0.596 0.730 0.824 0.825 0.816
  Loess 0.746 0.800 0.596 0.725 0.818 0.823 0.813
  Rank 0.759 0.805 0.598 0.731 0.825 0.825 0.817
  vsn 0.724 0.766 0.607 0.728 0.807 0.814 0.780
100% (10018) Quantile 0.666 0.732 0.289 0.428 0.732 0.739 0.735
  Loess 0.645 0.722 0.285 0.421 0.724 0.735 0.730
  Rank 0.653 0.721 0.286 0.422 0.723 0.731 0.727
  vsn 0.590 0.647 0.280 0.402 0.670 0.674 0.656
  1. The percentages indicate the percentile cut-off for the intensity on both platforms as well as an approximate number of matches (app. N) selected (normalisation-dependent).