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Legends to Supplementary Material
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Supplementary Figure 1. Similarity of samples of distinct tissues within each
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normal tissue expression data set as shown by hierarchical clustering.
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The basis of these clustered heatmap is an expression compendium of 105 genes
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selected without knowledge of class/tissue membership, only by usage of statistics of
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a gene’s expression across samples. A) Hierarchical clustering of 10 samples of the
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Su et al. data set. Samples cluster by tissue type. B) Hierarchical clustering of 25
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samples of the Roth et al. data set. Samples cluster by tissue type.
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Supplementary Figure 2. Similarity of samples of different tissues between
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normal tissue expression data sets as investigated by correspondence
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analysis.
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The Pearson correlation coefficients r of a cross-comparison between all samples
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from Su and Roth data sets were calculated. Each cell of the resulting matrix of
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correlation coefficients was transformed appropriately (x = (1+r) / (1-r)), and
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subjected to standard correspondence analysis as implemented in the R package ca. In
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the correspondence analysis biplot shown here, the coordinates of row and column
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objects of the cross-correlation matrix (i.e. the samples in Su et al. and Roth et al. data
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sets) are transformed into the same 3D space. Note, that samples of each data set that
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originate from matching tissue sources are located at similar positions in the plot.
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Supplementary Figure 3. Clustered heatmap of the combined normal tissue
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data set.
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Here we show a heat map clustered on rows and columns by hierarchical clustering
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that is based on the expression data of 105 genes in the 35 normal tissue samples. For
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between-study normalization we mean centered the log-transformed expression
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values. Note, that samples cluster by tissue type and that distinct clusters of genes
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emerge that show characteristic expression in distinct tissues or tissue combinations.
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Supplementary Figure 4. Correspondence analysis to select gene for classifier
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training.
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Here we show the result of a correspondence analysis of the exponentially
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transformed 105-gene-by-5-normal-tissue-centroids data matrix. Note, that some
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genes cluster closely with some tissues while others are not associated with distinct
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tissues. Only the 70 genes closest to distinct tissues were used for training of the
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normal tissue classifier.
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Supplementary Figure 5. Heatmap of normal tissue expression data for the 70
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classifier genes.
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Here we show a heatmap of the combined normal tissue expression data (log
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transformed, mean centered) of the 70 classifier genes that was used for training of the
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classifier. Samples are ordered by tissue type. This is an excerpt from Figure 2.
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Supplementary Figure 6. Heatmap of primary cancer expression data for the 70
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classifier genes.
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Here we show a heatmap of the expression data of the 70 classifier genes in 652
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tumors extracted from the expO expression compendium (log transformed, mean
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centered). Samples are ordered by cancer type. This is an excerpt from Figure 2.
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Supplementary Table 1. Over-represented literature sub-networks among the
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70 classifier genes.
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The networks were derived from a large literature-based gene-to-gene and gene-to-
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compound network that underlies the metacore software from GeneGo (CA, USA).
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Statistical testing for over-representation of this network in the 70-gene lists and for
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overrepresentation of functional categories is performed by hypergeometric testing.
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