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The strategy we adopt is to “impute” the counts of the missing tags. Specifically, for a given library with a low total count, we use similar libraries to conservatively estimate the count of a missing tag. This estimate allows us to include the library and its true tag counts in our analysis. edu/cc/faqs/stat/general/gen25. html). Note that missing value imputation is not new to gene expression analysis. But the estimation methods for DNA microarrays as proposed in Troyanskaya et al. [2001] are not applicable in this case, since missing values in DNA microarrays are due to different types of errors.

The reachability plot is a simple bar plot, which visualizes a cluster ordering of the data set, where each library is represented by one bar. It shows simultaneously: r which clusters are formed by which points (easily recognized as “valleys” in the plot), r how dense clusters are in relation to each other (the deeper the “valley”, the denser), r a lower bound on the distance between 2 clusters (tallest bar between two “valleys”), ACM Transactions on Information Systems, Vol. 23, No. 1, January 2005.

Fig. 2. Result of OPTICS after error removal and normalization. a condition relative to a reference condition. These values are typically log transformed, mainly so that equivalent fold changes in either direction (overor under-expression) have the same absolute value (x/y and y/x are very different, whereas log(x/y) = − log(y/x)). In SAGE, tag counts represent absolute number of occurrences of a gene tag, so a log transformation is not necessary. ACM Transactions on Information Systems, Vol. 23, No.

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ACM transactions on information systems (January) by Association for Computing Machinery

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