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