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This package provides an OPSM Miner and information project. Input - Tab delimited ASCII file. Rows are tissues, columns are gene expressions. A row may contain a header column & their could be a header row. In ANY case the number of columns should be equal on all rows. - A header row & column may contain any string, all other values must contain numbers. - Matrix size is T (tissues) x G (gene expressions). Algorithms & flags The algorithm is performing DFS vertical compare, which mean two gene expressions are compared. - "Use in-core dual compare" - pre-build all pairs of expressions vectors, this would take GxGxT size in memory so make sure it can fit the memory. - "Support" - The number of tissues that contains a pattern. Set the application support for this minimal number of tissues you want to contain the pattern you are seeking. Setting a very high number will yield very few patterns, very low number - and the mining might not finish (exponential number of patterns). - "Min Length", "Max Length" - The patterns size you are looking for. Set "Min Length" so the application will not return very short and not interesting patterns. Set "Max Length" to reduce running time. - "Max Errors" - Only for OPSM with Errors. OPSM searches for exact patterns. OPSM with Errors count a tissue even if it contains an X number of genes that does not fit the pattern. - "Max Group Len" - for OPSM with groups, the pattern is compared between groups of genes instead of single genes. For example the pattern will be: (0.4 0.6 0.4) (0.7 0.8) (0.9 0.95) - "Max Layer Diff" - Don't remember... How to used OPSM with Layers algorithm When setting to layers there should be a layers definition file with the same name as the dataset file, appended with ".layers". For example: SaltDat.txt SaltDat.txt.layers File format - each line represent a layer. Each layer is a range of columns: 0-5 6-10 ... ...

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