This is because diffReps merges a lot of sliding windows into one differential

This is because diffreps merges a lot of sliding

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values, diffReps reports less differential sites than DESeq and edgeR. This is because diffReps merges a lot of sliding windows into one differential site if they overlap. To show the sensitivity from another angle, we also illustrate the total size of the genomic regions that are covered by the differential sites in Figs. S4 & S6. In these comparisons, diffReps is consistently more sensitive than the other methods at all p- values. Computational cost and memory usage We recorded the running time and memory usage of diffReps for the two datasets used in this manuscript on a single core node with CPU speed of 2.1GHz. On the in vivo mouse brain data, diffReps finished running in 1h20m with maximum memory usage of 3.2GB; on the ENCODE data, diffReps finished running in 2h30m with maximum memory usage of 2.8GB. Some analyses for the running time and memory usage are as follows: The preparation of bin count data scales linearly with data size (i.e. the number of short reads); The bin scanning and testing time scales linearly with the number of bins which equals genome size times the inverse of window size; Memory usage scales linearly with bin number times the number of replicates. The results conclude that diffReps can work on a typical real-world dataset of mammalian genome size with reasonable running time and memory requirement. References 1. (2011) A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS biology 9: e1001046.
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