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Fig. 3 | BMC Bioinformatics

Fig. 3

From: Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data

Fig. 3

Schematic overview of the approach: short intervals (windows) of the several mass axes are considered as smallest building blocks of the data set and generated from a raw dataframe. Then for each window the (several) hash functions h map into the N hash buckets (colored boxes at the bottom right). These are indexed from \(i=0\) to \(i=N-1\) and the number of windows n within a bucket is counted. Finally, if more than one window is mapped in a given hash bucket, all windows inside the box are considered “true” signal

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BMC Bioinformatics

ISSN: 1471-2105

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