  Large-scale proteomic analysis is emerging as a powerful technique in biology
and relies heavily on data acquired by state-of-the-art mass spectrometers. As
with any other field in Systems Biology, computational tools are required to
deal with this ocean of data. iTRAQ (isobaric Tags for Relative and Absolute
quantification) is a technique that allows simultaneous quantification of
proteins from multiple samples. Although iTRAQ data gives useful insights to
the biologist, it is more complex to perform analysis and draw biological
conclusions because of its multi-plexed design. One such problem is to find
proteins that behave in a similar way (i.e. change in abundance) among various
time points since the temporal variations in the proteomics data reveal
important biological information. Distance based methods such as Euclidian
distance or Pearson coefficient, and clustering techniques such as k-mean etc,
are not able to take into account the temporal information of the series. In
this paper, we present an linear-time algorithm for clustering similar patterns
among various iTRAQ time course data irrespective of their absolute values. The
algorithm, referred to as Temporal Pattern Mining(TPM), maps the data from a
Cartesian plane to a discrete binary plane. After the mapping a dynamic
programming technique allows mining of similar data elements that are
temporally closer to each other. The proposed algorithm accurately clusters
iTRAQ data that are temporally closer to each other with more than 99%
accuracy. Experimental results for different problem sizes are analyzed in
terms of quality of clusters, execution time and scalability for large data
sets. An example from our proteomics data is provided at the end to demonstrate
the performance of the algorithm and its ability to cluster temporal series
irrespective of their distance from each other.
