Lag penalized weighted correlation for time series clustering.

ABSTRACT:

Background

The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets generated with high-throughput biological assays, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure.

Results

We propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns time series profiles to identify common temporal patterns. It down-weights aligned profiles base

SUBMITTER: Chandereng T 

PROVIDER: S-EPMC6966853 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature