ACM Computing Surveys (CSUR)
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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Multi-tag bioimaging systems have recently emerged as powerful tools which provide spatiotemporal localization of several different proteins in the same tissue specimen. The analysis of such multivariate bioimages requires sophisticated analytical methods that extract a molecular signature of various types of cells and assist in analyzing interaction behaviors of functional protein complexes. Previous studies were mainly focused on pixel-level analysis which essentially ignore cellular structures as units which can be crucial when analyzing cancerous cells. In this paper, we present a framework in order to overcome these limitations by incorporating cell-level analysis. We use this framework to identify cell phenotypes based on their high-dimensional co-expression profiles contained within the images generated by the robotically controlled TIS microscope installed at Warwick. The proposed paradigm employs a refined cell segmentation algorithm followed by a locality preserving nonlinear embedding algorithm which is shown to produce significantly better cell classification and phenotype distribution results as compared to its linear counterpart.