Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data
This work addresses exploratory data analysis for cell biology researchers, but it is incremental as it applies an existing deep learning method to a specific domain.
The authors tackled the problem of dimensionality reduction for high-content screening cell image data by proposing stacked de-noising auto-encoders, demonstrating superior performance over methods like PCA and Isomap.
High-content screening uses large collections of unlabeled cell image data to reason about genetics or cell biology. Two important tasks are to identify those cells which bear interesting phenotypes, and to identify sub-populations enriched for these phenotypes. This exploratory data analysis usually involves dimensionality reduction followed by clustering, in the hope that clusters represent a phenotype. We propose the use of stacked de-noising auto-encoders to perform dimensionality reduction for high-content screening. We demonstrate the superior performance of our approach over PCA, Local Linear Embedding, Kernel PCA and Isomap.