A typical HCS platform will consist of an automated microscope and a robotic arm for automatic sample preparation. Modern HCS platforms date back to 1997 where they were first introduced by Cellomics, Inc. \cite{taylor2010personal}.
Cell phenotypes are altered by means of gene silencing, a technique used to reduce expression of a particular gene \cite{hannon2002rna}. RNAi involves the delivery of double-stranded RNA molecules that activate the multi-protein RNA induced silencing complex (RISC). When activated, RISC will silence a particular gene. RNAi is a popular means of gene silencing as it’s able to target genes with a high degree of specificity but is also susceptible to off-target effects, where genes are unintentionally silenced \cite{Jackson2010}. Much research goes into the identification and management of off-target effects.
A typical HCS assay is designed by treating cells in a 384-well plate with RNAi or other interfering agents. A number of cells will be designed as control samples. After some period of incubation, images of the cells are obtained and properties of interest are obtained with automated image analysis. Hundreds of parameters can be obtained from these images. The combination of fast imaging and automated analysis is what gives such great power to HCS. Changes in cell phenotype measured in HCS include but certainly aren’t limited to intracellular translational, organelle structure changes, morphology changes and cell sub population distribution \cite{buchser2004assay}.
Modern HCS platforms include bundled software both the storage/retrieval of image data and its subsequent analysis. There also exist free and open source packages available for image analysis. In the following sections we briefly discuss some of the software currently available.
\label{sec:software}
A HCS screening platform will include bundled proprietary software. Examples of such software include Thermo-Scientific’s HCS Studio 2.0, Molecular Devices’ MetaXpress Becton Dickinson’s (BD) Pathway Software and Perkin Elmer’s Opera. Data obtained from a HCS platform need not be used with the bundled proprietary software, with free and open source packages available including CellProfiler\cite{carpenter2006cellprofiler}, PhenoRipper \cite{Rajaram2012} and HCS-Analyzer \cite{ogier2012hcs}. Of particular interest is the PhenoRipper package, which takes a similar approach to our project in that it is built with the purpose of clustering imaging data according to phenotype similarity as opposed to quantifying parameters of interest. CellProfiler and PhenoRipper are examined in the following sections.
CellProfiler \cite{carpenter2006cellprofiler} is a free and open source tool designed to enable biologists with no training in computer programming or image analysis to derive quantitative measurements from HCS experiments. CellProfiler is able to handle the image analysis stage of a HCS experiment from preprocessing of imaging data through to exporting the data in a spreadsheet format for analysis.
CellProfiler also encompasses the companion tool, CellProfiler Analyst \cite{jones2008cellprofiler}. CellProfiller Analyst was built with the goal of enabling researchers to have an easy to use method to visualise and filter the large amount of data generated in a HCS experiment.
CellProfiler already provides an easy to use tool to obtain and process HCS data, however attempting to cluster images using CellProfiler is a more difficult task. Image features must be chosen by the user that will allow a clustering algorithm (clustering algorithms are discussed in detail in Section \ref{sec:cluster}). This is certainly possible with the right domain knowledge and image analysis expertise, but would need to repeated for each dataset a user wishes to cluster. Furthermore, clustering of the image data requires the application of clustering algorithms which are not included in CellProfiler Analyst. The data would need to be analysed externally using R or Python packages.
PhenoRipper \cite{Rajaram2012} is a free and open source tool that groups images obtained from microscopy experiments together according to phenotypic similarity. This is process is relatively easy to carry out and does not require any knowledge of image analysis to use the software. PhenoRipper uses a visual codebooks technique to characterise images (this method is discussed in greater detail in Section \ref{sec:vizcodebooks}).
The images are plotted together onto a two or three dimensional plot by means of dimensionality reduction techniques such as Principal Components Analysis and Multi-Dimension Scaling. While this does offer a way to estimate similarity and dissimilarity of phenotypes, no clustering algorithm is utilised to group the images together. This method also relies heavily upon the human eye, and may be unsuitable for some image datasets measuring in the thousands.