Software Developed to Provide Data Analysis for the Life Science and Biotech Industries

With its intuitive user interface, Qlucore Omics Explorer will allow researchers to lessen analysis time and add more creativity to research, due to the system's speed and statistical analysis capabilities.

As it provides users with immediate results, this latest version of Qlucore's software (previously known as Qlucore Gene Expression Explorer) will enable researchers to explore different hypotheses and alternative scenarios within seconds. The software will therefore be beneficial for discovery, as it will allow researchers to examine the data and look for patterns and structures, without requiring one to be an expert in statistics.

"Qlucore Omics Explorer 2.0 has been specially designed to improve everyday efficiency and stimulate creative data analysis,” stated Carl-Johan Ivarsson, president, Qlucore. "As with previous versions of our software, Qlucore Omics Explorer can provide extremely fast analysis of data sets that contain many samples--even those containing more than 100 million data samples--on a regular PC [personal computer].”

The latest enhancements to Qlucore Omics Explorer mean that the software can provide researchers with very powerful (and highly interactive) hierarchal clustering capabilities. The results are presented both as cluster trees and as a heatmap. The heatmap view works in full synchronization with each of the other four plot types (PCA [principal component analysis] sample, PCA variable, scatter and data table).

Researchers can work simultaneously with all five plot types--all of which can be fully synchronized--even if the most typical usage will include a subset. As such, if a researcher uses the software's powerful statistical filters, or wishes to deselect a sample group, all five plots are immediately updated to reflect this new point in the analysis.

Scatter plots, which can be used to plot how one variable is distributed over a group of samples, represent another new plot type in Qlucore Omics Explorer 2.0. With scatter plots, the grouping is freely selected among the annotations available for samples. Scatter plots are good for quality control and for presenting results.

Also new in Qlucore Omics Explorer 2.0 is a powerful log function that allows users to restore their research to an earlier point in their analysis. Since many users often work on parallel projects, it is important to monitor the analysis steps. As a result, Qlucore Omics Explorer 2.0 gives the users full freedom to explore data in their own way, with the log function available to ensure that the user can document precisely which steps have been taken, without being forced to follow a single path.

Qlucore started as a collaborative research project at Lund University (Sweden), supported by researchers at the departments of mathematics and clinical genetics, in order to address the vast amount of high-dimensional data generated with microarray gene-expression analysis. As a result, it was recognized that an interactive scientific software tool was needed to conceptualize the theories evolving from the research collaboration.

The basic concept behind the software is to provide a tool that can take full advantage of the most powerful pattern recognizer that exists--the human brain. The result is a core software engine that visualizes the data in three-dimensions (3D) and will help the user in identifying hidden structures and patterns. Over the last few years, major efforts have been made to optimize the early ideas and to develop a core software engine that is extremely fast, allowing the user to interactively explore and analyze high-dimensional data sets with the use of an ordinary PC.

Qlucore's first product released was the Qlucore Gene Expression Explorer 1.0. The latest version of this software, now called Qlucore Omics Explorer 2.0, was released in May 2009, and represents a major step forward with the added support for hierarchical clustering, scatter plots, and log function. The combination of instant visualization and advanced statistics support gives the user new opportunities. All user actions are at most two mouse clicks away. The company's early customers are chiefly from the life science and biotech industries, but solutions for other industries are currently under development.

One of the early key methods used by Qlucore Gene Expression Explorer to visualize data is dynamic PCA, an innovative way of combining PCA analysis with immediate user interaction. Dynamic PCA is PCA analysis combined with instant user response, a combination that provides an optimal way for users to visualize and analyze a large dataset by presenting a comprehensive view of the data at the same time, since the user is given full freedom to explore all possible versions of the presented view. Later versions combine PCA analysis with other analysis methods such as hierarchical clustering.

Related Links:
Qlucore