TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput sequencing and microarray technologies generate vast biological data.
• Integrating data from different platforms (e.g., RNA-seq, microarrays) is crucial but lacks adequate biostatistical tools.
• Experiments with similar goals produce comparable outcomes when transformed into platform-independent rankings.

Purpose of the Study:

• To develop and present the R package TopKLists for integrating results from diverse high-throughput experiments.
• To provide tools for statistical inference on top-k lists, stochastic aggregation, and graphical exploration.
• To demonstrate the package's utility by analyzing microRNA data in non-small cell lung cancer.

Main Methods:

• Development of the R package TopKLists.
• Implementation of algorithms for statistical inference on top-k lists and list aggregation.
• Creation of a graphical user interface for algorithm accessibility.
• Application to integrate microRNA data from non-small cell lung cancer across different measurement techniques.

Main Results:

• The TopKLists package facilitates the integration of heterogeneous high-throughput data.
• Statistical inference and stochastic aggregation of ranked gene lists are enabled.
• Graphical exploration tools aid in understanding integrated results.
• Successful application to non-small cell lung cancer microRNA data demonstrated the package's utility.

Conclusions:

• TopKLists addresses the need for biostatistical tools to integrate data from diverse high-throughput technologies.
• The package allows for robust analysis and discovery by combining results into a common format.
• It enhances the utilization of existing biological data resources for scientific research.