Easy processing of high-throughput microplate data

From Master Projects
Jump to: navigation, search


Easy processing of high-throughput microplate data
status: finished
Student name: student name::Gerrit-Jan Schutten
Dates
Start start date:=2014/05/01
End end date:=2014/12/20
Supervision
Supervisor: Douwe Molenaar
Thesis: has thesis::Media:Thesis.pdf
Poster: has poster::Media:Posternaam.pdf

Signature supervisor



..................................

Abstract

About Easy processing of high-throughput microplate data


Description

Microplates (also erroneously called microtiter plates) and the spectrophotometers, fluorimeters and microscopes adapted for the microplate format, are convenient tools for miniaturization and parallelization of laboratory assays. They enable the high-throughput generation of data for almost anything that you can measure with these instruments. However, a practical limit in really making this a high-throughput tool is the unavailability of software for general data transformation and statistical analysis. Manufacturers of microplate readers deliver software that cannot cope with the data from other machines, and that have only limited data analysis capabilities for their own machines. For example, the high-throughput assessment of bacterial growth rates is virtually impossible with the current commercial software.

The statistical language R would be optimally suitable to mend this limitation. However, to our surprise, there is no package for general microplate data analysis. Hence, we started writing such a package together with users, with the philosophy to allow easy manipulation and visualization of data from dozens of microplates with a few lines of script. Currently we have version 0.3 of the microplate package.

The task of the prospective student will be to extend the existing code to version 1, publish and test it extensively on available and new data. The package should include routines that tackle specific problems of microplates, like position-dependent effects and their correction. We have ample “real biological” data available to test and show the benefits of the package. Additionally, the student will also be encouraged to do some experiments in the lab if needed for further validation. In this project, you will be able to combine programming skills to solve real biological problems related to data analysis. Your challenge will be to making this tool versatile and easy to use. We expect this package to become highly useful in the cell biology lab.

Employed techniques and requirements

You should have some experience in R-programming. During during this project you will be able to develop/learn the following skills: R-package programming; statistical analysis and modeling of biological data; high-throughput microplate assay techniques.

Type of project, estimated duration

  • Programming (70%) & Experiment (30%)
  • 4-6 Months