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PreP+07: improvements of a user friendly tool to preprocess and analyse microarray data.

Martin-Requena V, Muñoz-Merida A, Claros MG, Trelles O - BMC Bioinformatics (2009)

Bottom Line: PreP+07 implementation provides a high degree of freedom in selecting and organizing a small set of widely used data processing protocols, and can handle many data formats.Its reliability has been proven so that a laboratory researcher can afford a statistical pre-processing of his/her microarray results and obtain a list of differentially expressed genes using PreP+07 without any programming skills.All of this gives support to scientists that have been using previous PreP releases since its first version in 2003.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computer Architecture Department, University of Málaga, Málaga, Spain. vickymr@ac.uma.es

ABSTRACT

Background: Nowadays, microarray gene expression analysis is a widely used technology that scientists handle but whose final interpretation usually requires the participation of a specialist. The need for this participation is due to the requirement of some background in statistics that most users lack or have a very vague notion of. Moreover, programming skills could also be essential to analyse these data. An interactive, easy to use application seems therefore necessary to help researchers to extract full information from data and analyse them in a simple, powerful and confident way.

Results: PreP+07 is a standalone Windows XP application that presents a friendly interface for spot filtration, inter- and intra-slide normalization, duplicate resolution, dye-swapping, error removal and statistical analyses. Additionally, it contains two unique implementation of the procedures - double scan and Supervised Lowess-, a complete set of graphical representations - MA plot, RG plot, QQ plot, PP plot, PN plot - and can deal with many data formats, such as tabulated text, GenePix GPR and ArrayPRO. PreP+07 performance has been compared with the equivalent functions in Bioconductor using a tomato chip with 13056 spots. The number of differentially expressed genes considering p-values coming from the PreP+07 and Bioconductor Limma packages were statistically identical when the data set was only normalized; however, a slight variability was appreciated when the data was both normalized and scaled.

Conclusion: PreP+07 implementation provides a high degree of freedom in selecting and organizing a small set of widely used data processing protocols, and can handle many data formats. Its reliability has been proven so that a laboratory researcher can afford a statistical pre-processing of his/her microarray results and obtain a list of differentially expressed genes using PreP+07 without any programming skills. All of this gives support to scientists that have been using previous PreP releases since its first version in 2003.

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Related in: MedlinePlus

Typical steps in a complete analysis of gene expression. (by row): (1) Filtering empty spots; (2) double scan resolution; (3) Lowess estimation of parameters; (4) applying the Lowess estimation; and (5) Replicates resolution. Inside the box: object diagram of a PreP+07 project, where diamonds represent "is composed of" and circles represent "one or more" (6) final result.
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Figure 1: Typical steps in a complete analysis of gene expression. (by row): (1) Filtering empty spots; (2) double scan resolution; (3) Lowess estimation of parameters; (4) applying the Lowess estimation; and (5) Replicates resolution. Inside the box: object diagram of a PreP+07 project, where diamonds represent "is composed of" and circles represent "one or more" (6) final result.

Mentions: A typical normalization procedure (Figure 1) using PreP+07 starts loading data to which column-functionality is assigned (pre-defined format files can be used to automate this step). Frequently, a row filtering step is needed to remove low quality and empty spots. Several options are available for filtering, such as spot quality, signal presence/absence, fold change, etc. When applicable, a 2 scan resolution can be performed to extend dynamic range of intensity values. Data normality can be visually evaluated using normality graphs and then data normalization can be chosen to correct the deviation (lowess or supervised lowess). Statistical tests can be used to identify differentially expressed genes. Finally, results can be saved in several formats for further processing.


PreP+07: improvements of a user friendly tool to preprocess and analyse microarray data.

Martin-Requena V, Muñoz-Merida A, Claros MG, Trelles O - BMC Bioinformatics (2009)

Typical steps in a complete analysis of gene expression. (by row): (1) Filtering empty spots; (2) double scan resolution; (3) Lowess estimation of parameters; (4) applying the Lowess estimation; and (5) Replicates resolution. Inside the box: object diagram of a PreP+07 project, where diamonds represent "is composed of" and circles represent "one or more" (6) final result.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2657788&req=5

Figure 1: Typical steps in a complete analysis of gene expression. (by row): (1) Filtering empty spots; (2) double scan resolution; (3) Lowess estimation of parameters; (4) applying the Lowess estimation; and (5) Replicates resolution. Inside the box: object diagram of a PreP+07 project, where diamonds represent "is composed of" and circles represent "one or more" (6) final result.
Mentions: A typical normalization procedure (Figure 1) using PreP+07 starts loading data to which column-functionality is assigned (pre-defined format files can be used to automate this step). Frequently, a row filtering step is needed to remove low quality and empty spots. Several options are available for filtering, such as spot quality, signal presence/absence, fold change, etc. When applicable, a 2 scan resolution can be performed to extend dynamic range of intensity values. Data normality can be visually evaluated using normality graphs and then data normalization can be chosen to correct the deviation (lowess or supervised lowess). Statistical tests can be used to identify differentially expressed genes. Finally, results can be saved in several formats for further processing.

Bottom Line: PreP+07 implementation provides a high degree of freedom in selecting and organizing a small set of widely used data processing protocols, and can handle many data formats.Its reliability has been proven so that a laboratory researcher can afford a statistical pre-processing of his/her microarray results and obtain a list of differentially expressed genes using PreP+07 without any programming skills.All of this gives support to scientists that have been using previous PreP releases since its first version in 2003.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computer Architecture Department, University of Málaga, Málaga, Spain. vickymr@ac.uma.es

ABSTRACT

Background: Nowadays, microarray gene expression analysis is a widely used technology that scientists handle but whose final interpretation usually requires the participation of a specialist. The need for this participation is due to the requirement of some background in statistics that most users lack or have a very vague notion of. Moreover, programming skills could also be essential to analyse these data. An interactive, easy to use application seems therefore necessary to help researchers to extract full information from data and analyse them in a simple, powerful and confident way.

Results: PreP+07 is a standalone Windows XP application that presents a friendly interface for spot filtration, inter- and intra-slide normalization, duplicate resolution, dye-swapping, error removal and statistical analyses. Additionally, it contains two unique implementation of the procedures - double scan and Supervised Lowess-, a complete set of graphical representations - MA plot, RG plot, QQ plot, PP plot, PN plot - and can deal with many data formats, such as tabulated text, GenePix GPR and ArrayPRO. PreP+07 performance has been compared with the equivalent functions in Bioconductor using a tomato chip with 13056 spots. The number of differentially expressed genes considering p-values coming from the PreP+07 and Bioconductor Limma packages were statistically identical when the data set was only normalized; however, a slight variability was appreciated when the data was both normalized and scaled.

Conclusion: PreP+07 implementation provides a high degree of freedom in selecting and organizing a small set of widely used data processing protocols, and can handle many data formats. Its reliability has been proven so that a laboratory researcher can afford a statistical pre-processing of his/her microarray results and obtain a list of differentially expressed genes using PreP+07 without any programming skills. All of this gives support to scientists that have been using previous PreP releases since its first version in 2003.

Show MeSH
Related in: MedlinePlus