MASQOT: a method for cDNA microarray spot quality control

doi:10.1186/1471-2105-6-250

BMC Bioinformatics
Volume 6

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Bylesjö M
Eriksson D
Sjödin A
Sjöström M
Jansson S
Antti H
Trygg J

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Eriksson D
Sjödin A
Sjöström M
Jansson S
Antti H
Trygg J
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Methodology article

MASQOT: a method for cDNA microarray spot quality control

Max Bylesjö¹ , Daniel Eriksson² , Andreas Sjödin³ , Michael Sjöström¹ , Stefan Jansson³ , Henrik Antti¹ and Johan Trygg¹

¹Research group for Chemometrics, Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden

²Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden

³Umeå Plant Science Centre, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden

author email corresponding author email

BMC Bioinformatics 2005, 6:250doi:10.1186/1471-2105-6-250


Published:	13 October 2005

Additional files

Additional File 1:

Definition of the employed spot descriptors. Provides a definition of the employed spot descriptors used to assess the quality of each spot.

Format: PDF Size: 121KB Download file

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Additional File 2:

A list of the employed POP2 slides. Provides a list of the employed POP2 slides.

Format: TXT Size: 1KB Download file

Additional File 3:

Implementation details of the segmentation process. Provides in-depth information regarding the implementation of the seeded region growing (SRG) algorithm.

Format: PDF Size: 161KB Download file

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Additional File 4:

The processed POP2 training data. Provides the processed POP2 training data set, which contains per-spot values of all descriptors employed here.

Format: ZIP Size: 7.1MB Download file

Additional File 5:

The processed POP2 test data. Provides the processed POP2 test data set, which contains per-spot values of all descriptors employed here.

Format: ZIP Size: 7MB Download file

Additional File 6:

Manual quality assessments. Provides a summary of the quality assessments as classified by the three experienced microarray users.

Format: ZIP Size: 193KB Download file

Additional File 7:

Visual representations of the sub-classes of bad spots. Provides images of typical examples of the 4 main sub-classes of bad spots.

Format: PDF Size: 50KB Download file

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Additional File 8:

Details of the PLS-DA model. Provides details and statistics from the utilized PLS-DA model.

Format: PDF Size: 132KB Download file

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Additional File 9:

The designed subset of the POP2 training data. Provides the processed and filtered POP2 training data set, containing only the spots from the not bad, FI and BI classes which were selected according to the D-optimal design.

Format: ZIP Size: 206KB Download file

Additional File 10:

Description of the utilized D-optimal design. Provides information regarding generation of the D-optimal design used in to select subsets of the three classes.

Format: PDF Size: 75KB Download file

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