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"Hook"-calibration of GeneChip-microarrays: chip characteristics and expression measures.

Binder H, Krohn K, Preibisch S - Algorithms Mol Biol (2008)

Bottom Line: Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures.It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude.The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany. binder@izbi.uni-leipzig.de

ABSTRACT

Background: Microarray experiments rely on several critical steps that may introduce biases and uncertainty in downstream analyses. These steps include mRNA sample extraction, amplification and labelling, hybridization, and scanning causing chip-specific systematic variations on the raw intensity level. Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures. In the accompanying publication we presented theory and algorithm of the so-called hook method which aims at correcting expression data for systematic biases using a series of new chip characteristics.

Results: In this publication we summarize the essential chip characteristics provided by this method, analyze special benchmark experiments to estimate transcript related expression measures and illustrate the potency of the method to detect and to quantify the quality of a particular hybridization. It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude. In addition, the method calculates a detection call judging the relation between the signal and the detection limit of the particular measurement. The performance of the method in the context of different chip generations and probe set assignments is illustrated. The hook method characterizes the RNA-quality in terms of the 3'/5'-amplification bias and the sample-specific calling rate. We show that the proper judgement of these effects requires the disentanglement of non-specific and specific hybridization which, otherwise, can lead to misinterpretations of expression changes. The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.

Conclusion: The single-chip based hook-method provides accurate expression estimates and chip-summary characteristics using the natural metrics given by the hybridization reaction with the potency to develop new standards for microarray quality control and calibration.

No MeSH data available.


Cross-chip comparison of the expression estimates of four selected probe sets taken from the HG-U133A and HG-U133plus2 arrays (chip data were taken from [10]). Both chip types were hybridized with human reference RNA in five replicates (solid symbols). The open symbols are the log-means over the replicates. Expression measures taken from ref. [10] were compared with the four alternative measures provided by the hook-method. Note the systematic shift of the expression values between both different chip-types which changes sign upon increasing expression value. The chip-type specific bias considerably reduces for the hook-measures. The MMonly-method performes worst among the hook-methods. (see also Figure 7). The Zhang-measures are given in arbitrary units which were scaled for comparison with the hook data.
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Figure 8: Cross-chip comparison of the expression estimates of four selected probe sets taken from the HG-U133A and HG-U133plus2 arrays (chip data were taken from [10]). Both chip types were hybridized with human reference RNA in five replicates (solid symbols). The open symbols are the log-means over the replicates. Expression measures taken from ref. [10] were compared with the four alternative measures provided by the hook-method. Note the systematic shift of the expression values between both different chip-types which changes sign upon increasing expression value. The chip-type specific bias considerably reduces for the hook-measures. The MMonly-method performes worst among the hook-methods. (see also Figure 7). The Zhang-measures are given in arbitrary units which were scaled for comparison with the hook data.

Mentions: Figure 8 compares the expression values of four probe sets selected by Zhang et al. as representative examples ranging from small to high expression levels to illustrate the bias caused by the chip-types (see also Fig. 3 in ref. [10]). Note that the difference between the expression values of both chip-types inverses sign upon increasing expression suggesting that simple re-scaling of the data does not solve the problem.


"Hook"-calibration of GeneChip-microarrays: chip characteristics and expression measures.

Binder H, Krohn K, Preibisch S - Algorithms Mol Biol (2008)

Cross-chip comparison of the expression estimates of four selected probe sets taken from the HG-U133A and HG-U133plus2 arrays (chip data were taken from [10]). Both chip types were hybridized with human reference RNA in five replicates (solid symbols). The open symbols are the log-means over the replicates. Expression measures taken from ref. [10] were compared with the four alternative measures provided by the hook-method. Note the systematic shift of the expression values between both different chip-types which changes sign upon increasing expression value. The chip-type specific bias considerably reduces for the hook-measures. The MMonly-method performes worst among the hook-methods. (see also Figure 7). The Zhang-measures are given in arbitrary units which were scaled for comparison with the hook data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2543012&req=5

Figure 8: Cross-chip comparison of the expression estimates of four selected probe sets taken from the HG-U133A and HG-U133plus2 arrays (chip data were taken from [10]). Both chip types were hybridized with human reference RNA in five replicates (solid symbols). The open symbols are the log-means over the replicates. Expression measures taken from ref. [10] were compared with the four alternative measures provided by the hook-method. Note the systematic shift of the expression values between both different chip-types which changes sign upon increasing expression value. The chip-type specific bias considerably reduces for the hook-measures. The MMonly-method performes worst among the hook-methods. (see also Figure 7). The Zhang-measures are given in arbitrary units which were scaled for comparison with the hook data.
Mentions: Figure 8 compares the expression values of four probe sets selected by Zhang et al. as representative examples ranging from small to high expression levels to illustrate the bias caused by the chip-types (see also Fig. 3 in ref. [10]). Note that the difference between the expression values of both chip-types inverses sign upon increasing expression suggesting that simple re-scaling of the data does not solve the problem.

Bottom Line: Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures.It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude.The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.

View Article: PubMed Central - HTML - PubMed

Affiliation: Interdisciplinary Centre for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany. binder@izbi.uni-leipzig.de

ABSTRACT

Background: Microarray experiments rely on several critical steps that may introduce biases and uncertainty in downstream analyses. These steps include mRNA sample extraction, amplification and labelling, hybridization, and scanning causing chip-specific systematic variations on the raw intensity level. Also the chosen array-type and the up-to-dateness of the genomic information probed on the chip affect the quality of the expression measures. In the accompanying publication we presented theory and algorithm of the so-called hook method which aims at correcting expression data for systematic biases using a series of new chip characteristics.

Results: In this publication we summarize the essential chip characteristics provided by this method, analyze special benchmark experiments to estimate transcript related expression measures and illustrate the potency of the method to detect and to quantify the quality of a particular hybridization. It is shown that our single-chip approach provides expression measures responding linearly on changes of the transcript concentration over three orders of magnitude. In addition, the method calculates a detection call judging the relation between the signal and the detection limit of the particular measurement. The performance of the method in the context of different chip generations and probe set assignments is illustrated. The hook method characterizes the RNA-quality in terms of the 3'/5'-amplification bias and the sample-specific calling rate. We show that the proper judgement of these effects requires the disentanglement of non-specific and specific hybridization which, otherwise, can lead to misinterpretations of expression changes. The consequences of modifying probe/target interactions by either changing the labelling protocol or by substituting RNA by DNA targets are demonstrated.

Conclusion: The single-chip based hook-method provides accurate expression estimates and chip-summary characteristics using the natural metrics given by the hybridization reaction with the potency to develop new standards for microarray quality control and calibration.

No MeSH data available.