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Application of scaling factors in simultaneous modeling of microarray data from diverse chips.

ABSTRACT: Microarrays have been utilized in many biological, physiological and pharmacological studies as a high-throughput genomic technique. Several generations of Affymetrix GeneChip microarrays are widely used in gene expression studies. However, differences in intensities of signals for different probe sets that represent the same gene on various types of Affymetrix chips make comparison of datasets complicated.A power coefficient scaling factor was applied in the pharmacokinetic/pharmacodynamic (PK/PD) modeling to account for differences in probe set sensitivities (i.e., signal intensities). Microarray data from muscle and liver following methylprednisolone 50 mg/kg i.v. bolus and 0.3 mg/kg/h infusion regimens were taken as an exemplar.The scaling factor applied to the pharmacodynamic output function was used to solve the problem of intensity differences between probe sets. This approach yielded consistent pharmacodynamic parameters for the applied models.Modeling of pharmacodynamic/pharmacogenomic (PD/PG) data from diverse chips should be performed with caution due to differential probe set intensities. In such circumstances, a power scaling factor can be applied in the modeling.

SUBMITTER: Yao Z

PROVIDER: S-EPMC4181592 | biostudies-literature | 2007 Apr

REPOSITORIES: biostudies-literature

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Application of scaling factors in simultaneous modeling of microarray data from diverse chips.

Yao Zhenling Z Zhao Baiteng B Hoffman Eric P EP Ghimbovschi Svetlana S DuBois Debra C DC Almon Richard R RR Jusko William J WJ

Pharmaceutical research 20070221 4

<h4>Purpose</h4>Microarrays have been utilized in many biological, physiological and pharmacological studies as a high-throughput genomic technique. Several generations of Affymetrix GeneChip microarrays are widely used in gene expression studies. However, differences in intensities of signals for different probe sets that represent the same gene on various types of Affymetrix chips make comparison of datasets complicated.<h4>Materials and methods</h4>A power coefficient scaling factor was appli ...[more]

PMID: 17318415

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Project description:BackgroundPost-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities.ResultsWe conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM) and mismatch (MM) probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values.ConclusionsWashing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental 'washing data set' which might be used by the community for developing amendments of the washing correction.

Project description:Post-hybridization washing is an essential part of microarray experiments. Both, the quality of the experimental washing protocol and the adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities. We conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of washing cycles. Particularly, three Affymetrix GeneChip HGU133plus2 arrays were hybridized and equilibrated for 16 hours in the hybridization oven. For one of the three arrays washing and staining was performed according to the manufacturer’s instructions. For another array the first scan was done immediately after low stringent wash and staining without intermitting stringent washing. Then, the array was stringently washed and scanned in alternating order three more times where each washing step consists of a definite number of washing cycles. The third array was low stringently washed followed by two stringent washing cycles and staining before the first scan. Subsequently it was analogously processed as array A. All three chips are repeatedly processed in a second series of alternating wash/scan-cycles which was performed using the same protocol for each chip as in the first series as described above. As in the first series the arrays were also stained a second time to compensate for any loss of bleached fluorescent dye. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. The washing function allows calibrating probe intensities for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures especially in the limit of small and large values.

Project description:BackgroundMicroarrays are widely used to investigate the blood stage of Plasmodium falciparum infection. Starting with synchronized cells, gene expression levels are continually measured over the 48-hour intra-erythrocytic cycle (IDC). However, the cell population gradually loses synchrony during the experiment. As a result, the microarray measurements are blurred. In this paper, we propose a generalized deconvolution approach to reconstruct the intrinsic expression pattern, and apply it to P. falciparum IDC microarray data.MethodsWe develop a statistical model for the decay of synchrony among cells, and reconstruct the expression pattern through statistical inference. The proposed method can handle microarray measurements with noise and missing data. The original gene expression patterns become more apparent in the reconstructed profiles, making it easier to analyze and interpret the data. We hypothesize that reconstructed gene expression patterns represent better temporally resolved expression profiles that can be probabilistically modeled to match changes in expression level to IDC transitions. In particular, we identify transcriptionally regulated protein kinases putatively involved in regulating the P. falciparum IDC.ResultsBy analyzing publicly available microarray data sets for the P. falciparum IDC, protein kinases are ranked in terms of their likelihood to be involved in regulating transitions between the ring, trophozoite and schizont developmental stages of the P. falciparum IDC. In our theoretical framework, a few protein kinases have high probability rankings, and could potentially be involved in regulating these developmental transitions.ConclusionsThis study proposes a new methodology for extracting intrinsic expression patterns from microarray data. By applying this method to P. falciparum microarray data, several protein kinases are predicted to play a significant role in the P. falciparum IDC. Earlier experiments have indeed confirmed that several of these kinases are involved in this process. Overall, these results indicate that further functional analysis of these additional putative protein kinases may reveal new insights into how the P. falciparum IDC is regulated.

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Application of scaling factors in simultaneous modeling of microarray data from diverse chips.

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Application of scaling factors in simultaneous modeling of microarray data from diverse chips.

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