Integration of pre-normalized microarray data using quantile correction
نویسندگان
چکیده
An enormous amount of microarray data has been collected and accumulated in public repositories. Although some of the depositions include raw and processed data, significant parts of them include processed data only. If we need to combine multiple datasets for specific purposes, the data should be adjusted prior to use to remove bias between the datasets. We focused on a GeneChip platform and a pre-processing method, RMA, and examined simple quantile correction as the post-processing method for integration. Integration of the data pre-processed by RMA was evaluated using artificial spike-in datasets and real microarray datasets of atopic dermatitis and lung cancer. Studies using the spike-in datasets show that the quantile correction for data integration reduces the data quality at some extent but it should be acceptable level. Studies using the real datasets show that the quantile correction significantly reduces the bias. These results show that the quantile correction is useful for integration of multiple datasets processed by RMA, and encourage effective use of public microarray data.
منابع مشابه
Integration and Reduction of Microarray Gene Expressions Using an Information Theory Approach
The DNA microarray is an important technique that allows researchers to analyze many gene expression data in parallel. Although the data can be more significant if they come out of separate experiments, one of the most challenging phases in the microarray context is the integration of separate expression level datasets that have gathered through different techniques. In this paper, we prese...
متن کاملStatistical Applications in Genetics and Molecular Biology
Normalization of expression levels applied to microarray data can help in reducing measurement error. Different methods, including cyclic loess, quantile normalization and median or mean normalization, have been utilized to normalize microarray data. Although there is considerable literature regarding normalization techniques for mRNA microarray data, there are no publications comparing normali...
متن کاملHow to use the OutlierD Package
It is important to preprocess high-throughput data generated from microarray or mass spectrometry experiments in order to obtain a successful analysis. Outlier detection is an important preprocessing step. For outlier detection, upper and lower fences (Q3+1.5IQR and Q1 − 1.5IQR) of the differences are often used in statistics, where Q1=lower 25% quantile, Q3=upper 25% quantile, and IQR = Q3 − Q...
متن کاملComparison of Affymetrix expression array summarization methods for reproducibility and consistency across studies
Affymetrix gene expression microarray is a popularly used platform for differential analysis. The analysis pipeline includes five steps: background correction, normalization, PM-only correction, and summarization, and differential analysis. Using publicly available microarray data, we compared the performance of five summarization methods: Median, Mean, Median Polish, Robust Linear Model, Li-Wo...
متن کامل