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MicroRNA array normalization: an evaluation using a randomized dataset as the benchmark.

Qin LX, Zhou Q - PLoS ONE (2014)

Bottom Line: The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark.Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%.We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.

ABSTRACT
MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays.

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Related in: MedlinePlus

Density curves for the benchmark data and the test data with or without normalization.Each density curve represents the data for one array. Arrays for endometrial samples are colored in blue, and arrays for ovarian samples in red.
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pone-0098879-g001: Density curves for the benchmark data and the test data with or without normalization.Each density curve represents the data for one array. Arrays for endometrial samples are colored in blue, and arrays for ovarian samples in red.

Mentions: For the purpose of evaluating the effect of normalization on the accuracy of biomarker discovery, we called the randomized data as the benchmark data and the non-randomized data as the test data. Figure 1 shows the effect of normalization on the overall distribution of the test data. Table 1 shows the relative accuracy of biomarker detection in the normalized test data comparing with the benchmark data.


MicroRNA array normalization: an evaluation using a randomized dataset as the benchmark.

Qin LX, Zhou Q - PLoS ONE (2014)

Density curves for the benchmark data and the test data with or without normalization.Each density curve represents the data for one array. Arrays for endometrial samples are colored in blue, and arrays for ovarian samples in red.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4048305&req=5

pone-0098879-g001: Density curves for the benchmark data and the test data with or without normalization.Each density curve represents the data for one array. Arrays for endometrial samples are colored in blue, and arrays for ovarian samples in red.
Mentions: For the purpose of evaluating the effect of normalization on the accuracy of biomarker discovery, we called the randomized data as the benchmark data and the non-randomized data as the test data. Figure 1 shows the effect of normalization on the overall distribution of the test data. Table 1 shows the relative accuracy of biomarker detection in the normalized test data comparing with the benchmark data.

Bottom Line: The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark.Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%.We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.

ABSTRACT
MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays.

Show MeSH
Related in: MedlinePlus