TY - JOUR
T1 - Smooth quantile normalization
AU - Hicks, Stephanie C.
AU - Okrah, Kwame
AU - Paulson, Joseph N.
AU - Quackenbush, John
AU - Irizarry, Rafael A.
AU - Bravo, Héctor Corrada
N1 - Publisher Copyright:
© The Author 2017. Published by Oxford University Press. All rights reserved.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions.We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods.A software implementation is available from https://github.com/stephaniehicks/qsmooth.
AB - Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions.We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods.A software implementation is available from https://github.com/stephaniehicks/qsmooth.
KW - Global normalization methods
KW - Quantile normalization
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U2 - 10.1093/biostatistics/kxx028
DO - 10.1093/biostatistics/kxx028
M3 - Article
C2 - 29036413
AN - SCOPUS:85029389470
SN - 1465-4644
VL - 19
SP - 185
EP - 198
JO - Biostatistics
JF - Biostatistics
IS - 2
ER -