[HTML][HTML] How to do quantile normalization correctly for gene expression data analyses
Quantile normalization is an important normalization technique commonly used in high-
dimensional data analysis. However, it is susceptible to class-effect proportion effects (the …
dimensional data analysis. However, it is susceptible to class-effect proportion effects (the …
[HTML][HTML] quantro: a data-driven approach to guide the choice of an appropriate normalization method
SC Hicks, RA Irizarry - Genome biology, 2015 - Springer
Normalization is an essential step in the analysis of high-throughput data. Multi-sample
global normalization methods, such as quantile normalization, have been successfully used …
global normalization methods, such as quantile normalization, have been successfully used …
[HTML][HTML] The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis
X Qiu, H Wu, R Hu - BMC bioinformatics, 2013 - Springer
Background Quantile and rank normalizations are two widely used pre-processing
techniques designed to remove technological noise presented in genomic data. Subsequent …
techniques designed to remove technological noise presented in genomic data. Subsequent …
Smooth quantile normalization
Between-sample normalization is a critical step in genomic data analysis to remove
systematic bias and unwanted technical variation in high-throughput data. Global …
systematic bias and unwanted technical variation in high-throughput data. Global …
Removing technical variability in RNA-seq data using conditional quantile normalization
The ability to measure gene expression on a genome-wide scale is one of the most
promising accomplishments in molecular biology. Microarrays, the technology that first …
promising accomplishments in molecular biology. Microarrays, the technology that first …
[HTML][HTML] Iterative rank-order normalization of gene expression microarray data
Background Many gene expression normalization algorithms exist for Affymetrix GeneChip
microarrays. The most popular of these is RMA, primarily due to the precision and low noise …
microarrays. The most popular of these is RMA, primarily due to the precision and low noise …
[HTML][HTML] Normalization methods for the analysis of unbalanced transcriptome data: a review
Dozens of normalization methods for correcting experimental variation and bias in high-
throughput expression data have been developed during the last two decades. Up to 23 …
throughput expression data have been developed during the last two decades. Up to 23 …
[HTML][HTML] YuGene: a simple approach to scale gene expression data derived from different platforms for integrated analyses
Gene expression databases contain invaluable information about a range of cell states, but
the question “Where is my gene of interest expressed?” remains one of the most difficult to …
the question “Where is my gene of interest expressed?” remains one of the most difficult to …
[HTML][HTML] A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data
X Li, GN Brock, EC Rouchka, NGF Cooper, D Wu… - PloS one, 2017 - journals.plos.org
Normalization is an essential step with considerable impact on high-throughput RNA
sequencing (RNA-seq) data analysis. Although there are numerous methods for read count …
sequencing (RNA-seq) data analysis. Although there are numerous methods for read count …
[HTML][HTML] Empirical comparison of cross-platform normalization methods for gene expression data
J Rudy, F Valafar - BMC bioinformatics, 2011 - Springer
Background Simultaneous measurement of gene expression on a genomic scale can be
accomplished using microarray technology or by sequencing based methods. Researchers …
accomplished using microarray technology or by sequencing based methods. Researchers …