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The raw data from microarray were pre-processed through three steps: background correction was performed, the data were then log-transformed to log2 scale, and normalized by the quantile normalization method implemented in the Genome Studio software (Illumina Inc.).
Log2 transformation of the expression value was performed for each probe, and the data was normalized by the quantile normalization method.
Raw probe intensities for all treatment conditions were normalized by the quantile normalization algorithm [ 29] using GenomeStudio software from Illumina and log-2 transformed expression obtained for analyses.
For the RNA-seq datasets in maize and millet, we normalized the FPKM values of genes by the quantile normalization method [ 86].
Prior to any data analysis, individual tumor gene expression levels were log2-transformed to remove skewness and mean-centered within each of the four sites [3] to remove potential site effects not removed by the quantile normalization.
In order to distinguish the two groups under assessment, phenodata was created, and the normalization of genes was made by the quantile normalization method (which makes the expression value to follow the same distribution on all chips), with no production of flags on the RatRef-12 chip type, beadstudio version 3, and by using "Probe ID" as identifier.
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The log-ratio values were normalized between arrays by using the quantile normalization method.
We tested the quantile normalization's effect by applying it before averaging and feature selection.
The parallelization of the quantile normalization is visualized by the flowchart in Figure 1 (a).
Another algorithm to carry out the quantile normalization was introduced by Amaratunga and Cabrera [ 22].
The mouse data are part of a study of AHR-/ animals described elsewhere [ 28], but were pre-processed separately for this study to avoid introducing bias by including AHR-/ mice in the quantile-normalization step of pre-processing.
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