What is it about?
We were interested in assessing the performance of 20 different microarrays background correction and gene expression data normalisation arrangements from R software “linear models for microarray and RNA-seq data analysis” package, by comparing the number of differentially expressed genes detected by our previous developed custom microarray designs and RNA-seq platform.
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Why is it important?
Processed background subtraction and gene expression data normalization arrangement (BS+DN) claimed to improve the agreement (sensitivity) between microarrays and RNA-seq in calling DEGs; quantile normalisation procedure applied to our processed custom microarray designs has been recorded as exhibiting the best sensitivity (p-value<0.05), since discriminates the highest number of DEGs in agreement with RNA-seq as opposed to the others analysed microarray gene expression data normalisation systems.
Perspectives
Our findings confirmed the pre-eminence of data pre-processing procedure in microarray gene expression profiling analysis according a priority to data normalisation procedure and suggested the stability of quantile normalisation system with respect to the others processed normalisation arrangements in the present executed gene expression comparative study.
Ph.D Dago Dougba Noel
Universite Peleforo Gon Coulibaly
Read the Original
This page is a summary of: RNA-Seq Evaluating Several Custom Microarrays Background Correction and Gene Expression Data Normalization Systems, Biotechnology Journal International, January 2017, Sciencedomain International,
DOI: 10.9734/bji/2017/36345.
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