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A two-stage normalization method for robust differential expression analysis in microarray experiments.

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posted on 24.05.2021, 15:03 by Shirin Manafi
In this research, we introduce an approach to improve the reliability of genetic data analysis. Consistency of the results obtained from microarray data analysis strongly relies on elimination of non-biological variations during data normalization process. Instability in Housekeeping Gene (HKG) expression after performing common normalization methods might be an indication of inefficiency potentially resulting in sampling bias in differential expression analysis. This research aims to reduce the sampling bias in microarray experiments proposing a two-stage normalization algorithm. Proposed approach consists of non-linear Quantile normalization at the first stage and linear HKG based normalization at the second stage. We tested the efficiency of the two-stage normalization method using publicly available microarray datasets obtained from the experiments mainly in the field of reproductive biology. Results show that combined Robust Multiarray Average (RMA) and HKG normalization method reduces the sampling bias in experiments when variations in HKG expression is observed after RMA normalization.



Master of Applied Science


Mechanical and Industrial Engineering

Granting Institution

Ryerson University

LAC Thesis Type


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Mechanical and Industrial Engineering (Theses)