Normalization of Large-Scale Transcriptome Data Using Heuristic Methods.

ABSTRACT: In this study, we introduce an artificial intelligent method for addressing the batch effect of a transcriptome data. The method has several clear advantages in comparison with the alternative methods presently in use. Batch effect refers to the discrepancy in gene expression data series, measured under different conditions. While the data from the same batch (measurements performed under the same conditions) are compatible, combining various batches into 1 data set is problematic because of incompatible measurements. Therefore, it is necessary to perform correction of the combined data (normalization), before performing biological analysis. There are numerous methods attempting to correct data set for batch effect. These methods rely on various assumptions regarding the distribution of th

SUBMITTER: Yosef A 

PROVIDER: S-EPMC10068970 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature