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Results supported the correctness of the batch correction operations and cell type assignments from the integrated analysis.
The Empirical Bayes (EB) [ 17] batch correction method was shown an effective way to correct batch effects along with normalization.
The data then underwent batch correction using Partek Genomics Suite software (version 6.5) to correct for any batch effect created by the different NSRL campaigns.
One could also argue that, when combining such a large set of array data collected over different batches, batch correction techniques should be applied.
For the integrated analysis of healthy blood and AML samples, a batch correction between AML and healthy cells was performed on quantile-normalized log2-values, using the parametric ComBat algorithm as implemented in the R package sva50.
Otherwise, the ComBat method70 was used for batch correction on the whole transcriptome of selected cells, and then we repeated steps (2)–(4) using ComBat-corrected data and used new PCs for clustering analysis.
The 125 arrays were adjusted by batch correction method included in Partek Genomics Suite 6.4.
Approximate standard deviation was set so that some lower fold change genes were differentially expressed, so we could test whether small fold changes were still detectable after batch correction.
The median z-score of the raw data was 0.188; after batch correction, the median z-scores were 0.305 for ComBat_p, 0.304 for ComBat_n, 0.298 for PAMR, 0.319 for DWD and 0.268 for Ratio_G.
Results of batch correction.
No further batch correction was performed.
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