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Statistical strategies for microRNAseq batch effect reduction

	author = {Yan Guo and Shilin Zhao and Pei-Fang Su and Chung-I Li and Fei Ye and Charles R. Flynn and Yu Shyr},
	title = {Statistical strategies for microRNAseq batch effect reduction},
	journal = {Translational Cancer Research},
	volume = {3},
	number = {3},
	year = {2021},
	keywords = {},
	abstract = {RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. Among the many difficulties of RNAseq analysis, correctly adjusting for batch effect is a pivotal one for large-scale RNAseq based studies. The batch effect of RNAseq data is most obvious in microRNA (miRNA) sequencing studies. Using real miRNA sequencing (miRNAseq) data, we evaluated several batch removal techniques and discussed their effectiveness. We illustrate that by adjusting for batch effect, more reliable differentially expressed genes can be identified. Our study on batch effect in miRNAseq data can serve as a guideline for future miRNAseq studies that might contain batch effect.},
	issn = {2219-6803},	url = {}