the impact on quality control on RNAseq experiments

MERINO, GABRIELA; FRESNO, CRISTOBAL; NETTO, FREDERICO; DIAS NETTO, EMMANUEL; PRATO, LAURA; FERNÁNDEZ, ELMER ANDRÉS

San Nicolas

Congreso; XX congreso argentino de bioingenieria; 2015

SABI

Abstract. High throughput mRNA sample sequencing, known as RNA-seq, is as a powerfulapproach to detect differentially expressed genes starting from millions of short sequence reads.Although several workflows have been proposed to analyze RNA-seq data, the experimentquality control as a whole is not usually considered, thus potentially biasing the results and/orcausing information lost. Experiment quality control refers to the analysis of the experimentas a whole, prior to any analysis. It not only inspects the presence of technical effects, but alsoif general biological assumptions are fulfilled. In this sense, multivariate approaches are crucialfor this task.Here, a multivariate approach for quality control in RNA-seq experiments is proposed. Thisapproach uses simple and yet effective well-known statistical methodologies. In particular,Principal Component Analysis was successfully applied over real data to detect and removeoutlier samples. In addition, traditional multivariate exploration tools were applied in order toasses several controls that can help to ensure the results quality. Based on differential expressionand functional enrichment analysis, here is demonstrated that the information retrieval issignificantly enhanced through experiment quality control. Results show that the proposedmultivariate approach increases the information obtained from RNA-seq data after outliersamples removal.