Project description:It is well known, but frequently overlooked, that low- and high-throughput molecular data may contain batch effects, i.e., systematic technical variation. Confounding of experimental batches with the variable(s) of interest is especially concerning, as a batch effect may then be interpreted as a biologically significant finding. An integral step towards reducing false discovery in molecular data analysis includes inspection for batch effects and application of computational tools to reduce this signal if present. In a 30-sample pilot Illumina Infinium HumanMethylation450 (450k array) experiment, we identified two sources of batch effects: array row and chip. Here, we demonstrate two approaches taken to process the 450k data in which an R function, ComBat, was applied to adjust for this non-biological signal. In the “initial analysis”, the application of ComBat to an unbalanced study design resulted in 9,683 and 19,192 significant (FDR<0.05) DNA methylation differences, despite none present prior to correction. Suspicious of this dramatic change, a “revised processing” included changes to our analysis as well as a greater number of samples, and successfully reduced batch effects without introducing false signal. Our work supports conclusions made by an article previously published in this journal: though the ultimate antidote to batch effects is thoughtful study design, every DNA methylation microarray analysis should inspect, assess and, if necessary, adjust for batch effects. The analysis experience presented here can serve as a reminder to the broader community to establish research questions a priori, ensure that they match with study design and encourage communication between technicians and analysts.
Project description:A genome wide association study (GWAS) testing association of parasite genotypes with clinically decreased piperaquine sensitivity phenotype Submission of genotypes from all microarray genotyped samples
Project description:The Cancer Genome Atlas (TCGA) Isoform Expression Quantification Data is the largest ressource of isomiR level sequenced cancer data publicly available. Since the datasets were built up over years and through different contributing institutions, it is not free of batch effects. We evaluated different batch correction approaches to remove batch effects in the data, details of the best performing algorithm and batch variables are included in the supplementary file. Additionally, annotation of the chromosomal end position of each isomiR feature was corrected by the offset of 1 to account for exclusive annotation.
