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Batch effects, artificial sources of variation due to experimental design,are a widespread phenomenon in high throughput data. Therefore, mechanisms fordetection of batch effects are needed requiring comparison of multiple samples.We apply hierarchical clustering (HC) on DNA k-mer counts of multiple RNA-seqderived Fastq files. Ideally, HC generated trees reflect experimental treatmentgroups and thus may indicate experimental effects, but clustering ofpreparation groups indicates the presence of batch effects. In order to providea simple applicable tool we implemented sequential analysis of Fastq reads withlow memory usage in an R package (seqTools) available on Bioconductor. DNAk-mer counts were analysed on 61 Fastq files containing RNA-seq data from twocell types (dermal fibroblasts and Jurkat cells) sequenced on 8 differentIllumina Flowcells. Results: Pairwise comparison of all Flowcells withhierarchical clustering revealed strong Flowcell based tree separation in 6 (21%) and detectable Flowcell based clustering in 17 (60.7 %) of 28 Flowcellcomparisons. In our samples, batch effects were also present in reads mapped tothe human genome. Filtering reads for high quality (Phred >30) did not removethe batch effects. Conclusions: Hierarchical clustering of DNA k-mer countsprovides a quality criterion and an unspecific diagnostic tool for RNA-seqexperiments.

Hierarchical clustering of DNA k-mer counts in RNA-seq fastq files reveals batch effects