Dear ResearchGate community,
I am fairly new to RNASeq analysis & wanted to ask for your input regarding accounting for different sequencing depth across my samples. I am aware that there are several normalization techniques (e.g. TMM) for this case, however, some of my samples have considerably higher sequencing depths than others. Specifically, my samples (30) range from 20M to 46M reads/sample in sequencing depth (single-end). Can I still normalize this using the tools provided in the various packages (DESeq2, limma etc) or is it preferable to apply random subsampling of the fastq files prior to alignment (I am using kallisto)?
Many thanks in advance!
Best,
Luise
It is preferable to avoid downsampling (randomly reducing the number of reads in some samples).
Normalization techniques like DESeq2, edgeR, and limma are preferred for handling differences in sequencing depth across samples, preserving the maximum amount of information. Downsampling, on the other hand, can be used to equalize sequencing depth across all samples but may reduce statistical power, especially in samples with lower read depths. Downsampling may be considered if computational capacity is limited, data size is significant, or extreme cases have vast differences in read depths. In most cases, normalization methods are preferable.
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