Data from: Batch effects in a multi-year sequencing study: false biological trends due to changes in read lengths

Deborah M. Leigh, Heidi E.L. Lischer, Christine Grossen & Lukas F. Keller
High-throughput sequencing is a powerful tool, but suffers biases and errors that must be accounted for to prevent false biological conclusions. Such errors include batch effects, technical errors only present in subsets of data due to procedural changes within a study. If overlooked and multiple batches of data are combined, spurious biological signals can arise, particularly if batches of data are correlated with biological variables. Batch effects can be minimized through randomisation of sample groups...
1 citation reported since publication in 2018.
70 views reported since publication in 2018.

These counts follow the COUNTER Code of Practice, meaning that Internet robots and repeats within a certain time frame are excluded.
What does this mean?
14 downloads reported since publication in 2018.

These counts follow the COUNTER Code of Practice, meaning that Internet robots and repeats within a certain time frame are excluded.
What does this mean?