|
RNAseq example
|
Biological replication is much more important than technical replication in giving the experimental design power. Technical replication is still better than no replication.
We need to have appropriate controls to test hypotheses and we need to be aware of confounding variables in our designs.
|
|
AD metabolomics example
|
Due to how LCMS data (proteomics or metabolomics) is generated, we need to consider how our sample running order can be altered to reduce biases in the collected data
Randomised block designs are better for LCMS data because it reduces drift between calibrations. It also reduces the chance of accidental severe imbalances in run order
Multiple QC injections and blanks are used to condition the column prior to the first samplesbeing injected to reduce carryover from previous experiments
|
|
E. coli long-term evolution experiment example
|
Most experiments are not long term compared to this LTEE experiment, however it does allow us to perform additional analyses we are not able to do with shorter term experiments.
For experiments such as this, where measurements are taken over a long time course, making sure all record keeping is up to date stops the data from being redundant.
|