This allows the user to judge which Raw files have similar corrected RT's (i.e. where aligned successfully). If there are clear sub-clusters, it might be worth introducing artifical fractions into MaxQuant, to avoid ID-transfer between these clusters (use the MBR-Align and MBR-ID-Transfer metrics to support the decision).
RTalignmentTree(df_evd, col_fraction = c())Evidence table containing calibrated retention times and sequence information.
Empty vector or 1-values vector giving the name of the fraction column (if existing)
ggplot object containing the correlation tree
If the input contains fractions, leaf nodes will be colored accordingly. Distinct sub-clusters should have their own color. If not, MaxQuant's fraction settings should be optimized. Note that introducing fractions in MaxQuant will naturally lead to a clustering here (it's somewhat circular).