Based on the current published tutorial and the public tidymassshiny 1.0.0 source; checked September 23, 2026.
Generate an overview
Open Overview and click START. Review m/z–RT distribution, missingness by sample/feature, QC RSD, intensity distributions, PCA and sample correlations. Select the relevant group, batch or injection-order coloring and download plots with their settings.
QC RSD describes technical repeatability where suitable quantitative QCs exist. Matrix intensity totals are not automatically raw TIC. Investigate suspicious samples against acquisition records before excluding them.
Remove noisy features
Choose QC and biological-group missingness cutoffs, run the filtering step and inspect its retained feature count and missingness plots. The existing tutorial’s 20% QC / 50% group examples are adjustable choices, not universal cutoffs. Do not activate a QC-dependent condition when quantitative QCs are absent. Confirm how group criteria are combined in the installed module.
Review outlier samples
Generate missingness/PCA displays. By tidymass offers the documented missingness, PC SD, PC MAD and distance-based strategies; By myself permits explicit selections. Review candidate IDs and experimental reasons, then run the exclusion operation. Preserve both the original object and your exclusion decisions.
Impute missing values
Choose a method and inspect its parameter panel. kNN uses neighbors and row/column missingness controls; random forest uses iteration/tree controls; PPCA uses component and convergence settings. maxp in kNN concerns the maximum block size used by the underlying algorithm, not a percentage. Record the seed when provided.
Click START, wait for completion and verify that the intended sample subset was processed. Avoid imputing a wholly unobserved feature as if it were supported quantitative evidence.
Normalize and integrate
Select a method appropriate to the experiment. SVR and LOESS require suitable QCs and acquisition information; PQN, median, mean and total are sample-wise approaches. Review keep_scale, optimization, reference and thread controls. Click Start analysis, then Show plot to inspect before/after results.
Record batch handling separately from normalization. No normalization method can rescue a completely confounded biological-group/batch design. Separately fitted PCAs should not be compared as a single unchanged coordinate system.
Save cleaned objects and diagnostic outputs before proceeding to annotation or statistics.