2 Processing

This vignette describes the main processing function. It assumes you already checked the previous basic steps.

Compare peaks to features

To do so, you will need:

• A previously generated mzmine features’ table

Then, you should be able to run

process_compare_peaks(show_example = TRUE)
#> loading MS data
#> Loading example MS file in memory, doing it on disk will be more efficient
#> loading chromatograms
#> loading name
#> loading feature table
#> preparing features
#> selecting 10 random features for the example
#> ... preparing features
#> ... keeping features above desired intensity only
#> setting joining keys
#> preprocessing chromatograms
#> preprocessing cad chromatograms
#> improving chromatograms
#> baselining chromatograms
#> preprocessing peaks
#> preprocessing cad peaks
#> joining peaks
#> joining within given rt tolerance
#> selecting features outside peaks
#> splitting by file
#> splitting by peak
#> normalizing chromato
#> preparing peaks chromato
#> preparing rt
#> preparing mz
#> processing cad peaks
#> extracting ms chromatograms (longest step)
#> count approx 1 minute per worker per 1000 features (increasing with features number)
#> varies a lot depending on features distribution
#> CAD Peak: 1
#>  ■■■■                               9% |  ETA: 12m
#> CAD Peak: 2
#>  ■■■■■■                            18% |  ETA: 11m
#> CAD Peak: 3
#>  ■■■■■■■■■                         27% |  ETA:  9m
#> CAD Peak: 4
#>  ■■■■■■■■■■■■                      36% |  ETA:  8m
#> CAD Peak: 5
#>  ■■■■■■■■■■■■■■■                   45% |  ETA:  7m
#> CAD Peak: 6
#>  ■■■■■■■■■■■■■■■■■                 55% |  ETA:  6m
#> CAD Peak: 7
#>  ■■■■■■■■■■■■■■■■■■■■              64% |  ETA:  5m
#> CAD Peak: 8
#>  ■■■■■■■■■■■■■■■■■■■■■■■           73% |  ETA:  4m
#> CAD Peak: 9
#>  ■■■■■■■■■■■■■■■■■■■■■■■■■■        82% |  ETA:  2m
#> CAD Peak: 10
#>  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■      91% |  ETA:  1m
#> CAD Peak: 11
#> extracting ms peaks
#> comparing peaks
#> summarizing comparison scores
#> there are 11 scores calculated
#> selecting features with peaks
#> final aesthetics
#> checking export directory
#> exporting

And this basically it! 🚀

Pseudo chromatograms

If you know want to add some cosmetics, and you already have a TIMA annotation table, you can then run:

plots_list <- generate_pseudochromatograms(show_example = TRUE)

The different plots offer the following views:

Taxo (major peaks)

plots_list$plots_1$histograms_taxo_maj

Taxo (minor peaks)

plots_list$plots_1$histograms_taxo_min

Confident unique annotations (major)

plots_list$plots_1$histograms_unique_conf_maj

Confident unique annotations (minor)

plots_list$plots_1$histograms_unique_conf_min

Or getting rid of the chromatogram:

Treemap semi-quantitative (major)

plots_list$treemaps$peaks_maj

Treemap semi-quantitative (minor)

plots_list$treemaps$peaks_min

Or both together for comparison:

Treemap both

plots_list$treemaps$special

Tabular reports

If you prefer tables rather than figures, you can also:

tables_list <- generate_tables(show_example = TRUE)

This will export the table as CSV and/or HTML. A small preview of the HTML table is presented below:

tables_list$pretty_table

For some other available helper functions, we now recommend you to read the next vignette.