MS Data

Loading scripts

devtools::load_all()

Load data

MTandem_obj <- MetaboTandem$new()

MTandem_obj$load_metadata(metadata_file = 'test_data/metadata.csv')
MTandem_obj$metadata
MTandem_obj$get_groups()

MTandem_obj$load_spectra_data('test_data/minimal_test/')

MTandem_obj$centroid_check()

Peak picking

# test_peak_picking(MTandem_obj$data,
#                   mz_range = c(300, 310),
#                   rt_range = c(440, 550),
#                   p_width = c(20, 100),
#                   snt = 3,
#                   noise = 1e6,
#                   cores = 10)

MTandem_obj$apply_peak_picking(method = 'cw',
                               ppm = 25,
                               p_width = c(20, 50),
                               snt = 3,
                               noise = 1e6,
                               prefilter = c(1, 100),
                               mz_diff = 0.001,
                               cores = 10)

# MTandem_obj$apply_peak_refinement(expand_rt = 2,
#                                   expand_mz = 0,
#                                   ppm = 10,
#                                   min_prop = 0.75)

xcms::hasChromPeaks(MTandem_obj$data)

xcms::chromPeaks(MTandem_obj$data) %>% 
  tidyr::as_tibble() %>% 
  dplyr::group_by(sample) %>% 
  dplyr::count() %>% 
  cbind(SampleID = xcms::phenoData(MTandem_obj$data)@data$SampleID) %>% 
  dplyr::ungroup() %>% 
  dplyr::select(SampleID, Num_peaks = n)

Alignment

MTandem_obj$apply_alignment(method = 'pg',
                            group_by = 'treatment',
                            bin_size = 0.25,
                            ppm = 0,
                            min_fraction = 0.9,
                            extra_peaks = 1,
                            smooth = 'loess',
                            span = 1,
                            family = 'gaussian')

treatment_colors <- setNames(ggpubr::get_palette('Dark2',
                                                 length(unique(MTandem_obj$metadata$treatment))),
                             nm = unique(MTandem_obj$metadata$treatment))

col_vector <- treatment_colors[MTandem_obj$metadata$treatment]


xcms::plotAdjustedRtime(MTandem_obj$data, col = col_vector, lwd = 1.5, cex = .7)
legend('topleft', legend = names(treatment_colors), col = treatment_colors, 
       lty = rep(1, length(treatment_colors)), cex = .8, 
       lwd = rep(2, length(treatment_colors)))


MTandem_obj$apply_correspondence(method = 'np',
                                 group_by = 'treatment',
                                 mzvsrtbal = 10,
                                 abs_mz = 0.2,
                                 abs_rt = 15,
                                 knn = 10)

Gap Filling

MTandem_obj$apply_gap_filling(cores = 10)

xcms::hasFilledChromPeaks(MTandem_obj$data)

Stat Analysis

MTandem_obj$extract_abundance_table()
MTandem_obj$filter_and_normalize(min_perc_samples = 100,
                                 filter_method = 'iqr',
                                 perc_remove = 10,
                                 norm_method = 'global',
                                 log_transform = TRUE)

MTandem_obj$calculate_ordination(method = 'nmds',
                                 distance = 'bray')

MTandem_obj$plot_ordination(group_by = 'treatment')