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Dual Inlet

Source: vignettes/dual_inlet.Rmd
dual_inlet.Rmd
# libraries
library(isoorbi) #load isoorbi R package
library(forcats) #better ordering of factor variables in plots
library(dplyr) # for mutating data frames
library(ggplot2) # for data visualization

A basic data processing example 

# Read .isox test data
df <- 
  system.file("extdata", "testfile_dual_inlet_new.isox", package = "isoorbi") |>
  orbi_read_isox() |> # reads .isox test data
  orbi_simplify_isox() |> # optionally: keeps only most important columns; equivalent to simplify check box in IsoX
  # check for issues
  orbi_flag_satellite_peaks() |> # removes minor signals that were reported by IsoX in the same tolerance window where the peak of interest is
  orbi_flag_weak_isotopocules(min_percent = 10) |> # removes signals of isotopocules that were not detected at least in min_percent scans
  orbi_flag_outliers(agc_fold_cutoff = 2) |> # removes outlying scans that have more than 2 times or less than 1/2 times the average number of ions in the Orbitrap analyzer; another method: agc_window (see function documentation for more details)
  orbi_define_basepeak(basepeak_def = "M0") # sets one isotopocule in the dataset as the base peak (denominator) for ratio calculation

No satellite peaks, no weak isotopocules, a few AGC fold outliers:

df |> orbi_plot_raw_data(isotopocule = "15N", y = tic * it.ms, y_scale = "log")

Define dual inlet blocks 

# define blocks
df_w_blocks <-
  df |>
  # general definition
  orbi_define_blocks_for_dual_inlet(
    ref_block_time.min = 10, # the reference block is 10 min long
    sample_block_time.min = 10, # the sample block is 10 min long
    startup_time.min = 5, # there is 5 min of data before the reference block starts, to stabilize spray conditions
    change_over_time.min = 2, # it takes 2 min to make sure the right solution is measured after switching the valve
    sample_block_name = "sample",
    ref_block_name = "reference"
  ) |> 
  # fine adjustments
  orbi_adjust_block(block = 1, shift_start_time.min = 2) |> # the 1st reference block is shorter by 2 min, cut from the start
  orbi_adjust_block(block = 4, set_start_time.min = 38, set_end_time.min = 44) # the start and end of the 2nd reference block are manually set

# get blocks info
blocks_info <- df_w_blocks |> orbi_get_blocks_info()
blocks_info |> knitr::kable()
filename data_group block sample_name data_type segment start_scan.no end_scan.no start_time.min end_time.min
20230518_05_USGS32_vs_USGS34 1 0 reference startup NA 5 820 0.031 4.985
20230518_05_USGS32_vs_USGS34 2 1 reference unused NA 825 1150 5.015 6.990
20230518_05_USGS32_vs_USGS34 3 1 reference data NA 1155 2465 7.021 14.982
20230518_05_USGS32_vs_USGS34 4 2 sample changeover NA 2470 2795 15.016 16.990
20230518_05_USGS32_vs_USGS34 5 2 sample data NA 2800 4110 17.021 24.982
20230518_05_USGS32_vs_USGS34 6 3 reference changeover NA 4115 4440 25.016 26.991
20230518_05_USGS32_vs_USGS34 7 3 reference data NA 4445 5755 27.022 34.984
20230518_05_USGS32_vs_USGS34 8 4 sample changeover NA 5760 6085 35.017 36.992
20230518_05_USGS32_vs_USGS34 9 4 sample unused NA 6090 6250 37.023 37.995
20230518_05_USGS32_vs_USGS34 10 4 sample data NA 6255 7235 38.025 43.982
20230518_05_USGS32_vs_USGS34 11 4 sample unused NA 7240 7400 44.012 44.984
20230518_05_USGS32_vs_USGS34 12 5 reference changeover NA 7405 7730 45.018 46.994
20230518_05_USGS32_vs_USGS34 13 5 reference data NA 7735 9045 47.024 54.985
20230518_05_USGS32_vs_USGS34 14 6 sample changeover NA 9050 9375 55.019 56.994
20230518_05_USGS32_vs_USGS34 15 6 sample data NA 9380 10690 57.024 64.985
20230518_05_USGS32_vs_USGS34 16 7 reference changeover NA 10695 11020 65.019 66.994
20230518_05_USGS32_vs_USGS34 17 7 reference data NA 11025 12335 67.025 74.985

Raw data plots

Plot 1: default block highlights + outliers 

# ions
df_w_blocks |> 
  orbi_plot_raw_data(
    isotopocules = "15N",
    y = ions.incremental
  )


# ratios - you can see that even the AGC outliers still create decent ratios
df_w_blocks |> 
  orbi_plot_raw_data(
    isotopocules = "15N",
    y = ratio
  )

Plot 2: highlight blocks in data + no outliers 

df_w_blocks |> 
  orbi_plot_raw_data(
    isotopocules = "15N",
    y = ratio,
    color = NULL,
    add_all_blocks = TRUE,
    show_outliers = FALSE
  ) +
  # add other ggplot elements, e.g. more specific axis labels
  labs(x = "time [min]", y = "15N/M0 ratio")

Plot 3: highlight sample blocks on top 

df_w_blocks |> 
  orbi_plot_raw_data(
    isotopocules = "15N",
    y = ratio,
    add_all_blocks = TRUE,
    show_outliers = FALSE,
    color = factor(block)
  ) +
  labs(x = "time [min]", y = "15N/M0 ratio", color = "block #")

Data summaries 

# calculate summary
df_summary <- 
  df_w_blocks |>
  # segment (optional)
  orbi_segment_blocks(into_segments = 3) |>
  # calculate results, including for the unused parts of the data blocks
  orbi_summarize_results(
    ratio_method = "sum",
    include_unused_data = TRUE
  )

Plot 1: ratios summary by block and segment 

# plot all isotopocules using a ggplot from scratch
df_summary |>
  filter(data_type == "data") |>
  mutate(block_seg = sprintf("%s.%s", block, segment) |> fct_inorder()) |>
  # data
  ggplot() +
  aes(
    x = block_seg,
    y = ratio, ymin = ratio - ratio_sem, ymax = ratio + ratio_sem,
    color = sample_name
  ) +
  geom_pointrange() +
  facet_grid(isotopocule ~ ., scales = "free_y") +
  # scales
  scale_color_brewer(palette = "Set1") +
  theme_bw() +
  labs(x = "block.segment", y = "ratio")

Plot 2: ratios with block backgrounds and raw data 

# make a plot for 15N
plot2 <- df_w_blocks |>
  filter(isotopocule == "15N") |>
  mutate(panel = "raw ratios") |>
  # raw data plot
  orbi_plot_raw_data(
    y = ratio,
    color = NULL,
    add_all_blocks = TRUE,
    show_outliers = FALSE
  ) +
   # ratio summary data
  geom_pointrange(
    data = function(df) {
      df_summary |> 
        filter(as.character(isotopocule) == df$isotopocule[1]) |> 
        mutate(panel = "summary")
    },
    map = aes(
      x = mean_time.min, y = ratio, 
      ymin = ratio - ratio_sem, ymax = ratio + ratio_sem,
      shape = sample_name
    ), 
    size = 0.5
  ) +
  facet_grid(panel ~ ., switch = "y") +
  theme(strip.placement = "outside") +
  labs(y = NULL, title = "15N/M0")

plot2

# same but with 18O
plot2 %+% 
  (df_w_blocks |> filter(isotopocule == "18O") |> mutate(panel = "raw ratios")) +
  labs(title = "18O/M0")