Dual Inlet

This is a minimal example for processing .raw data files from a dual inlet experiment

# 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

Load raw file(s)

# Read test data .raw file
raw_file <- orbi_get_example_files("dual_inlet.raw")

data_all <-
  raw_file |>
  orbi_read_raw(include_spectra = c(10, 100)) |>
  orbi_aggregate_raw()
✔ [417ms] orbi_read_raw() read dual_inlet.raw from cache, included the spectra
from 2 scans

✔ [92ms] orbi_aggregate_raw() aggregated file_info (1), scans (12.34k), peaks
(51.77k), and spectra (277) from 1 file using the standard aggregator

# identify nitrate isotopocules
# could come from a tsv, csv, or xlsx spreadsheet instead
isotopocules <- tibble(
  compound = "nitrate",
  isotopocule = c("M0", "15N", "17O", "18O"),
  mass = c(61.9878, 62.9850, 62.9922, 63.9922)
)
data_all <- data_all |>
  orbi_identify_isotopocules(isotopocules) |>
  # disregard unidentified and missing isotopocules
  orbi_filter_isotopocules()
✔ [456ms] orbi_identify_isotopocules() identified 49.35k/51.77k peaks (95%)
representing 100% of the total ion current (TIC) as isotopocules M0, 15N, 17O,
and 18O using the default_tolerance of 1 mmu

✔ [8ms] orbi_filter_isotopocules() removed 2.42k / 51.77k peaks (4.7%) because
they were unidentified peaks (2.42k). Remaining isotopocules: M0, 15N, 17O, and
18O.

Show spectrum

data_all |> orbi_plot_spectra()

Preprocess data

# Preprocess data (this is exactly the same as with an isox file)
df <- 
  data_all |>
  # check for issues
  # removes minor peaks that are in the same mass tolerance window
  # of an isotopocule 
  orbi_flag_satellite_peaks() |> 
  # flag signals of isotopocules that were not detected
  # in all scans
  orbi_flag_weak_isotopocules(min_percent = 100) |> 
  # flags 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_flag_outliers(agc_fold_cutoff = 2) |>
  # sets one isotopocule in the dataset as the base peak
  # (denominator) for ratio calculation
  orbi_define_basepeak(basepeak_def = "M0") 
✔ [376ms] orbi_flag_satellite_peaks() confirmed there are no satellite peaks

✔ [13ms] orbi_flag_weak_isotopocules() confirmed there are no weak
isotopocules: all are detected in at least 100% of scans in each of the 4 data
groups (based on uidx, compound, and isotopocule)

✔ [8ms] orbi_flag_outliers() flagged 14/12338 scans (0.11%) as outliers based
on 2 fold AGC cutoff, i.e. based on scans below 1/2 and above 2 times the
average number of ions tic * it.ms in the Orbitrap analyzer → use
orbi_plot_raw_data(y = tic * it.ms) to visualize them

✔ [438ms] orbi_define_basepeak() set M0 as the ratio denominator and calculated
37.01k ratio values for 3 isotopocules (15N, 17O, and 18O)

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

df |> orbi_plot_raw_data(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(
    # the reference block is 10 min long
    ref_block_time.min = 10, 
    # the sample block is 10 min long
    sample_block_time.min = 10, 
    # there is 5 min of data before the reference block starts, 
    # to stabilize spray conditions
    startup_time.min = 5, 
    # it takes 2 min to make sure the right solution is measured
    # after switching the valve
    change_over_time.min = 2, 
    sample_block_name = "sample",
    ref_block_name = "reference"
  ) |> 
  # fine adjustments
  # the 1st reference block is shorter by 2 min, cut from the start
  orbi_adjust_block(block = 1, shift_start_time.min = 2) |> 
  # the start and end of the 2nd reference block are manually set
  orbi_adjust_block(block = 4, set_start_time.min = 38, set_end_time.min = 44) 
Adding missing grouping variables: `uidx`
✔ [17ms] orbi_define_blocks_for_dual_inlet() identified 8 blocks (4 reference,
4 sample) in data from 1 file
✔ [3ms] orbi_adjust_block() made the following block adjustments in file
dual_inlet:
→ moved block 1 start from scan.no 823 (5.00 min) to 1153 (7.01 min)
✔ [3ms] orbi_adjust_block() made the following block adjustments in file
dual_inlet:
→ moved block 4 start from scan.no 6087 (37.00 min) to 6251 (38.00 min)
→ moved block 4 end from scan 7402 (45.00 min) to 7238 (44.00 min)

# get blocks info
blocks_info <- df_w_blocks |> orbi_get_blocks_info()
blocks_info |> knitr::kable()
uidx filename data_group block sample_name data_type segment start_scan.no end_scan.no start_time.min end_time.min
1 dual_inlet 1 0 reference startup NA 1 822 0.0069267 4.996783
1 dual_inlet 2 1 reference unused NA 823 1152 5.0028499 7.002534
1 dual_inlet 3 1 reference data NA 1153 2467 7.0086023 14.994198
1 dual_inlet 4 2 sample changeover NA 2468 2796 15.0002893 16.996492
1 dual_inlet 5 2 sample data NA 2797 4112 17.0025578 24.994222
1 dual_inlet 6 3 reference changeover NA 4113 4441 25.0005399 26.997343
1 dual_inlet 7 3 reference data NA 4442 5757 27.0034184 34.995687
1 dual_inlet 8 4 sample changeover NA 5758 6086 35.0019892 36.998449
1 dual_inlet 9 4 sample unused NA 6087 6250 37.0045150 37.994845
1 dual_inlet 10 4 sample data NA 6251 7238 38.0009123 43.999949
1 dual_inlet 11 4 sample unused NA 7239 7402 44.0060263 44.996372
1 dual_inlet 12 5 reference changeover NA 7403 7730 45.0026717 46.994070
1 dual_inlet 13 5 reference data NA 7731 9047 47.0001525 54.996822
1 dual_inlet 14 6 sample changeover NA 9048 9376 55.0031201 56.999885
1 dual_inlet 15 6 sample data NA 9377 10692 57.0059518 64.997181
1 dual_inlet 16 7 reference changeover NA 10693 11020 65.0035025 66.994101
1 dual_inlet 17 7 reference data NA 11021 12337 67.0001943 74.997220
1 dual_inlet 18 8 sample changeover NA 12338 12338 75.0035170 75.003517

Raw data plots

Plot 1: default block highlights + outliers

# total ions per scan
df_w_blocks |> orbi_plot_raw_data(y = intensity, y_scale = "linear")

# isotopocule ratios - you can see that even the AGC outliers
# still create decent ratios
df_w_blocks |>  orbi_plot_raw_data(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_w_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
  )
✔ [15ms] orbi_segment_blocks() segmented 8 data blocks in 1 file creating 3
segments per block (on average) with 420 scans per segment (on average)

✔ [165ms] orbi_summarize_results() summarized ratios from 36.97k peak
(excluding 42 flagged peaks; including 10.35k unused peaks) using the sum
method and grouping the data by uidx, filename, compound, basepeak,
isotopocule, block, sample_name, segment, data_group, and data_type

# export to excel
df_w_summary |> orbi_export_data_to_excel(file = "output.xlsx")
✔ [2.5s] orbi_export_data_to_excel() exported the dataset (1 row of file_info,
96 rows of summary, 12.34k rows of scans, 37.01k rows of peaks, and 0 rows of
problems) to output.xlsx

Plot 1: ratios summary by block and segment

# get out the summary and plot all isotopocules using a ggplot from scratch
df_w_summary |>
  orbi_get_data(summary = everything()) |>
  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")
✔ [4ms] orbi_get_data() retrieved 96 records from the combination of file_info
(1) and summary (96) via uidx

Plot 2: ratios with block backgrounds and raw data

# make a plot for 15N
plot2 <- df_w_blocks |>
  orbi_get_data(scans = everything(), peaks = everything()) |>
  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_w_summary |> 
        orbi_get_data(summary = everything()) |>
        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")
✔ [8ms] orbi_get_data() retrieved 37.01k records from the combination of
file_info (1), scans (12.34k), and peaks (37.01k) via uidx and scan.no

plot2
✔ [4ms] orbi_get_data() retrieved 96 records from the combination of file_info
(1) and summary (96) via uidx


# same but with 18O
plot2 %+% 
  (df_w_blocks |> orbi_get_data(scans = everything(), peaks = everything()) |>
   filter(isotopocule == "18O") |> mutate(panel = "raw ratios")) +
  labs(title = "18O/M0")
✔ [7ms] orbi_get_data() retrieved 37.01k records from the combination of
file_info (1), scans (12.34k), and peaks (37.01k) via uidx and scan.no

✔ [3ms] orbi_get_data() retrieved 96 records from the combination of file_info
(1) and summary (96) via uidx