library(isoorbi) #load isoorbi R package
library(forcats) #better ordering of factor variables in plots
# Load a path to a system test file
file_path <-system.file("extdata", "testfile_dual_inlet_new.isox", package = "isoorbi")A basic data processing example
# Read .isox test data
df <- orbi_read_isox(file = file_path)
# Keep only most important columns; equivalent to simplify check box in IsoX
df.simple <- orbi_simplify_isox(dataset = df)
# Filter the data
df.filtered <- df.simple |> orbi_filter_isox(
time_min = 0,
time_max = 75,
compounds = "NO3",
isotopocules = c("M0", "15N", "18O", "17O")
)
# Clean the data by removing noise and outliers
df.clean <- df.filtered |> orbi_filter_satellite_peaks() |>
orbi_filter_weak_isotopocules(min_percent = 10) |>
orbi_filter_scan_intensity(outlier_percent = 2)Examples for block definitions
Calculations
The following code block contains functionality that is specific to processing data from dual inlet experiments
# change a global setting
orbi_set_settings(di_ref_name = "my ref")
# define blocks
df_w_blocks <-
# general definition
orbi_define_blocks_for_dual_inlet(
df.simple,
ref_block_time.min = 10,
sample_block_time.min = 10,
startup_time.min = 5,
change_over_time.min = 2,
sample_block_name = "sample"
) |>
# fine adjustments
orbi_adjust_block(block = 1, shift_start_time.min = 2) |>
orbi_adjust_block(block = 4, set_start_time.min = 38, set_end_time.min = 46)## orbi_define_blocks_for_dual_inlet() identified 7 blocks (4 'my ref', 3 'sample') in data from 1 file(s)## orbi_adjust_block() is making the following block adjustments in file 20230518_05_USGS32_vs_USGS34:
## - moving block 1 start from scan 825 (5.01 min) to 1155 (7.02 min)## orbi_adjust_block() is making the following block adjustments in file 20230518_05_USGS32_vs_USGS34:
## - moving block 4 start from scan 6090 (37.02 min) to 6255 (38.02 min)
## - moving block 4 end from scan 7400 (44.98 min) to 7565 (45.99 min)
## - moving block 5 start to the new end of block 4
# 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 | my ref | startup | NA | 5 | 820 | 0.031 | 4.985 |
| 20230518_05_USGS32_vs_USGS34 | 2 | 1 | my ref | unused | NA | 825 | 1150 | 5.015 | 6.990 |
| 20230518_05_USGS32_vs_USGS34 | 3 | 1 | my ref | 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 | my ref | changeover | NA | 4115 | 4440 | 25.016 | 26.991 |
| 20230518_05_USGS32_vs_USGS34 | 7 | 3 | my ref | 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 | 7565 | 38.025 | 45.991 |
| 20230518_05_USGS32_vs_USGS34 | 11 | 5 | my ref | changeover | NA | 7570 | 7730 | 46.021 | 46.994 |
| 20230518_05_USGS32_vs_USGS34 | 12 | 5 | my ref | data | NA | 7735 | 9045 | 47.024 | 54.985 |
| 20230518_05_USGS32_vs_USGS34 | 13 | 6 | sample | changeover | NA | 9050 | 9375 | 55.019 | 56.994 |
| 20230518_05_USGS32_vs_USGS34 | 14 | 6 | sample | data | NA | 9380 | 10690 | 57.024 | 64.985 |
| 20230518_05_USGS32_vs_USGS34 | 15 | 7 | my ref | changeover | NA | 10695 | 11020 | 65.019 | 66.994 |
| 20230518_05_USGS32_vs_USGS34 | 16 | 7 | my ref | data | NA | 11025 | 12335 | 67.025 | 74.985 |
Raw data plots
Plot 1: signal by block
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
df_w_blocks |>
filter(isotopocule == "15N") |>
# data
ggplot() +
aes(x = time.min, y = ions.incremental, group = data_group, color = data_type) +
geom_line() +
# scales
scale_fill_brewer(palette = "Set1") +
scale_x_continuous(breaks = scales::breaks_pretty(8), expand = c(0, 0)) +
theme_bw() +
labs(x = "time [min]", y = "intensity")
Plot 2: highlight blocks
ggplot() +
# blocks background
geom_rect(
data = blocks_info,
map = aes(
xmin = start_time.min,
xmax = end_time.min,
ymin = -Inf,
ymax = Inf,
# use sample name for data blocks, otherwise data type
fill = ifelse(data_type == "data", sample_name, data_type)
), alpha = 0.5
) +
# data
geom_line(
data = df_w_blocks |> filter(isotopocule == "15N"),
map = aes(x = time.min, y = ions.incremental, group = data_group)
) +
# scales
scale_fill_brewer(palette = "Set1") +
scale_x_continuous(breaks = scales::breaks_pretty(8), expand = c(0, 0)) +
theme_bw() +
labs(x = "time [min]", y = "intensity", fill = "block")
Ratio data
# calculate results
df_results <-
df_w_blocks |>
# define base peak
orbi_define_basepeak(basepeak_def = "M0") |>
# segment (optional)
orbi_segment_blocks(into_segments = 3) |>
# calculate results
orbi_summarize_results(ratio_method = "sum")## orbi_define_basepeak() is setting the M0 isotopocule as the ratio denominator...## orbi_segment_blocks() segmented 7 data blocks in 1 file(s) into 3 segments / block (on average) with 88 scans / segment (on average)## orbi_summarize_results() is grouping the data by 'filename', 'compound', 'basepeak', 'isotopocule', 'block', 'sample_name', 'segment', 'data_group', 'data_type' and summarizing ratios using the 'sum' method...Plot 1: ratios by block and segment
library(forcats)
df_results |>
filter(data_type == "data") |>
mutate(block_seg = paste0(block, ".", segment) |> fct_inorder()) |>
# data
ggplot() +
aes(x = block_seg, y = ratio, color = sample_name) +
geom_point(size = 2) +
facet_grid(isotopocule ~ ., scales = "free_y") +
# scales
scale_color_brewer(palette = "Set1") +
theme_bw() +
labs(x = "block.segment", y = "ratio")
Plot 2: with block backgrounds and raw data
# demonstrate use of a base plot
base_plot <-
ggplot() +
# blocks background
geom_rect(
data = blocks_info,
map = aes(
xmin = start_time.min, xmax = end_time.min, ymin = -Inf, ymax = Inf,
fill = data_type
), alpha = 0.5
) +
# data
geom_point(
data = function(df) df |> filter(panel == "ratio"),
map = aes(x = mean_time.min, y = ratio, shape = sample_name),
size = 3
) +
geom_line(
data = function(df) df |> filter(panel == "intensity"),
map = aes(x = time.min, y = ions.incremental)
) +
facet_grid(panel ~ ., scales = "free_y") +
# scales
scale_fill_brewer(palette = "Set1") +
scale_x_continuous(breaks = scales::breaks_pretty(8), expand = c(0, 0)) +
theme_bw() +
labs(x = "time [min]", y = NULL, fill = "block")
# with 15N data
df_plot <-
df_w_blocks |>
mutate(panel = "intensity") |>
bind_rows(df_results |> mutate(panel = "ratio"))
base_plot %+% filter(df_plot, isotopocule == "15N") + labs(title = "15N/M0")
Plot 3: small scans
# calculate ratios segmented with 5 scans at a time
df_ratios <-
df_w_blocks |>
# define base peak
orbi_define_basepeak(basepeak_def = "M0") |>
# segment (optional)
orbi_segment_blocks(by_scans = 5) |>
# calculate results
orbi_summarize_results(ratio_method = "mean")## orbi_define_basepeak() is setting the M0 isotopocule as the ratio denominator...## orbi_segment_blocks() segmented 7 data blocks in 1 file(s) into 53 segments / block (on average) with 5 scans / segment (on average)## orbi_summarize_results() is grouping the data by 'filename', 'compound', 'basepeak', 'isotopocule', 'block', 'sample_name', 'segment', 'data_group', 'data_type' and summarizing ratios using the 'mean' method...
ggplot() +
# blocks background
geom_rect(
data = blocks_info,
map = aes(
xmin = start_time.min, xmax = end_time.min, ymin = -Inf, ymax = Inf,
fill = data_type
), alpha = 0.5
) +
# data
geom_line(
data = df_ratios |> filter(data_type == "data", isotopocule == "17O"),
map = aes(x = mean_time.min, y = ratio, group = block)
) +
# scales
scale_fill_brewer(palette = "Set1") +
scale_x_continuous(breaks = scales::breaks_pretty(8), expand = c(0, 0)) +
theme_bw() +
labs(x = "time [min]", y = "ratio", fill = "block")