In jaspershen/lipidflow: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)

lipidflow also provide only one function get_lipid_absolute_quantification() to finish all steps (step 1-4).

Demo data to show how to use `get_lipid_absolute_quantification()`

Organize the demo data

First, we need to get the demo dataset.

library(lipidflow)
library(tidyverse)
pos_data = system.file("POS", package = "lipidflow")
neg_data = system.file("NEG", package = "lipidflow")
path = file.path(".", "example")
dir.create(path)
file.copy(from = pos_data, to = path, recursive = TRUE, overwrite = TRUE)
file.copy(from = neg_data, to = path, recursive = TRUE, overwrite = TRUE)

Now there will be a example folder in your work directory. And in the example folder, there are two folders: POS and NEG. The are two groups for each mode: "D25" and "M19". And each group has two repeats.

Next, we should set the match list between internal standards and lipid class.

Positive mode:

match_item_pos =
  list(
    "Cer" = "d18:1 (d7)-15:0 Cer",
    "ChE" = c("18:1(d7) Chol Ester", "Cholesterol (d7)"),
    "Chol" = "Cholesterol (d7)",
    "DG" = "15:0-18:1(d7) DAG",
    "LPC" = "18:1(d7) Lyso PC",
    "LPE" = "18:1(d7) Lyso PE",
    "MG" = "18:1 (d7) MG",
    "PA" = "15:0-18:1(d7) PA (Na Salt)",
    "PC" = "15:0-18:1(d7) PC",
    "PE" = "15:0-18:1(d7) PE",
    "PG" = "15:0-18:1(d7) PG (Na Salt)",
    "PI" = "15:0-18:1(d7) PI (NH4 Salt)",
    "PPE" = "C18(Plasm)-18:1(d9) PE",
    "PS" = "15:0-18:1(d7) PS (Na Salt)",
    "SM" = "d18:1-18:1(d9) SM",
    "TG" = "15:0-18:1(d7)-15:0 TAG"
  )

Negative mode:

match_item_neg =
  list(
    "Cer" = "d18:1 (d7)-15:0 Cer",
    "Chol" = "Cholesterol (d7)",
    "ChE" = c("18:1(d7) Chol Ester", "Cholesterol (d7)"),
    "LPC" = "18:1(d7) Lyso PC",
    "LPE" = "18:1(d7) Lyso PE",
    "PC" = "15:0-18:1(d7) PC",
    "PE" = "15:0-18:1(d7) PE",
    "PG" = "15:0-18:1(d7) PG (Na Salt)",
    "PI" = "15:0-18:1(d7) PI (NH4 Salt)",
    "PPE" = "C18(Plasm)-18:1(d9) PE",
    "PS" = "15:0-18:1(d7) PS (Na Salt)",
    "SM" = "d18:1-18:1(d9) SM"
  )

Run `get_lipid_absolute_quantification()` function

Then we run get_lipid_absolute_quantification() function. Please note that path need to set as example.

get_lipid_absolute_quantification(
  path = "example",
  is_info_name_pos = "IS_information.xlsx",
  is_info_name_neg = "IS_information.xlsx",
  use_manual_is_info = FALSE,
  lipid_annotation_table_pos = "lipid_annotation_table_pos.xlsx",
  lipid_annotation_table_neg = "lipid_annotation_table_neg.xlsx",
  output_eic = TRUE,
  ppm = 40,
  rt.tolerance = 180,
  threads = 3,
  rerun = FALSE,
  which_group_for_rt_confirm = "D25",
  match_item_pos = match_item_pos,
  match_item_neg = match_item_neg
)

Manually check for relative quantification data of internal standards

Sometimes, the peak detection maybe not accurate, so we need to manually check the peak shape of all the internal standards and then detect peak and integrate peak shape again.

For example, the internal standard 18_1_d7_ Lyso PC in positive mode (example/POS/is_relative_quantification/peak_shape/18_1_d7_ Lyso PC.html), the peak shape is like below figure shows:

htmltools::tags$iframe(
  title = "Internal standard",
  src = "../man/figures/figure31.html",
  width = "100%",
  height = "600",
  scrolling = "no",
  seamless = "seamless",
  frameBorder = "0"
)

From this figure, we can see that for the samples D25_1, D25_2 and M19_2, the integrate region (begin point) is not correct, so we need to manually correct that.

Open the table in example/POS/is_relative_quantification/forced_targeted_peak_table_temple.xlsx, like the below figure shows:

Then open the peak shape plot for each internal standard in example/POS/is_relative_quantification/peak_shape. Then if you find that the peak integration is not correct, you can add the right begin and end retention time for peaks like below video shows:

library(vembedr)
embed_url("https://www.youtube.com/watch?v=OiUdqkw5KZc&feature=youtu.be") %>% 
  use_align("center") %>% 
  use_bs_responsive()

After check all the internal standards which are not normal, please rename it as forced_targeted_peak_table_temple_manual.xlsx and rerun get_relative_quantification(), and note that set forced_targeted_peak_table_name as forced_targeted_peak_table_temple_manual.xlsx:

For the negative mode, we can also manually check internal standards like this and rerun to get more accurate peak integration and relative quantification data.

Manually check for absolute quantification data of lipids

Absolute quantification in class level

First, please open the example/Result/class_plot, and check each plot. For example, for the lipid class ChE, we can see that D25_1 and D25_2 are in the sample groups, but their ChE are very different, so it indicates that the absolute quantification for ChE class may be wrong. So next we should check each lipid in ChE.

Absolute quantification in lipid level

Then open the example/Result/intensity_plot/ChE folder, and there is only one ChE lipid ChE(0_0):

From the lipid_data_um.xlsx, we know that ChE(0_0) is from positive mode, so we then open the html plot for ChE(0_0), and we can clearly see that the peak integration for D25_1 is wrong.

htmltools::tags$iframe(
  title = "Internal standard",
  src = "../man/figures/figure35.html",
  width = "100%",
  height = "600",
  scrolling = "no",
  seamless = "seamless",
  frameBorder = "0"
)

Rerun `get_lipid_absolute_quantification()`

Then we need to get the relative and absolute quantification data again.

First, open the forced_targeted_peak_table_temple.xlsx in example/POS/lipid_relative_quantification folder. And add the correct peak integration begin and end time into forced_targeted_peak_table_temple.xlsx, and rename it as forced_targeted_peak_table_temple_manual.xlsx. This step is same with Manually check for relative quantification data of internal standards in step 2. Please check the video there.

Then run the get_relative_quantification() function for lipids.

get_lipid_absolute_quantification(
  path = "example",
  forced_targeted_peak_table_name = "forced_targeted_peak_table_temple_manual.xlsx",
  is_info_name_pos = "IS_information.xlsx",
  is_info_name_neg = "IS_information.xlsx",
  lipid_annotation_table_pos = "lipid_annotation_table_pos.xlsx",
  lipid_annotation_table_neg = "lipid_annotation_table_neg.xlsx",
  output_eic = TRUE,
  ppm = 40,
  rt.tolerance = 180,
  threads = 3,
  rerun = FALSE,
  which_group_for_rt_confirm = "D25",
  match_item_pos = match_item_pos,
  match_item_neg = match_item_neg
)

Then open the peak shape for ChE(0_0):

htmltools::tags$iframe(
  title = "Internal standard",
  src = "../man/figures/figure37.html",
  width = "100%",
  height = "600",
  scrolling = "no",
  seamless = "seamless",
  frameBorder = "0"
)