This step is used to get the relative quantification data of internal standards and lipids in all samples. If you have not finished the previous step, please click here:

Get retention time of internal standards.

If you have finished this step, click here to next step:

Get relative quantification

We should have finished step 1. Then we combine the internal standard information from positive and negative mode.

Combine internal standard information

library(lipidflow)
library(tidyverse)
library(openxlsx)

is_info_table_new_pos = 
  readxl::read_xlsx("example/POS/IS_info_new.xlsx")

is_info_table_new_neg = 
  readxl::read_xlsx("example/NEG/IS_info_new.xlsx")

is_info_table_new =
  is_info_table_new_pos %>%
  dplyr::left_join(is_info_table_new_neg[, c("name", "rt_neg_second", "rt_neg_min", "adduct_neg", "mz_neg")],
                   by = "name")

head(is_info_table_new)
#> # A tibble: 6 x 13
#>   name  exact.mass formula ug_ml    um rt_pos_second rt_pos_min adduct_pos
#>   <chr>      <dbl> <chr>   <dbl> <dbl>         <dbl>      <dbl> <chr>     
#> 1 15:0…       588. C36H61… 0.22  0.375         1147.       19.1 M+H-H2O   
#> 2 15:0…       690. C36H61… 0.172 0.25           783.       13.0 M+H       
#> 3 15:0…       753. C41H73… 1.88  2.5           1182.       19.7 M+H       
#> 4 15:0…       711. C38H67… 0.125 0.175         1193.       19.9 M+H       
#> 5 15:0…       764. C39H67… 0.095 0.125         1181.       19.7 M+H       
#> 6 15:0…       847. C42H75… 0.422 0.5           1147.       19.1 M+H       
#> # … with 5 more variables: mz_pos <dbl>, rt_neg_second <dbl>, rt_neg_min <dbl>,
#> #   adduct_neg <chr>, mz_neg <dbl>

Then we output the is_info_table_new into the example/Result folder.

We first create a folder named as Result:

dir.create("example/Result")

dir.create("example/Result")
openxlsx::write.xlsx(is_info_table_new,
                     file = "example/Result/IS_info_table.xlsx")

Relative quantification

1. Positive mode

We need to get the sample information of samples first.

sample_info_pos =
  generate_sample_info(path = "example/POS")

head(sample_info_pos)
#>   sample.name group
#> 1       D25_1   D25
#> 2       D25_2   D25
#> 3       M19_1   M19
#> 4       M19_2   M19

Get the Cholesterol retention time:

if (any(is_info_table_new$name == "Cholesterol")) {
  idx = which(is_info_table_new$name == "Cholesterol")
  chol_rt2 = c(is_info_table_new$rt_pos_second[idx],
               is_info_table_new$rt_neg_second[idx])
  chol_rt2 = chol_rt2[!is.na(chol_rt2)]
  if (length(chol_rt2) > 0) {
    chol_rt = chol_rt2[1]
  }
} else{
  chol_rt = 1169
}

chol_rt
#> [1] 1720.245

Internal standards

Then run get_relative_quantification() to get the relative quantification data of internal standards:

get_relative_quantification(
  path = "example/POS",
  output_path_name = "is_relative_quantification",
  targeted_table_name = "IS_info_new.xlsx",
  sample_info = sample_info_pos,
  targeted_table_type = "is",
  polarity = "positive",
  chol_rt = chol_rt,
  output_eic = TRUE,
  ppm = 40,
  rt.tolerance = 180,
  threads = 5,
  rerun = FALSE
)
#> 15:0-18:1(d7) DAG  15:0-18:1(d7) PA (Na Salt)  15:0-18:1(d7) PC  15:0-18:1(d7) PE  15:0-18:1(d7) PG (Na Salt)  15:0-18:1(d7) PI (NH4 Salt)  15:0-18:1(d7) PS (Na Salt)  15:0-18:1(d7)-15:0 TAG  18:1 (d7) MG  18:1(d7) Chol Ester  18:1(d7) Lyso PC  18:1(d7) Lyso PE  C18(Plasm)-18:1(d9) PC  C18(Plasm)-18:1(d9) PE  Cholesterol (d7)  d18:1 (d7)-15:0 Cer  d18:1-18:1(d9) SM  rac-16: 0 (d5) PI (3, 5) P2  rac-16: 0 (d5) PI (5) P  Cholesterol  15:0-18:1(d7) DAG  15:0-18:1(d7) PA (Na Salt)  15:0-18:1(d7) PC  15:0-18:1(d7) PE  15:0-18:1(d7) PG (Na Salt)  15:0-18:1(d7) PI (NH4 Salt)  15:0-18:1(d7) PS (Na Salt)  15:0-18:1(d7)-15:0 TAG  18:1 (d7) MG  18:1(d7) Chol Ester  18:1(d7) Lyso PC  18:1(d7) Lyso PE  C18(Plasm)-18:1(d9) PC  C18(Plasm)-18:1(d9) PE  Cholesterol (d7)  d18:1 (d7)-15:0 Cer  d18:1-18:1(d9) SM  rac-16: 0 (d5) PI (3, 5) P2  rac-16: 0 (d5) PI (5) P  Cholesterol  15:0-18:1(d7) DAG  15:0-18:1(d7) PA (Na Salt)  15:0-18:1(d7) PC  15:0-18:1(d7) PE  15:0-18:1(d7) PG (Na Salt)  15:0-18:1(d7) PI (NH4 Salt)  15:0-18:1(d7) PS (Na Salt)  15:0-18:1(d7)-15:0 TAG  18:1 (d7) MG  18:1(d7) Chol Ester  18:1(d7) Lyso PC  18:1(d7) Lyso PE  C18(Plasm)-18:1(d9) PC  C18(Plasm)-18:1(d9) PE  Cholesterol (d7)  d18:1 (d7)-15:0 Cer  d18:1-18:1(d9) SM  rac-16: 0 (d5) PI (3, 5) P2  rac-16: 0 (d5) PI (5) P  Cholesterol  15:0-18:1(d7) DAG  15:0-18:1(d7) PA (Na Salt)  15:0-18:1(d7) PC  15:0-18:1(d7) PE  15:0-18:1(d7) PG (Na Salt)  15:0-18:1(d7) PI (NH4 Salt)  15:0-18:1(d7) PS (Na Salt)  15:0-18:1(d7)-15:0 TAG  18:1 (d7) MG  18:1(d7) Chol Ester  18:1(d7) Lyso PC  18:1(d7) Lyso PE  C18(Plasm)-18:1(d9) PC  C18(Plasm)-18:1(d9) PE  Cholesterol (d7)  d18:1 (d7)-15:0 Cer  d18:1-18:1(d9) SM  rac-16: 0 (d5) PI (3, 5) P2  rac-16: 0 (d5) PI (5) P  Cholesterol  
#> Output peak shapes...
#> 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  
#> Done

The meanings of parameters:

path: Work directory. Here we set as example/POS.
targeted_table_name: The name of internal standard information.
sample_info: sample information of samples.
targeted_table_type: The type of extracted peaks, “is” (internal standard) or “lipid” (lipids).
polarity: “positive” or negative.
chol_rt: Cholesterol retention time (second).
output_eic: Output EICs of peaks or not.
ppm: peak detection ppm.
rt.tolerance: peak detection retention time tolerance (second).
threads: Number of cores to run.
rerun: Rerun peak detection or not.

Then all the results are outputted into a folder named as example/POS/is_relative_quantification. The relative quantification table is is_quantification_table.xlsx and all the peak shapes are in peak_shape.

is_quantification_table.xlsx is:

is_quantification_table.xlsx

One example of peak shape (15_0-18_1(d7) PC):

Rectangle region means the integrate of peak region.

Lipids

Then run get_relative_quantification() for lipids:

get_relative_quantification(
  path = "example/POS",
  output_path_name = "lipid_relative_quantification",
  targeted_table_name = "lipid_annotation_table_pos.xlsx",
  sample_info = sample_info_pos,
  targeted_table_type = "lipid",
  polarity = "positive",
  chol_rt = chol_rt,
  output_eic = TRUE,
  ppm = 40,
  rt.tolerance = 180,
  threads = 5,
  rerun = FALSE
)
#> Error in nlsModel(formula, mf, start, wts) : 
#>   singular gradient matrix at initial parameter estimates
#> Error in nlsModel(formula, mf, start, wts) : 
#>   singular gradient matrix at initial parameter estimates
#> Error in nlsModel(formula, mf, start, wts) : 
#>   singular gradient matrix at initial parameter estimates
#> Error in nlsModel(formula, mf, start, wts) : 
#>   singular gradient matrix at initial parameter estimates
#> Error in nlsModel(formula, mf, start, wts) : 
#>   singular gradient matrix at initial parameter estimates
#> Error in nlsModel(formula, mf, start, wts) : 
#>   singular gradient matrix at initial parameter estimates
#> Error in nlsModel(formula, mf, start, wts) : 
#>   singular gradient matrix at initial parameter estimates
#> Error in nlsModel(formula, mf, start, wts) : 
#>   singular gradient matrix at initial parameter estimates
#> 
#> Output peak shapes...
#> 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99  100  101  102  103  104  105  106  107  108  109  110  111  112  113  114  115  116  117  118  119  120  121  122  123  124  125  126  127  128  129  130  131  132  133  134  135  136  137  138  139  140  141  142  143  144  145  146  147  148  149  150  151  152  153  154  155  156  157  158  159  160  161  162  163  164  165  166  167  168  169  170  171  172  173  174  175  176  177  178  
#> Done

Here the targeted_table_name is the annotation table from lipidsearch. It should be placed in POS and NEG folders, respectively.

The results are outputted in example/POS/lipid_relative_quantification folder. These results are similar with internal standards.

2. Negative mode

Negative mode is same with positive mode.

sample_info_neg =
  generate_sample_info(path = "example/NEG")

Internal standards

get_relative_quantification(
  path = "example/NEG",
  output_path_name = "is_relative_quantification",
  targeted_table_name = "IS_info_new.xlsx",
  sample_info = sample_info_neg,
  targeted_table_type = "is",
  polarity = "negative",
  chol_rt = chol_rt,
  output_integrate = TRUE,
  output_eic = TRUE,
  ppm = 40,
  rt.tolerance = 180,
  threads = 5,
  rerun = FALSE
)
#> 
#> Output peak shapes...
#> 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  
#> Done

Lipids

Then run get_relative_quantification() for lipids:

get_relative_quantification(
  path = "example/NEG",
  output_path_name = "lipid_relative_quantification",
  targeted_table_name = "lipid_annotation_table_neg.xlsx",
  sample_info = sample_info_neg,
  targeted_table_type = "lipid",
  polarity = "negative",
  chol_rt = chol_rt,
  output_eic = TRUE,
  ppm = 40,
  rt.tolerance = 180,
  threads = 5,
  rerun = FALSE
)
#> 
#> Output peak shapes...
#> 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  
#> Done

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:

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:

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:

get_relative_quantification(
  path = "example/POS",
  forced_targeted_peak_table_name = "forced_targeted_peak_table_temple_manual.xlsx",
  output_path_name = "is_relative_quantification",
  targeted_table_name = "IS_info_new.xlsx",
  sample_info = sample_info_pos,
  targeted_table_type = "is",
  polarity = "positive",
  chol_rt = chol_rt,
  output_integrate = TRUE,
  output_eic = TRUE,
  ppm = 40,
  rt.tolerance = 180,
  threads = 5,
  rerun = FALSE
)
#> Manually check..
#> 
#> Output peak shapes...
#> 10  
#> Done

Then open 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 detection is good for now:

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.

Next step

Next we need to get the absolute quantification data of all lipids in all sample.

Please click here:

Step3: get absolute quantification data

Step 2: get relative quantification data

Xiaotao Shen PhD (https://www.shenxt.info/)

Chuchu wang PhD

Created on 2021-02-09 and updated on 2021-03-03

Get relative quantification

Combine internal standard information

Relative quantification

1. Positive mode

Internal standards

Lipids

2. Negative mode

Internal standards

Lipids

Manually check for relative quantification data of internal standards

Next step