I'm trying to run the LFQ workflow from the DEP package in R using my protein dataset but some arguments don't apply to my case:
.#Run LFQ workflow of DEP package
data_results <- LFQ(comparison_table, expdesign, type = "all", name = "description", alpha = 0.05, lfc = 1)
When running the code, I get errors like :
Error: 'Protein.IDs' is not a column in 'comparison_table'.
Error: 'Gene.names' is not a column in 'comparison_table'.
These errors indicate that these arguments are required for the code to run properly. However, when I tried the code using the example dataset provided in the package documentation, it worked without using some of the arguments :
data_results <- LFQ(data, experimental_design, fun = "MinProb", type = "control", control = "Ctrl", alpha = 0.05, lfc = 1)
worked with no errors although they didn't use some arguments like ids.
Here's my dataset "comparison_table" :
'data.frame': 4251 obs. of 41 variables:
$ description: chr "1433B" "1433E" "1433F" "1433G" ...
$ S2.50 : num 0.1241 0.1535 0.0477 0.0412 0.041 ...
$ S3.50 : num 0.1649 0.2283 0.0681 0.0821 0.0925 ...
$ S4.50 : num 0.14 0.1455 0.0323 0.0501 0.0285 ...
$ S5.50 : num 0.1028 0.1735 0.044 0.0366 0.1784 ...
$ S6.50 : num 0.106 0.1268 0.0424 0.038 0.0463 ...
$ S7.50 : num 0.0578 0.0906 0 0.0503 0.043 ...
$ S8.50 : num 0.1112 0.1262 0.0229 0.0456 0.0713 ...
$ S9.30 : num 0.275 0.1972 0.0494 0.0632 0.1748 ...
$ S10.50 : num 0.2444 0.1801 0.04 0.0531 0 ...
$ S11.30 : num 0.0911 0.1381 0.0195 0.0324 0 ...
$ S13.50 : num 0.0557 0.1223 0.0186 0.0339 0.0706 ...
$ S16.50 : num 0.1488 0.2271 0.0692 0.1448 0.0275 ...
$ S18.50 : num 0.133 0.1941 0.0357 0.0582 0.2929 ...
$ S21.50 : num 0.0452 0.1454 0.0264 0.0263 0.1271 ...
$ S22.50 : num 0.1013 0.1303 0.0439 0.0471 0.0603 ...
$ S23.50 : num 0.1362 0.2774 0.0492 0.0716 0.0826 ...
$ S24.30 : num 0.0806 0.105 0.0242 0.0602 0.172 ...
$ S25.50 : num 0.2112 0.1573 0.0482 0.107 0.0221 ...
$ S26.50 : num 0.0747 0.1473 0.026 0.0453 0.0999 ...
$ S27.50 : num 0.1742 0.1427 0.0394 0.0982 0 ...
$ S28.50 : num 0.1813 0.1007 0.0275 0.0493 0.0763 ...
$ S29.50 : num 0.137 0.2102 0.0156 0.0403 0.0432 ...
$ S31.50 : num 0.1186 0.1456 0.0539 0.1181 0.1497 ...
$ S33.30 : num 0.1494 0.2145 0.0382 0.0588 0.1861 ...
$ S34.50 : num 0.1192 0.1255 0.0145 0.0648 0.2043 ...
$ S35.30 : num 0.3148 0.0675 0.1399 0.0871 0.1041 ...
$ S36.50 : num 0.0631 0.0468 0.0097 0.0483 0.0337 ...
$ S37.50 : num 0.1911 0.1843 0.0303 0.0876 0.0181 ...
$ S38.30 : num 0.1872 0.1354 0.0351 0.0505 0.1045 ...
$ S40.50 : num 0.18 0.2015 0.0771 0.0832 0.1211 ...
$ S41.50 : num 0.1595 0.1871 0.0156 0.0717 0.0807 ...
$ S42.30 : num 0.0743 0.1569 0.0418 0.0416 0.0323 ...
$ S44.50 : num 0.0888 0.156 0.0571 0.0663 0 ...
$ S45.50 : num 0.117 0.1437 0.0355 0.0283 0.0176 ...
$ S46.50 : num 0.2356 0.1623 0.0294 0.0461 0 ...
$ S48.50 : num 0.0876 0.1655 0.0245 0.0837 0 ...
$ S49.50 : num 0.1308 0.1452 0.0425 0.0358 0 ...
$ S50.50 : num 0.1036 0.1766 0.0449 0.0447 0.1131 ...
$ S51.50 : num 0.2043 0.1906 0.0201 0.0767 0.0565 ...
$ S52.50 : num 0.2043 0.1183 0 0.0661 0 ...
Here are the LFQ workflow arguments:
-proteins Data.frame, The data object.
-expdesign Data.frame, The experimental design object.
-fun "man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed" or "nbavg", Function used for data imputation based on manual_impute and impute.
-type 'all', 'control' or 'manual', The type of contrasts that will be generated.
-control Character(1), The sample name to which the contrasts are generated (the control sample would be most appropriate).
-test Character, The contrasts that will be tested if type = "manual". These should be formatted as "SampleA_vs_SampleB" or c("SampleA_vs_SampleC", "SampleB_vs_SampleC").
-filter Character, Name(s) of the column(s) to be filtered on.
-name Character(1), Name of the column representing gene names.
-ids 'Character(1), Name of the column representing protein IDs.
-alpha Numeric(1), sets the false discovery rate threshold.
-lfc Numeric(1), sets the log fold change threshold.
