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Preprocessing

Shiying Xiao1^1, Charles Watt1^1, Jennifer C. Liddle2^2, Jeremy L. Balsbaugh2^2, Timothy E. Moore3^3

1^1Department of Statistics, UConn
2^2Proteomics and Metabolomics Facility, UConn
3^3Statistical Consulting Services, UConn

2025-06-11

Source: vignettes/preprocessing.Rmd
preprocessing.Rmd

Preliminary 

## load R package
library(msDiaLogue)

Example 

## if the raw data is in a .csv file
fileName <- "../tests/testData/Toy_Spectronaut_Data.csv"
dataSet <- preprocessing(fileName,
                         filterNaN = TRUE, filterUnique = 2,
                         replaceBlank = TRUE, saveRm = TRUE)
Note: preprocessing() does not perform a transformation on your data. You still need to use the function transform(). 
## if the raw data is in an .Rdata file
load("../tests/testData/Toy_Spectronaut_Data.RData")
dataSet <- preprocessing(dataSet = Toy_Spectronaut_Data,
                         filterNaN = TRUE, filterUnique = 2,
                         replaceBlank = TRUE, saveRm = TRUE)
#> Warning: Removed 62 rows containing non-finite outside the scale range
#> (`stat_bin()`).

#> Summary of Full Data Signals (Raw):
#>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
#>     20.93    263.87    669.79   6897.92   1963.53 117803.49

#> Levels of Condition: 100pmol 200pmol 50pmol 
#> Levels of Replicate: 1 2 3 4
R.Condition R.Replicate NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN TMC5B_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN KRT16_MOUSE F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TCPR2_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_HUMAN PIP_HUMAN CO6_HUMAN BGAL_HUMAN SYTC_HUMAN CASPE_HUMAN DCAF6_HUMAN DALD3_HUMAN HGNAT_HUMAN RFFL_HUMAN RN185_HUMAN ZN462_HUMAN ALKB7_HUMAN POLK_HUMAN ACAD8_HUMAN A0A7I2PK40_HUMAN NBDY_HUMAN H0Y5R1_HUMAN
100pmol 1 1547.983 3168.32568 2819.7874 318.54376 495.5136 456.3309 213.21727 237.1306 111209.7 10737.953 15097.67 1799.391 630.1937 1311.8127 1279.6390 280.6318 299.51523 1154.5566 16461.2012 179.3190 516.1104 1234.587 27599.42 13798.590 23840.03 614.0895 990.5613 440.0417 132.31737 150.6033 3578.014 26872.50 109.55331 211.6450 1292.5234 1963.5321 189.79155 1106.1482 981.11432 180.6320 199.14555 209.7806 NA NA NA
100pmol 2 1680.730 4576.37158 1061.9502 404.25836 556.8611 501.0473 184.89574 314.0320 111659.9 10655.384 15840.28 NA 575.0490 1114.2773 1294.9751 271.8160 248.04329 1032.0381 1460.7496 213.1137 492.3771 1186.433 27221.59 13880.411 23963.31 640.2153 1077.4829 364.5241 128.78983 128.2592 3412.794 26742.22 155.37483 348.6104 1066.3511 1509.1512 153.90802 1303.6520 388.65823 122.7458 751.19849 247.3832 1420.1351 NA NA
100pmol 3 1414.811 4675.13281 2177.8496 275.09167 559.3206 NA 111.24314 501.2060 105982.9 10663.714 15022.21 NA 613.3968 1224.3837 946.0795 309.7599 270.67770 1808.1924 21555.3555 200.7485 342.1992 1227.435 26587.62 13723.719 22957.35 551.6828 1176.7791 319.0364 NA 118.5104 3499.113 26124.20 91.82145 319.1320 1003.3372 1342.4712 143.12419 1352.7024 430.13318 144.6799 171.13177 221.9161 1889.0665 835.6825 NA
100pmol 4 1620.490 3828.19971 2062.8384 385.05573 558.0967 422.0465 84.27336 334.6389 104442.6 10843.115 15160.49 NA 886.5406 1148.7343 1091.7800 NA 229.40149 901.5703 22937.2500 240.7981 418.1846 1190.952 26168.72 13944.603 22311.30 438.5425 1162.6656 351.5390 NA 137.8860 3481.821 25910.39 88.26187 217.7478 489.8084 1721.8601 99.95578 990.6649 393.55930 134.5238 145.17339 216.3736 1610.2407 950.3087 913.3416
200pmol 1 1512.770 4232.05078 2004.8613 338.27777 156.3478 364.5416 146.80331 NA 109245.3 19524.863 21577.97 2212.190 491.7787 1246.4460 1080.4132 270.1487 252.09808 1454.3271 21113.4512 223.8396 313.7860 1176.982 48693.35 24344.188 41234.67 364.7307 1203.0853 385.5154 65.40555 151.0895 3553.484 26261.47 81.22160 185.4865 939.8899 2149.7632 131.13179 381.0588 429.62201 239.4998 145.04378 424.7914 2337.8496 NA 837.8737
200pmol 2 1480.490 3496.84155 2177.9534 NA 550.4083 NA 135.78349 295.8571 113357.5 20072.297 22968.96 NA 669.7894 1068.2001 NA 285.4891 259.50000 1049.7526 25760.0527 190.3054 452.8294 1220.266 49866.29 24742.227 42899.43 633.5656 1234.5601 414.1271 NA 135.8605 3686.869 27638.89 69.56509 250.4035 1020.4291 725.6615 116.20615 877.0164 438.22589 133.4297 160.92671 155.0986 NA 1053.8444 1000.5491
200pmol 3 1555.834 356.43225 2280.6846 379.62103 564.2863 496.0772 103.30424 473.9141 114321.8 20787.127 20720.13 1451.198 586.7260 1378.0652 1194.8448 291.6754 184.18954 1123.7469 NA 174.5702 432.1681 1216.306 50704.73 24803.633 42904.95 446.4135 1082.7312 357.6343 NA 129.0676 3530.710 27101.22 62.08423 136.7023 1171.5715 1675.6870 109.60301 938.3956 568.89239 315.7039 146.75146 198.4779 1397.9890 837.2197 694.5791
200pmol 4 1529.628 350.70822 2223.3093 410.82349 292.9041 522.1325 95.18819 318.4948 116439.8 19924.240 22153.40 NA 539.0703 923.3237 1115.3848 322.9086 97.65465 957.0436 NA 164.7767 NA 1183.197 53744.70 26381.047 43279.84 527.1628 1121.3438 342.5055 NA 121.3068 3751.769 27545.24 70.39470 199.2453 996.0696 1696.6189 125.31519 611.6407 506.49115 204.4332 161.96100 376.5362 895.9138 NA NA
50pmol 1 1480.210 561.38837 189.9275 264.24271 308.9420 NA 599.90497 192.3859 117803.5 6758.298 12183.81 NA 594.8999 899.5010 1163.1122 291.4431 176.21545 620.2048 14107.1250 152.5492 292.2440 1186.543 16408.28 7169.955 14728.67 2984.7190 1029.7336 288.4770 891.24725 129.7482 3547.950 25668.78 846.95880 146.3040 NA 461.3821 86.84789 373.6308 49.93938 236.2902 20.92994 142.3466 NA NA NA
50pmol 2 1486.144 NA 1462.2559 325.74991 351.2331 NA 254.75084 308.6775 110086.7 6721.135 12521.78 NA 582.8912 531.7106 1119.5256 287.1180 103.58258 849.2368 24912.3613 140.6493 362.3117 1260.574 16444.63 7797.536 14736.71 857.5026 NA 361.4482 179.10303 166.8891 3530.004 26351.25 207.83086 165.6463 265.2173 1184.9562 93.91448 768.2026 489.40918 146.9422 88.41573 101.6087 NA NA NA
50pmol 3 1468.554 42.51457 1364.9075 83.99377 296.5147 396.0038 257.78970 279.2477 105640.2 6172.877 11926.22 1373.660 569.8922 NA 1067.0791 294.0919 88.48861 738.7719 666.5015 NA NA 1175.953 16618.11 7432.793 14160.20 916.4893 992.5451 319.6350 128.63672 120.6974 3458.023 26017.54 203.64948 132.5755 291.4759 932.9668 93.50905 547.0935 263.86734 313.0341 111.88376 85.4563 NA NA NA
50pmol 4 1497.531 927.07886 1435.5588 275.60831 242.4643 425.7305 197.71338 382.4084 110446.0 6028.398 12021.50 NA NA 593.1353 1302.1250 339.3387 30.13688 873.1840 15711.3106 142.4270 291.5121 1150.711 16282.51 7543.633 14758.73 886.7808 1138.6193 NA 152.56187 NA 3575.316 25969.99 190.47060 220.1901 676.8246 996.8993 31.57284 523.4712 450.08408 164.1874 143.96025 135.2896 NA NA NA

Details

The function preprocessing() takes a .csv file of summarized protein abundances, exported from Spectronaut. The most important columns that need to be included in this file are: R.Condition, R.Replicate, PG.ProteinAccessions, PG.ProteinNames, PG.NrOfStrippedSequencesIdentified, and PG.Quantity. This function will reformat the data and provide functionality for some initial filtering (based on the number of unique peptides). The steps below describe the functions that happen in the Preprocessing code.

1. Loads the raw data

  • If the raw data is in a .csv file Toy_Spectronaut_Data.csv, specify the fileName to read the raw data file into R.

  • If the raw data is stored as an .RData file Toy_Spectronaut_Data.RData, first load the data file directly, then specify the dataSet in the function.

2. Filters out identified proteins that exhibit “NaN” quantitative values

NaN, which stands for ‘Not a Number,’ can be found in the PG.Quantity column for proteins that were identified by MS and MS/MS evidence in the raw data, but all peptides from that protein lack an associated integrated peak area or intensity. This usually occurs in low abundance peptides that exhibit intensities close to the limit of detection resulting in poor signal-to-noise (S/N) and/or when there is interference from other co-eluting peptide ions with very similar or identical m/z values that lead to difficulty in parsing out individual intensity profiles.

3. Applies a unique peptides per protein filter

General practice in the proteomics field is to filter out proteins which were identified on the basis of a single peptide. Because approximately 1% of all identified peptides are false positive matches, it’s more likely that 1 peptide was incorrectly identified and that protein ID is incorrect than that, for example, 5 peptides from the same protein were all incorrectly identified and that protein ID is incorrect. We recommend focusing on proteins with 2 or more peptide identifications, as these will be higher confidence. If you have a protein of interest with only 1 peptide identified, contact PMF faculty and we can help you evaluate the evidence from the raw data to determine believability.

4. Adds accession numbers to identified proteins without informative names

Spectronaut reports contain 4 different columns of identifying information:

  • PG.Genes, which is the gene name (e.g. CDK1).
  • PG.ProteinAccessions, which is the UniProt identifier number for a unique entry in the online database (e.g. P06493).
  • PG.ProteinDescriptions, which is the protein name as provided on UniProt (e.g. cyclin-dependent kinase 1).
  • PG.ProteinNames, which is a concatenation of an identifier and the species (e.g. CDK1_HUMAN).

Every entry in UniProt will have an accession number, but may not have all of the other identifiers, due to incomplete annotation. Because Uniprot includes entries for fragments of proteins and some proteins entries are redundant, a peptide can match to multiple entries for the same protein, which generates multiple possible identifiers in Spectronaut. Further, the ProteinNames entry in Spectronaut can switch formats: the preference is accession number and species, but can also be gene name and species instead.

This option tells msDiaLogue to substitute the accession number for an identifier if it tries to pull an identifier from a column with no information.

Note: Not all proteins can be identified unambiguously. In many cases, the identified peptides can be found in multiple protein sequences, which yields a protein group or protein cluster rather than a single protein identification. When this happens, the accession numbers for all potential matches are concatenated into one string, separated by periods. When you see long strings of multiple identifiers later in your data processing, this is why. Spectronaut sorts these alphanumerically, so you should not assume that the first protein in the list is most likely to be correct (other search algorithms such as MaxQuant, which is used in PMF for most Scaffold-based results, do rank protein cluster IDs by likelihood of correctness).

5. Saves a document to your working directory with all filtered out data, if desired

If saveRm = TRUE, the data removed in step 2 (preprocess_Filtered_Out_NaN.csv) and step 3 (preprocess_Filtered_Out_Unique.csv) will be saved in the current working directory.

As part of the preprocessing(), a histogram of log2log_2-transformed protein abundances is provided. This is a helpful way to confirm that the data have been read in correctly, and there are no issues with the numerical values of the protein abundances. Ideally, this histogram will appear fairly symmetrical (bell-shaped) without too much skew towards smaller or larger values.

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