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Preprocessing

Source: vignettes/preprocessing.qmd

Preliminary 

## load R package
library(msDiaLogue)

Example 

## if the raw data is in a .csv file
fileName <- "../inst/extdata/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 R data file
data("Toy_Spectronaut_Data")
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 Preprocessed Data Signals:
#>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
#>     23.19    266.76    694.02   6980.19   1869.80 132136.03

#> Levels of Condition: 100fmol, 200fmol, 50fmol
#> Levels of Replicate: 1, 2, 3, 4
R.Condition R.Replicate NUD4B_HUMAN (+1) A0A7P0T808_HUMAN (+1) A0A8I5KU53_HUMAN (+1) ZN840_HUMAN CC85C_HUMAN TMC5B_HUMAN C9JEV0_HUMAN (+1) C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN KRT16_MOUSE F8W0H2_HUMAN H0Y7V7_HUMAN (+1) H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN (+1) 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 (+2) NBDY_HUMAN H0Y5R1_HUMAN (+1)
100fmol 1 1512.759 2997.02209 3371.4833 334.80388 407.0488 489.4850 203.37113 213.1143 108811.84 9690.868 14036.36 1690.365 532.3802 1332.0875 1141.9809 318.1188 312.56476 1120.9867 18002.6458 195.7776 560.3036 1322.602 29171.33 13713.424 23121.66 591.1640 924.0823 430.9869 116.59002 187.0816 4037.412 24017.74 105.22731 201.9954 1397.3715 1947.2304 194.66073 1102.9950 976.91724 207.1249 194.69980 244.13206 NA NA NA
100fmol 2 1781.917 4357.08535 842.9818 447.03104 518.7363 467.7339 204.86459 305.2153 98828.47 10850.332 15621.79 NA 597.6368 1188.4426 1266.7351 280.9857 276.79778 1077.9421 1413.9067 239.0581 543.8075 1253.314 27879.27 13035.651 27456.12 602.9164 1340.9268 424.9000 125.78974 115.7475 3178.759 27438.09 151.58875 336.7029 969.5539 1502.3712 142.28964 1103.3769 374.15784 134.5607 709.19833 262.88982 1743.2662 NA NA
100fmol 3 1406.972 4786.48456 2194.8947 249.86449 555.3463 NA 116.38093 503.2769 101598.44 8684.345 16821.60 NA 660.5064 1059.7662 1014.8591 301.7436 231.29973 1554.0445 18365.4408 190.3686 295.6622 1314.844 32800.92 12066.374 24838.93 595.7836 1216.5285 288.4343 NA 115.2336 3701.760 25169.13 101.24499 307.3990 1114.7195 1208.7592 126.17845 1870.5048 412.57133 149.0596 182.37971 211.43579 1769.8376 1004.8916 NA
100fmol 4 1681.397 3696.65961 1891.8054 376.06429 547.2002 471.5889 84.99050 360.8483 99355.87 11078.152 14676.16 NA 810.6129 1402.6763 1159.3741 NA 215.80109 800.7830 28578.1735 274.5684 407.2423 1257.433 25106.60 13296.061 20619.41 413.2262 1371.3643 349.6449 NA 141.2874 3938.512 24607.16 79.92251 257.4710 468.6696 1601.7529 88.33184 871.2289 371.60609 143.0994 162.21354 232.23867 1827.0492 854.4468 801.1353
200fmol 1 1521.836 4371.54777 1854.1405 312.62524 148.6900 423.3701 131.02175 NA 132136.03 19328.703 18834.47 2069.912 516.2416 1178.3424 1043.8876 272.6046 295.79573 1441.5038 23523.2733 238.4131 310.2403 1009.710 46220.84 23180.376 41429.57 415.3739 1513.1556 450.0412 64.54044 126.7504 3417.982 26496.79 88.38325 204.2275 1006.4592 1869.7977 142.76032 364.4167 437.19794 241.2919 151.38894 425.84091 2437.8824 NA 806.9866
200fmol 2 1593.643 4272.11244 2192.5291 NA 480.8571 NA 153.86154 275.4352 105138.41 18274.136 20674.32 NA 692.3428 1091.0855 NA 350.0098 295.56072 1132.2785 21674.8350 179.1959 437.1651 1309.207 49342.12 21816.062 50769.83 694.0213 1264.2230 467.7733 NA 145.1417 3499.032 29594.79 69.14327 266.7654 1166.2285 726.1907 128.65335 778.7439 407.71731 155.3213 167.11595 126.32254 NA 1181.7201 818.0170
200fmol 3 1524.908 394.50764 1868.6211 363.74226 570.9065 453.6885 106.81184 493.8190 113944.78 16244.333 26795.97 1421.709 626.2046 1526.7599 1296.6478 285.6200 191.28838 1022.3743 NA 166.7045 550.3156 1031.190 48405.84 26937.736 45150.45 420.8589 980.0111 362.8388 NA 107.9112 3542.931 27621.70 63.17854 123.0147 1228.7033 1923.3727 114.71933 837.6604 544.64094 326.8218 137.55666 159.95827 1373.0632 759.5191 713.8570
200fmol 4 1407.869 338.65408 2398.0057 389.27371 299.6378 548.4764 97.77212 339.9953 115019.73 19116.837 17007.87 NA 562.7719 873.4510 1332.6237 332.2913 98.89600 1086.8900 NA 157.5208 NA 1184.514 63282.06 22846.437 42463.09 547.4942 1155.4982 309.7372 NA 108.9164 4028.918 30701.37 56.35208 225.4517 879.8159 1775.5572 133.86373 599.5360 474.84833 207.8410 169.22639 411.30891 759.5981 NA NA
50fmol 1 1256.771 591.26441 182.6504 236.88844 348.6800 NA 712.79758 193.6435 131830.66 8234.339 11845.64 NA 580.8299 913.2487 1380.3400 282.0912 182.91250 606.2433 14136.0046 157.4161 333.7562 1201.025 17620.63 7750.717 16139.56 2818.1092 1211.6563 277.6940 881.86896 149.3065 4038.116 23017.99 776.14934 127.7249 NA 469.0506 90.06842 394.8413 47.02223 213.9390 23.19320 153.44947 NA NA NA
50fmol 2 1472.071 NA 1435.5520 358.83284 347.4504 NA 247.81722 345.0750 116311.56 7605.903 12697.18 NA 551.2315 505.4586 971.2693 290.8165 125.83283 920.0514 27991.1799 145.7882 340.9205 1235.336 16001.37 7440.498 15811.83 760.7371 NA 394.0721 158.87202 177.9102 4501.100 24923.07 226.15373 153.1832 296.3736 1214.9322 110.78331 663.9165 486.90504 139.3999 86.68869 95.40880 NA NA NA
50fmol 3 1556.050 40.66504 1565.3149 89.84338 298.6623 340.5793 258.46342 270.5502 104564.53 5484.956 12536.01 1238.106 557.1447 NA 1131.1380 257.8148 66.81291 773.9335 724.9454 NA NA 1047.585 16408.15 6121.150 15935.57 1103.8303 1105.0807 318.7621 128.20868 103.7184 3742.431 25475.00 190.70466 115.1171 282.8682 857.0219 89.86919 484.3752 312.37532 312.5336 124.58086 65.88002 NA NA NA
50fmol 4 1399.717 866.26279 1192.4345 290.70430 250.1042 371.8249 162.80005 377.9867 123775.12 6424.339 11443.28 NA NA 631.6868 1251.5938 371.2717 27.73426 844.7931 16919.4242 157.2484 240.1422 1163.112 17304.55 6524.873 15485.40 816.2988 1260.9201 NA 164.75763 NA 3897.824 29814.45 231.85742 219.5657 540.5067 1000.0471 32.22857 515.4021 476.40258 181.6490 136.69320 131.36875 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 operations performed during preprocessing.

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 in an R data file (e.g., Toy_Spectronaut_Data.rda or a .RData file), first load the data file directly, then specify the loaded object as the dataSet argument.

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_filterNaN.csv) and step 3 (preprocess_filterUnique.csv) will be saved in the current working directory.

As part of the preprocessing(), a histogram of \(log_2\)-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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