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Other Useful

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/other.Rmd
other.Rmd

Preliminary 

## load R package
library(msDiaLogue)
## preprocessing
fileName <- "../tests/testData/Toy_Spectronaut_Data.csv"
dataSet <- preprocessing(fileName,
                         filterNaN = TRUE, filterUnique = 2,
                         replaceBlank = TRUE, saveRm = TRUE)
## transformation
dataTran <- transform(dataSet, logFold = 2)
## normalization
dataNorm <- normalize(dataTran, normalizeType = "quant")
## imputation
dataImput <- impute.min_local(dataNorm, reportImputing = FALSE,
                              reqPercentPresent = 0.51)
## filtering
dataImput <- filterNA(dataImput, saveRm = TRUE)

dataMissing

Function dataMissing() is designed to summarize the missingness for each protein, where plot = TRUE indicates plotting the missingness, and show_labels = TRUE means that the protein names are displayed in the printed plot. Note that the visual representation is not generated by default, and the plot generation time varies with project size.

dataMissing <- dataMissing(dataNorm, plot = TRUE, show_labels = TRUE)

The percentage in the protein labels represents the proportion of missing data in the samples for that protein. For instance, the label “ZN840_HUMAN (8%)” indicates that, within all observations for the protein “ZN840_HUMAN”, 8% of the data is missing. Additionally, the percentage in the legend represents the proportion of missing data in the whole dataset. In this case, 10.2% of the data in dataNorm is missing.

Regardless of plot generation, the function dataMissing() always returns a table providing the following information:

  • count_miss: The count of missing values for each protein.

  • pct_miss_col: The percentage of missing values for each protein.

  • pct_miss_tot: The percentage of missing values for each protein relative to the total missing values in the entire dataset.

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
count_miss 0 1.000000 0 1.000000 0 4.000000 0 1.000000 0 0 0 8.00000 1.000000 1.000000 1.000000 1.000000 0 0 2.000000 1.000000 2.000000 0 0 0 0 0 1.000000 1.000000 5.000000 1.000000 0 0 0 0 1.000000 0 0 0 0 0 0 0 6.00000 8.00000 8.00000
pct_miss_col 0 8.333333 0 8.333333 0 33.333333 0 8.333333 0 0 0 66.66667 8.333333 8.333333 8.333333 8.333333 0 0 16.666667 8.333333 16.666667 0 0 0 0 0 8.333333 8.333333 41.666667 8.333333 0 0 0 0 8.333333 0 0 0 0 0 0 0 50.00000 66.66667 66.66667
pct_miss_tot 0 1.818182 0 1.818182 0 7.272727 0 1.818182 0 0 0 14.54545 1.818182 1.818182 1.818182 1.818182 0 0 3.636364 1.818182 3.636364 0 0 0 0 0 1.818182 1.818182 9.090909 1.818182 0 0 0 0 1.818182 0 0 0 0 0 0 0 10.90909 14.54545 14.54545

For example, in the case of the protein “ZN840_HUMAN,” there are 1 NA values in the samples, representing 8.33% of the missing data for “ZN840_HUMAN” within that sample and 1.82% of the total missing data in the entire dataset.

pullProteinPath

The function pullProteinPath() allows you to see the quantitative values associated with specific proteins at each step of processing, using either the exact match argument listname =, or the text match argument regexName =, or both.

This can be useful for questions such as,

  • “Which of the values for my favorite protein were actually measured, vs. imputed?”
  • “Why didn’t my favorite protein make it to the final list? At what step was it filtered out?”.

It can also be used to check whether the fold-change observed for a specific protein is an artifact from one of the processing steps.

Check <- pullProteinPath(
  listName = c("LYSC_CHICK", "BGAL_ECOLI"),
  regexName = c("BOVIN"),
  by = "PG.ProteinNames",
  dataSetList = list(Initial = dataSet,
                     Transformed = dataTran,
                     Normalized = dataNorm,
                     Imputed = dataImput))
PG.ProteinNames PG.Genes PG.ProteinAccessions PG.ProteinDescriptions R.Condition R.Replicate Initial Transformed Normalized Imputed
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 100pmol 1 111209.703 16.76292 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 100pmol 2 111659.883 16.76875 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 100pmol 3 105982.914 16.69347 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 100pmol 4 104442.562 16.67235 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 200pmol 1 109245.289 16.73721 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 200pmol 2 113357.508 16.79052 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 200pmol 3 114321.836 16.80274 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 200pmol 4 116439.820 16.82923 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 50pmol 1 117803.492 16.84602 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 50pmol 2 110086.680 16.74828 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 50pmol 3 105640.203 16.68880 16.75777 16.75777
ALBU_BOVIN ALB CON__P02769 Bovine serum albumin 50pmol 4 110446.000 16.75298 16.75777 16.75777
BGAL_ECOLI lacZ P00722 Beta-galactosidase 100pmol 1 23840.031 14.54110 14.44005 14.44005
BGAL_ECOLI lacZ P00722 Beta-galactosidase 100pmol 2 23963.307 14.54854 14.44005 14.44005
BGAL_ECOLI lacZ P00722 Beta-galactosidase 100pmol 3 22957.350 14.48667 14.42169 14.42169
BGAL_ECOLI lacZ P00722 Beta-galactosidase 100pmol 4 22311.297 14.44549 14.20112 14.20112
BGAL_ECOLI lacZ P00722 Beta-galactosidase 200pmol 1 41234.672 15.33157 14.77650 14.77650
BGAL_ECOLI lacZ P00722 Beta-galactosidase 200pmol 2 42899.434 15.38867 14.70670 14.70670
BGAL_ECOLI lacZ P00722 Beta-galactosidase 200pmol 3 42904.945 15.38886 14.77650 14.77650
BGAL_ECOLI lacZ P00722 Beta-galactosidase 200pmol 4 43279.844 15.40141 14.70670 14.70670
BGAL_ECOLI lacZ P00722 Beta-galactosidase 50pmol 1 14728.673 13.84634 14.38206 14.38206
BGAL_ECOLI lacZ P00722 Beta-galactosidase 50pmol 2 14736.710 13.84713 14.10465 14.10465
BGAL_ECOLI lacZ P00722 Beta-galactosidase 50pmol 3 14160.203 13.78955 14.38206 14.38206
BGAL_ECOLI lacZ P00722 Beta-galactosidase 50pmol 4 14758.731 13.84928 14.10465 14.10465
CYC_BOVIN CYCS CON__P62894 Cytochrome c 100pmol 1 10737.953 13.39043 12.96499 12.96499
CYC_BOVIN CYCS CON__P62894 Cytochrome c 100pmol 2 10655.384 13.37929 13.62766 13.62766
CYC_BOVIN CYCS CON__P62894 Cytochrome c 100pmol 3 10663.714 13.38042 12.81909 12.81909
CYC_BOVIN CYCS CON__P62894 Cytochrome c 100pmol 4 10843.115 13.40449 12.96499 12.96499
CYC_BOVIN CYCS CON__P62894 Cytochrome c 200pmol 1 19524.863 14.25302 13.10393 13.10393
CYC_BOVIN CYCS CON__P62894 Cytochrome c 200pmol 2 20072.297 14.29292 12.49496 12.49496
CYC_BOVIN CYCS CON__P62894 Cytochrome c 200pmol 3 20787.127 14.34340 14.00189 14.00189
CYC_BOVIN CYCS CON__P62894 Cytochrome c 200pmol 4 19924.240 14.28224 13.38772 13.38772
CYC_BOVIN CYCS CON__P62894 Cytochrome c 50pmol 1 6758.298 12.72244 12.49496 12.49496
CYC_BOVIN CYCS CON__P62894 Cytochrome c 50pmol 2 6721.135 12.71449 12.30540 12.30540
CYC_BOVIN CYCS CON__P62894 Cytochrome c 50pmol 3 6172.877 12.59173 13.38772 13.38772
CYC_BOVIN CYCS CON__P62894 Cytochrome c 50pmol 4 6028.398 12.55756 12.30540 12.30540
LYSC_CHICK LYZ P00698 Lysozyme C 100pmol 1 13798.590 13.75223 13.62766 13.62766
LYSC_CHICK LYZ P00698 Lysozyme C 100pmol 2 13880.411 13.76076 13.97388 13.97388
LYSC_CHICK LYZ P00698 Lysozyme C 100pmol 3 13723.719 13.74438 13.55168 13.55168
LYSC_CHICK LYZ P00698 Lysozyme C 100pmol 4 13944.603 13.76742 13.62766 13.62766
LYSC_CHICK LYZ P00698 Lysozyme C 200pmol 1 24344.188 14.57129 14.22236 14.22236
LYSC_CHICK LYZ P00698 Lysozyme C 200pmol 2 24742.227 14.59469 13.88102 13.88102
LYSC_CHICK LYZ P00698 Lysozyme C 200pmol 3 24803.633 14.59826 14.22236 14.22236
LYSC_CHICK LYZ P00698 Lysozyme C 200pmol 4 26381.047 14.68721 14.13067 14.13067
LYSC_CHICK LYZ P00698 Lysozyme C 50pmol 1 7169.955 12.80775 13.38772 13.38772
LYSC_CHICK LYZ P00698 Lysozyme C 50pmol 2 7797.536 12.92880 13.25790 13.25790
LYSC_CHICK LYZ P00698 Lysozyme C 50pmol 3 7432.793 12.85969 13.88102 13.88102
LYSC_CHICK LYZ P00698 Lysozyme C 50pmol 4 7543.633 12.88104 13.25790 13.25790
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 100pmol 1 15097.670 13.88204 13.97388 13.97388
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 100pmol 2 15840.281 13.95131 14.20112 14.20112
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 100pmol 3 15022.215 13.87481 13.94448 13.94448
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 100pmol 4 15160.493 13.88803 13.97388 13.97388
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 200pmol 1 21577.973 14.39727 14.00189 14.00189
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 200pmol 2 22968.959 14.48740 13.38772 13.38772
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 200pmol 3 20720.127 14.33875 13.70002 13.70002
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 200pmol 4 22153.398 14.43524 13.88102 13.88102
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 50pmol 1 12183.812 13.57268 13.88102 13.88102
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 50pmol 2 12521.783 13.61215 13.84672 13.84672
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 50pmol 3 11926.220 13.54185 14.13067 14.13067
TRFE_BOVIN TF CON__Q0IIK2 Serotransferrin (UP merge to Q29443) 50pmol 4 12021.495 13.55333 13.84672 13.84672

sortcondition 

dataSort <- sortcondition(dataSet)

trimFASTA

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