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Normalization

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/normalization.Rmd
normalization.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)

Example 

dataNorm <- normalize(dataTran, normalizeType = "quant")
#> Warning: Removed 55 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).

#> Warning: Removed 55 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).

The message “Warning: Removed 55 rows containing non-finite values” indicates the presence of 55 NA (Not Available) values in the data. These NA values arise when a protein was not identified in a particular sample or condition and are automatically excluded when generating the boxplot but retained in the actual dataset.

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 10.37045 11.406514 10.956950 8.392426 8.710518 8.610420 7.829510 8.023133 16.75777 12.96499 13.97388 10.51096 9.136271 10.231965 10.048461 8.179306 8.279169 9.874410 14.201118 7.001503 8.832972 9.978488 15.16303 13.62766 14.44005 8.964155 9.574185 8.517979 6.420716 6.764393 12.07953 14.76033 6.004586 7.670711 10.129049 10.681337 7.242036 9.727210 9.376507 7.109682 7.393910 7.530379 NA NA NA
100pmol 2 11.40651 12.964987 9.727210 8.517979 8.832972 8.710518 7.242036 8.023133 16.75777 13.62766 14.20112 NA 8.964155 10.048461 10.231965 7.829510 7.670711 9.574185 10.681337 7.393910 8.610420 10.129049 15.16303 13.97388 14.44005 9.136271 9.978488 8.279169 6.764393 6.420716 12.07953 14.76033 7.109682 8.179306 9.874410 10.956950 7.001503 10.370449 8.392426 6.004586 9.376507 7.530379 10.510962 NA NA
100pmol 3 10.32522 11.893804 10.851852 7.868171 8.887142 NA 6.429596 8.646475 16.75777 12.81909 13.94448 NA 9.027184 9.940284 9.504539 8.074082 7.698334 10.467264 14.178809 7.413486 8.433160 10.022816 15.15272 13.55168 14.42169 8.758911 9.812352 8.213284 NA 6.777937 11.29130 14.74334 6.004586 8.311138 9.649565 10.097660 7.009435 10.195272 8.550204 7.121143 7.262869 7.555404 10.625304 9.244669 NA
100pmol 4 10.51096 12.079525 10.956950 8.279169 8.964155 8.610420 6.004586 8.023133 16.75777 12.96499 13.97388 NA 9.136271 10.048461 9.978488 NA 7.670711 9.376507 14.440054 7.829510 8.517979 10.231965 15.16303 13.62766 14.20112 8.710518 10.129049 8.179306 NA 7.109682 11.40651 14.76033 6.420716 7.530379 8.832972 10.681337 6.764393 9.874410 8.392426 7.001503 7.242036 7.393910 10.370449 9.727210 9.574185
200pmol 1 10.27762 12.256403 10.413259 8.356482 7.375266 8.476493 7.098768 NA 16.75777 13.10393 14.00189 10.74088 9.255807 10.077232 9.798890 8.142561 7.974610 10.164570 13.700017 7.644405 8.253204 9.927121 15.17286 14.22236 14.77650 8.577167 10.010298 8.782531 6.004586 7.222195 11.51625 14.45755 6.412260 7.506546 9.641587 10.556023 6.751494 8.669939 9.040857 7.792691 6.993948 8.905805 11.057043 NA 9.504539
200pmol 2 10.53171 11.078642 10.729201 NA 8.732804 NA 7.026553 8.267212 16.75777 12.49496 13.38772 NA 9.012072 10.120238 NA 8.149770 7.964140 9.954832 14.130668 7.608925 8.620541 10.229206 15.13045 13.88102 14.70670 8.866513 10.377729 8.386314 NA 7.145874 11.59517 14.38206 6.004586 7.766206 9.827232 9.232358 6.448756 9.504539 8.520402 6.807164 7.455730 7.307825 NA 10.036651 9.658384
200pmol 3 11.05704 8.142561 12.256403 8.356482 8.905805 8.782531 6.412260 8.669939 16.75777 14.00189 13.70002 10.74088 9.255807 10.413259 10.164570 7.792691 7.506546 10.010298 NA 7.375266 8.476493 10.277619 15.17286 14.22236 14.77650 8.577167 9.927121 8.253204 NA 6.993948 13.10393 14.45755 6.004586 7.098768 10.077232 11.516245 6.751494 9.798890 9.040857 7.974610 7.222195 7.644405 10.556023 9.641587 9.504539
200pmol 4 10.72920 8.520402 11.595175 8.732804 7.964140 9.012072 6.448756 8.149770 16.75777 13.38772 13.88102 NA 9.504539 9.954832 10.229206 8.267212 6.807164 10.036651 NA 7.455730 NA 10.531713 15.13045 14.13067 14.70670 9.232358 10.377729 8.386314 NA 7.026553 12.49496 14.38206 6.004586 7.608925 10.120238 11.078642 7.145874 9.658384 8.866513 7.766206 7.307825 8.620541 9.827232 NA NA
50pmol 1 10.72920 9.232358 7.766206 8.267212 8.732804 NA 9.658384 7.964140 16.75777 12.49496 13.88102 NA 9.504539 10.120238 10.377729 8.520402 7.608925 9.827232 14.130668 7.455730 8.620541 10.531713 14.70670 13.38772 14.38206 11.078642 10.229206 8.386314 10.036651 7.026553 11.59517 15.13045 9.954832 7.307825 NA 9.012072 6.807164 8.866513 6.448756 8.149770 6.004586 7.145874 NA NA NA
50pmol 2 10.96831 NA 10.662903 8.659793 8.785723 NA 8.190682 8.555305 16.75777 12.30540 13.84672 NA 9.753718 9.581714 10.189464 8.429646 7.035806 10.008606 14.686886 7.159242 9.099265 10.482590 14.36063 13.25790 14.10465 10.086066 NA 8.926299 7.806291 7.637117 11.47571 15.11842 8.017625 7.480137 8.298269 10.329078 6.459113 9.911682 9.362666 7.332126 6.004586 6.822962 NA NA NA
50pmol 3 11.59517 6.004586 10.729201 6.448756 8.732804 9.232358 8.149770 8.386314 16.75777 13.38772 14.13067 11.07864 9.658384 NA 10.377729 8.620541 7.026553 9.954832 9.827232 NA NA 10.531713 14.70670 13.88102 14.38206 10.036651 10.229206 9.012072 7.608925 7.455730 12.49496 15.13045 7.964140 7.766206 8.520402 10.120238 7.145874 9.504539 8.267212 8.866513 7.307825 6.807164 NA NA NA
50pmol 4 10.96831 10.008606 10.662903 8.298269 8.190682 8.785723 7.806291 8.659793 16.75777 12.30540 13.84672 NA NA 9.362666 10.482590 8.555305 6.004586 9.753718 14.360635 7.035806 8.429646 10.329078 14.68689 13.25790 14.10465 9.911682 10.189464 NA 7.332126 NA 11.47571 15.11842 7.637117 8.017625 9.581714 10.086066 6.459113 9.099265 8.926299 7.480137 7.159242 6.822962 NA NA NA

Details

Normalization is designed to address systematic biases in the data. Biases can arise from inadvertent sample grouping during generation or preparation, from variations in instrument performance during acquisition, analysis of different peptide amounts across experiments, or other reasons. These factors can artificially mask or enhance actual biological changes.

Many normalization methods have been developed for large datasets, each with its own strengths and weaknesses. The following factors should be considered when choosing a normalization method:

  1. Experiment-Specific Normalization:
    Most experiments run with UConn PMF are normalized by injection amount at the time of analysis to facilitate comparison. “Amount” is measured by UV absorbance at 280 nm, a standard method for generic protein quantification.

  2. Assumption of Non-Changing Species:
    Most biological experiments implicitly assume that the majority of measured species in an experiment will not change across conditions. This assumption is more robust the more measurements your experiment has (e.g. several thousand proteins). It may not be true at all for small datasets (tens of proteins).

If you are analyzing a batch of samples with very different complexities (e.g. a set of IPs where the control samples have tens of proteins and the experimental samples have hundreds of proteins), you should not normalize all of these together, but break them up into subsets of similar complexity.

By default, normalization is performed across samples, adjusting protein expression levels within each sample relative to the other samples. So far, this package provides eight normalization methods for use:

  1. “auto”: Auto scaling (mean centering and then dividing by the standard deviation of each variable) (Jackson 1991).

  2. “level”: Level scaling (mean centering and then dividing by the mean of each variable).

  3. “mean”: Mean centering.

  4. “median”: Median centering.

  5. “pareto”: Pareto scaling (mean centering and then dividing by the square root of the standard deviation of each variable).

  6. “quant”: Quantile normalization (Bolstad et al. 2003).

  7. “range”: Range scaling (mean centering and then dividing by the range of each variable).

  8. “vast”: Variable stability (VAST) scaling (Keun et al. 2003).

Reference

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Bolstad, B. M., R. A. Irizarry, M. Astrand, and T. P. Speed. 2003. “A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Variance and Bias.” Bioinformatics 19 (2): 185–93. https://doi.org/10.1093/bioinformatics/19.2.185.
Jackson, J. Edward. 1991. A User’s Guide to Principal Components. John Wiley & Sons.
Keun, Hector C., Timothy M. D. Ebbels, Henrik Antti, Mary E. Bollard, Olaf Beckonert, Elaine Holmes, John C. Lindon, and Jeremy K. Nicholson. 2003. “Improved Analysis of Multivariate Data by Variable Stability Scaling: Application to NMR-Based Metabolic Profiling.” Analytica Chimica Acta 490 (1–2): 265–76. https://doi.org/10.1016/S0003-2670(03)00094-1.