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Analysis

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/analysis.Rmd
analysis.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)

The functions in the analysis module calculate the results that can be used in subsequent visualizations.

Note: The following analyses compare all other conditions against the reference condition, which is specified by the argument ref, for multiple comparisons. If ref is not provided, it will be automatically generated by all the combinations of two conditions, based on the level attributes of the condition.
For example, suppose there are three conditions in the data: “A”, “B”, and “C”. If you specify ref = "A", then the result includes two comparisons: “B-A” and “C-A”. If ref = NULL, there will be three comparisons: “A-B”, “A-C”, and “B-C”.

Student’s t-test

Example 

anlys_t <- analyze.t(dataImput, ref = "50pmol", adjust.method = "none")
#> Data are essentially constant.
#> Data are essentially constant.
Note: In the Student’s t-test, a warning message might appear, stating “Data are essentially constant,” which means that the data contain proteins with the same value in all samples. In this case, the p-value of t-test returns NaN. 
#> $`100pmol-50pmol`
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
100pmol mean 10.6532858 12.0862074 10.6232407 8.2644365 8.8486965 6.8764321 8.1789684 16.75777 13.0941802 14.0233390 9.0659703 10.0672927 9.9408634 7.9781022 7.8297313 9.8230915 7.4096023 8.5986327 10.0905795 15.1604531 13.6952175 14.3757288 8.892464 9.8735187 8.2974346 6.7681823 11.7142153 14.7560796 6.3848929 7.9228836 9.6214990 10.6043210 7.0043419 10.0418355 8.6778910 6.8092287 7.8188307 7.5025180
50pmol mean 11.0652499 7.8125342 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.278260 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.3934284 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.8997407
difference -0.4119641 4.2736732 0.6679373 0.3459290 0.2381933 -1.5748494 -0.2124196 0.00000 0.4708090 0.0970569 -0.5393248 0.4604719 -0.4160144 -0.5533715 0.9107635 -0.0630057 0.2379563 -0.0461417 -0.3781941 0.5452255 0.2490808 0.1323776 -1.385796 -0.3358160 -0.3803155 -0.5183059 -0.0461744 -0.3683573 -2.0085355 0.2799353 0.9468351 0.7174576 0.2865258 0.6963357 0.4266578 -1.1479077 1.1997707 0.6027773
p-value 0.2425281 0.0223332 0.4450987 0.5455559 0.1909446 0.0348158 0.3685157 NaN 0.1922051 0.3275609 0.0005957 0.0767642 0.0683551 0.0041165 0.0651420 0.8119403 0.2805476 0.8086313 0.0024306 0.0076435 0.2145975 0.2343297 0.009692 0.0658082 0.1068935 0.0481610 0.8918516 0.0000000 0.0232926 0.3007562 0.0633640 0.0942018 0.1959323 0.0503672 0.5680738 0.0436514 0.1120976 0.0022576 
#> $`200pmol-50pmol`
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
200pmol mean 10.6488941 9.9995022 11.2485095 8.4505628 8.2445037 6.7465842 8.3091726 16.75777 13.2471248 13.7426617 9.2570565 10.1413903 9.9978891 8.0880583 7.5631150 10.0415877 7.5210813 8.4008606 10.2414149 15.1516556 14.1141044 14.7415973 8.8133014 10.1732192 8.4520910 7.0971425 12.1775775 14.4198014 6.1065048 7.4951113 9.9165721 10.5958169 6.7744045 9.4079382 8.8671576 7.5851679 7.2449244 8.1196440
50pmol mean 11.0652499 7.8125342 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.2782600 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.3934284 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.8997407
difference -0.4163558 2.1869680 1.2932062 0.5320553 -0.3659995 -1.7046973 -0.0822154 0.00000 0.6237535 -0.1836205 -0.3482386 0.5345694 -0.3589887 -0.4434153 0.6441472 0.1554905 0.3494352 -0.2439139 -0.2273586 0.5364280 0.6679676 0.4982461 -1.4649585 -0.0361156 -0.2256591 -0.1893458 0.4171878 -0.7046356 -2.2869237 -0.1478371 1.2419082 0.7089535 0.0565885 0.0624383 0.6159244 -0.3719685 0.6258644 1.2199033
p-value 0.1446884 0.1826838 0.1875421 0.3662078 0.3927857 0.0181442 0.6911235 NaN 0.1771520 0.2818360 0.0319217 0.0503531 0.0448461 0.0166531 0.1885601 0.0812056 0.0303572 0.2404181 0.1646796 0.0073303 0.0124946 0.0064381 0.0058656 0.7824826 0.3171743 0.3158950 0.3983925 0.0000458 0.0205236 0.5114326 0.0210858 0.2748735 0.8040085 0.8612941 0.4109857 0.4319860 0.1765836 0.0465999 
#> $total
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
100pmol mean 10.6532858 12.0862074 10.6232407 8.2644365 8.8486965 6.8764321 8.1789684 16.75777 13.0941802 14.0233390 9.0659703 10.0672927 9.9408634 7.9781022 7.8297313 9.8230915 7.4096023 8.5986327 10.0905795 15.1604531 13.6952175 14.3757288 8.8924636 9.8735187 8.2974346 6.7681823 11.7142153 14.7560796 6.3848929 7.9228836 9.6214990 10.6043210 7.0043419 10.0418355 8.6778910 6.8092287 7.8188307 7.5025180
200pmol mean 10.6488941 9.9995022 11.2485095 8.4505628 8.2445037 6.7465842 8.3091726 16.75777 13.2471248 13.7426617 9.2570565 10.1413903 9.9978891 8.0880583 7.5631150 10.0415877 7.5210813 8.4008606 10.2414149 15.1516556 14.1141044 14.7415973 8.8133014 10.1732192 8.4520910 7.0971425 12.1775775 14.4198014 6.1065048 7.4951113 9.9165721 10.5958169 6.7744045 9.4079382 8.8671576 7.5851679 7.2449244 8.1196440
50pmol mean 11.0652499 7.8125342 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.2782600 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.3934284 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.8997407
100pmol-50pmol: difference -0.4119641 4.2736732 0.6679373 0.3459290 0.2381933 -1.5748494 -0.2124196 0.00000 0.4708090 0.0970569 -0.5393248 0.4604719 -0.4160144 -0.5533715 0.9107635 -0.0630057 0.2379563 -0.0461417 -0.3781941 0.5452255 0.2490808 0.1323776 -1.3857964 -0.3358160 -0.3803155 -0.5183059 -0.0461744 -0.3683573 -2.0085355 0.2799353 0.9468351 0.7174576 0.2865258 0.6963357 0.4266578 -1.1479077 1.1997707 0.6027773
100pmol-50pmol: p-value 0.2425281 0.0223332 0.4450987 0.5455559 0.1909446 0.0348158 0.3685157 NaN 0.1922051 0.3275609 0.0005957 0.0767642 0.0683551 0.0041165 0.0651420 0.8119403 0.2805476 0.8086313 0.0024306 0.0076435 0.2145975 0.2343297 0.0096920 0.0658082 0.1068935 0.0481610 0.8918516 0.0000000 0.0232926 0.3007562 0.0633640 0.0942018 0.1959323 0.0503672 0.5680738 0.0436514 0.1120976 0.0022576
200pmol-50pmol: difference -0.4163558 2.1869680 1.2932062 0.5320553 -0.3659995 -1.7046973 -0.0822154 0.00000 0.6237535 -0.1836205 -0.3482386 0.5345694 -0.3589887 -0.4434153 0.6441472 0.1554905 0.3494352 -0.2439139 -0.2273586 0.5364280 0.6679676 0.4982461 -1.4649585 -0.0361156 -0.2256591 -0.1893458 0.4171878 -0.7046356 -2.2869237 -0.1478371 1.2419082 0.7089535 0.0565885 0.0624383 0.6159244 -0.3719685 0.6258644 1.2199033
200pmol-50pmol: p-value 0.1446884 0.1826838 0.1875421 0.3662078 0.3927857 0.0181442 0.6911235 NaN 0.1771520 0.2818360 0.0319217 0.0503531 0.0448461 0.0166531 0.1885601 0.0812056 0.0303572 0.2404181 0.1646796 0.0073303 0.0124946 0.0064381 0.0058656 0.7824826 0.3171743 0.3158950 0.3983925 0.0000458 0.0205236 0.5114326 0.0210858 0.2748735 0.8040085 0.8612941 0.4109857 0.4319860 0.1765836 0.0465999

Details

The Student’s t-test is used to compare the means between two conditions for each protein, reporting both the difference in means between the conditions and the p-value of the test.

The argument adjust.method is used to specify the testing correction procedure to be applied to p-values. This adjustment is very common in DNA or RNA-Seq analyses, where datasets are very large and where researchers are most interested in controlling the Type I error rate when conducting multiple comparisons.

However, for mass spectrometry-based proteomics results, the dataset sizes are smaller than in sequencing analyses, and testing corrections can be too harsh of a threshold to apply. Most often, applying any testing correction to proteomics data results in there being zero significant changes. This does not mean that nothing is meaningfully changing in your dataset. It does mean that these corrections are usually not a useful tool for finding biologically-relevant changes in your dataset.

Also keep in mind that reducing Type I error typically comes at the cost of increasing Type II error, and vice versa. There is no way to eliminate all error; each researcher must decide whether they are more comfortable with having more false positives or more false negatives in the dataset, and choose their analysis strategies accordingly.

UConn PMF recommends not applying testing corrections to your proteomics dataset, but if you would like to explore the effects of doing so, several methods are provided below:

  1. “BH” or its alias “fdr”: Benjamini and Hochberg (1995).

  2. “BY”: Benjamini and Yekutieli (2001).

  3. “bonferroni”: Bonferroni (1936).

  4. “hochberg”: Hochberg (1988).

  5. “holm”: Holm (1979).

  6. “hommel”: Hommel (1988).

Each method offers its own balance between statistical power and error control. The default value "none" indicates that no correction is applied.

Empirical Bayes moderated t-test

Example 

anlys_modt <- analyze.mod_t(dataImput, ref = "50pmol", adjust.method = "none")
#> Warning: Zero sample variances detected, have been offset away from zero
Note: In the moderated t-test, a warning message might occur stating, “Zero sample variances detected, have been offset away from zero.” This warning corresponds to examples of proteins that exhibited identical quant values, either pre- or post-imputation, and therefore no variance is present across conditions for those proteins. This does not impede downstream analysis; it merely serves to alert users to its occurrence. 
#> $`100pmol-50pmol`
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
100pmol mean 10.6532858 12.0862074 10.6232407 8.2644365 8.8486965 6.8764321 8.1789684 16.75777 13.094180 14.0233390 9.0659703 10.0672927 9.9408634 7.9781022 7.8297313 9.8230915 7.4096023 8.5986327 10.0905795 15.1604531 13.6952175 14.3757288 8.8924636 9.8735187 8.2974346 6.7681823 11.7142153 14.7560796 6.3848929 7.9228836 9.6214990 10.6043210 7.0043419 10.0418355 8.6778910 6.8092287 7.818831 7.5025180
50pmol mean 11.0652499 7.8125342 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.623371 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.2782600 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.3934284 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.619060 6.8997407
difference -0.4119641 4.2736732 0.6679373 0.3459290 0.2381933 -1.5748494 -0.2124196 0.00000 0.470809 0.0970569 -0.5393248 0.4604719 -0.4160144 -0.5533715 0.9107635 -0.0630057 0.2379563 -0.0461417 -0.3781941 0.5452255 0.2490808 0.1323776 -1.3857964 -0.3358160 -0.3803155 -0.5183059 -0.0461744 -0.3683573 -2.0085355 0.2799353 0.9468351 0.7174576 0.2865258 0.6963357 0.4266578 -1.1479077 1.199771 0.6027773
p-value 0.1692657 0.0041928 0.3594271 0.4188776 0.4476151 0.0077729 0.3082409 1.00000 0.205110 0.4719321 0.0003809 0.0210540 0.0273503 0.0007011 0.0290176 0.7547940 0.1763304 0.7787863 0.0090768 0.0000260 0.1367383 0.1511826 0.0002994 0.0325244 0.0523968 0.0126619 0.9082025 0.0000018 0.0015443 0.2394099 0.0184304 0.1590979 0.1596566 0.0381075 0.4486484 0.0171955 0.036571 0.0782396 
#> $`200pmol-50pmol`
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
200pmol mean 10.6488941 9.9995022 11.2485095 8.4505628 8.2445037 6.7465842 8.3091726 16.75777 13.2471248 13.7426617 9.2570565 10.1413903 9.9978891 8.0880583 7.5631150 10.0415877 7.5210813 8.4008606 10.2414149 15.1516556 14.1141044 14.7415973 8.813301 10.1732192 8.4520910 7.0971425 12.1775775 14.4198014 6.1065048 7.4951113 9.9165721 10.5958169 6.7744045 9.4079382 8.8671576 7.5851679 7.2449244 8.1196440
50pmol mean 11.0652499 7.8125342 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.278260 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.3934284 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.8997407
difference -0.4163558 2.1869680 1.2932062 0.5320553 -0.3659995 -1.7046973 -0.0822154 0.00000 0.6237535 -0.1836205 -0.3482386 0.5345694 -0.3589887 -0.4434153 0.6441472 0.1554905 0.3494352 -0.2439139 -0.2273586 0.5364280 0.6679676 0.4982461 -1.464959 -0.0361156 -0.2256591 -0.1893458 0.4171878 -0.7046356 -2.2869237 -0.1478371 1.2419082 0.7089535 0.0565885 0.0624383 0.6159244 -0.3719685 0.6258644 1.2199033
p-value 0.1651853 0.0881485 0.0926706 0.2239144 0.2525044 0.0049216 0.6864892 1.00000 0.1028334 0.1877393 0.0069376 0.0099405 0.0500060 0.0031304 0.1016953 0.4466320 0.0584428 0.1582493 0.0809141 0.0000298 0.0014873 0.0001655 0.000195 0.7948930 0.2207008 0.2936324 0.3105040 0.0000000 0.0006214 0.5236720 0.0042098 0.1636145 0.7701943 0.8346277 0.2815464 0.3767021 0.2366530 0.0026681 
#> $total
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
100pmol mean 10.6532858 12.0862074 10.6232407 8.2644365 8.8486965 6.8764321 8.1789684 16.75777 13.0941802 14.0233390 9.0659703 10.0672927 9.9408634 7.9781022 7.8297313 9.8230915 7.4096023 8.5986327 10.0905795 15.1604531 13.6952175 14.3757288 8.8924636 9.8735187 8.2974346 6.7681823 11.7142153 14.7560796 6.3848929 7.9228836 9.6214990 10.6043210 7.0043419 10.0418355 8.6778910 6.8092287 7.8188307 7.5025180
200pmol mean 10.6488941 9.9995022 11.2485095 8.4505628 8.2445037 6.7465842 8.3091726 16.75777 13.2471248 13.7426617 9.2570565 10.1413903 9.9978891 8.0880583 7.5631150 10.0415877 7.5210813 8.4008606 10.2414149 15.1516556 14.1141044 14.7415973 8.8133014 10.1732192 8.4520910 7.0971425 12.1775775 14.4198014 6.1065048 7.4951113 9.9165721 10.5958169 6.7744045 9.4079382 8.8671576 7.5851679 7.2449244 8.1196440
50pmol mean 11.0652499 7.8125342 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.2782600 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.3934284 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.8997407
100pmol-50pmol: difference -0.4119641 4.2736732 0.6679373 0.3459290 0.2381933 -1.5748494 -0.2124196 0.00000 0.4708090 0.0970569 -0.5393248 0.4604719 -0.4160144 -0.5533715 0.9107635 -0.0630057 0.2379563 -0.0461417 -0.3781941 0.5452255 0.2490808 0.1323776 -1.3857964 -0.3358160 -0.3803155 -0.5183059 -0.0461744 -0.3683573 -2.0085355 0.2799353 0.9468351 0.7174576 0.2865258 0.6963357 0.4266578 -1.1479077 1.1997707 0.6027773
100pmol-50pmol: p-value 0.1692657 0.0041928 0.3594271 0.4188776 0.4476151 0.0077729 0.3082409 1.00000 0.2051100 0.4719321 0.0003809 0.0210540 0.0273503 0.0007011 0.0290176 0.7547940 0.1763304 0.7787863 0.0090768 0.0000260 0.1367383 0.1511826 0.0002994 0.0325244 0.0523968 0.0126619 0.9082025 0.0000018 0.0015443 0.2394099 0.0184304 0.1590979 0.1596566 0.0381075 0.4486484 0.0171955 0.0365710 0.0782396
200pmol-50pmol: difference -0.4163558 2.1869680 1.2932062 0.5320553 -0.3659995 -1.7046973 -0.0822154 0.00000 0.6237535 -0.1836205 -0.3482386 0.5345694 -0.3589887 -0.4434153 0.6441472 0.1554905 0.3494352 -0.2439139 -0.2273586 0.5364280 0.6679676 0.4982461 -1.4649585 -0.0361156 -0.2256591 -0.1893458 0.4171878 -0.7046356 -2.2869237 -0.1478371 1.2419082 0.7089535 0.0565885 0.0624383 0.6159244 -0.3719685 0.6258644 1.2199033
200pmol-50pmol: p-value 0.1651853 0.0881485 0.0926706 0.2239144 0.2525044 0.0049216 0.6864892 1.00000 0.1028334 0.1877393 0.0069376 0.0099405 0.0500060 0.0031304 0.1016953 0.4466320 0.0584428 0.1582493 0.0809141 0.0000298 0.0014873 0.0001655 0.0001950 0.7948930 0.2207008 0.2936324 0.3105040 0.0000000 0.0006214 0.5236720 0.0042098 0.1636145 0.7701943 0.8346277 0.2815464 0.3767021 0.2366530 0.0026681

Details

The main distinction between the Student’s and empirical Bayes moderated t-tests (Smyth 2004) lies in how variance is computed. While the Student’s t-test calculates variance based on the data available for each protein individually (which will be limited by the number of replicates included for each condition), the moderated t-test utilizes information from all replicates of every protein in the current dataset to calculate variance.

Wilcoxon test

Example 

anlys_wilcox <- analyze.wilcox(dataImput, ref = "50pmol", adjust.method = "none")
Note: In the Wilcoxon test, the warning message “cannot compute exact p-value with ties.” may be displayed. This warning means that some values with tied rankings and the sample size is lower than 50, which prevents the exact p-value from being calculated. In such cases, a normal approximation is used. If all samples for a protein have the same value, the corresponding p-value returns NaN. 
#> $`100pmol-50pmol`
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
100pmol mean 10.6532858 12.086207 10.6232407 8.2644365 8.8486965 6.8764321 8.1789684 16.75777 13.0941802 14.0233390 9.0659703 10.0672927 9.9408634 7.9781022 7.8297313 9.8230915 7.4096023 8.5986327 10.0905795 15.1604531 13.6952175 14.3757288 8.8924636 9.8735187 8.2974346 6.7681823 11.7142153 14.7560796 6.384893 7.9228836 9.6214990 10.6043210 7.0043419 10.0418355 8.6778910 6.8092287 7.8188307 7.5025180
50pmol mean 11.0652499 7.812534 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.2782600 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.393428 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.8997407
difference -0.4119641 4.273673 0.6679373 0.3459290 0.2381933 -1.5748494 -0.2124196 0.00000 0.4708090 0.0970569 -0.5393248 0.4604719 -0.4160144 -0.5533715 0.9107635 -0.0630057 0.2379563 -0.0461417 -0.3781941 0.5452255 0.2490808 0.1323776 -1.3857964 -0.3358160 -0.3803155 -0.5183059 -0.0461744 -0.3683573 -2.008535 0.2799353 0.9468351 0.7174576 0.2865258 0.6963357 0.4266578 -1.1479077 1.1997707 0.6027773
p-value 0.1912670 0.029401 0.1885823 0.8857143 0.1912670 0.0571429 0.6572552 NaN 0.1885823 0.1885823 0.0284295 0.1885823 0.0590719 0.0294010 0.0294010 0.6857143 0.6631172 0.8845494 0.0294010 0.0255801 0.1885823 0.1059111 0.0285714 0.0284295 0.1102102 0.1102102 0.6611967 0.0246533 0.029401 0.3428571 0.0590719 0.1102102 0.3094241 0.1142857 0.6631172 0.0285714 0.1102102 0.0284295 
#> $`200pmol-50pmol`
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
200pmol mean 10.6488941 9.9995022 11.2485095 8.4505628 8.2445037 6.7465842 8.3091726 16.75777 13.2471248 13.7426617 9.2570565 10.1413903 9.9978891 8.0880583 7.5631150 10.0415877 7.5210813 8.4008606 10.2414149 15.1516556 14.1141044 14.7415973 8.813301 10.1732192 8.4520910 7.0971425 12.1775775 14.4198014 6.1065048 7.4951113 9.916572 10.5958169 6.7744045 9.4079382 8.8671576 7.5851679 7.2449244 8.119644
50pmol mean 11.0652499 7.8125342 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.278260 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.3934284 7.6429483 8.674664 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.899741
difference -0.4163558 2.1869680 1.2932062 0.5320553 -0.3659995 -1.7046973 -0.0822154 0.00000 0.6237535 -0.1836205 -0.3482386 0.5345694 -0.3589887 -0.4434153 0.6441472 0.1554905 0.3494352 -0.2439139 -0.2273586 0.5364280 0.6679676 0.4982461 -1.464959 -0.0361156 -0.2256591 -0.1893458 0.4171878 -0.7046356 -2.2869237 -0.1478371 1.241908 0.7089535 0.0565885 0.0624383 0.6159244 -0.3719685 0.6258644 1.219903
p-value 0.2425256 0.3094241 0.2425256 0.1831502 0.6572552 0.0285714 0.8845494 NaN 0.1858767 0.5589857 0.0530079 0.1440506 0.0575470 0.0285714 0.3428571 0.0814291 0.0795941 0.3778216 0.1831502 0.0274686 0.0396087 0.0265187 0.029401 1.0000000 0.2817179 0.4595974 0.3035251 0.0265187 0.0265187 0.7715034 0.029401 0.2000000 1.0000000 1.0000000 0.6631172 0.6857143 0.2425256 0.029401 
#> $total
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
100pmol mean 10.6532858 12.0862074 10.6232407 8.2644365 8.8486965 6.8764321 8.1789684 16.75777 13.0941802 14.0233390 9.0659703 10.0672927 9.9408634 7.9781022 7.8297313 9.8230915 7.4096023 8.5986327 10.0905795 15.1604531 13.6952175 14.3757288 8.8924636 9.8735187 8.2974346 6.7681823 11.7142153 14.7560796 6.3848929 7.9228836 9.6214990 10.6043210 7.0043419 10.0418355 8.6778910 6.8092287 7.8188307 7.5025180
200pmol mean 10.6488941 9.9995022 11.2485095 8.4505628 8.2445037 6.7465842 8.3091726 16.75777 13.2471248 13.7426617 9.2570565 10.1413903 9.9978891 8.0880583 7.5631150 10.0415877 7.5210813 8.4008606 10.2414149 15.1516556 14.1141044 14.7415973 8.8133014 10.1732192 8.4520910 7.0971425 12.1775775 14.4198014 6.1065048 7.4951113 9.9165721 10.5958169 6.7744045 9.4079382 8.8671576 7.5851679 7.2449244 8.1196440
50pmol mean 11.0652499 7.8125342 9.9553033 7.9185075 8.6105032 8.4512816 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.2782600 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.3934284 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.8997407
100pmol-50pmol: difference -0.4119641 4.2736732 0.6679373 0.3459290 0.2381933 -1.5748494 -0.2124196 0.00000 0.4708090 0.0970569 -0.5393248 0.4604719 -0.4160144 -0.5533715 0.9107635 -0.0630057 0.2379563 -0.0461417 -0.3781941 0.5452255 0.2490808 0.1323776 -1.3857964 -0.3358160 -0.3803155 -0.5183059 -0.0461744 -0.3683573 -2.0085355 0.2799353 0.9468351 0.7174576 0.2865258 0.6963357 0.4266578 -1.1479077 1.1997707 0.6027773
100pmol-50pmol: p-value 0.1912670 0.0294010 0.1885823 0.8857143 0.1912670 0.0571429 0.6572552 NaN 0.1885823 0.1885823 0.0284295 0.1885823 0.0590719 0.0294010 0.0294010 0.6857143 0.6631172 0.8845494 0.0294010 0.0255801 0.1885823 0.1059111 0.0285714 0.0284295 0.1102102 0.1102102 0.6611967 0.0246533 0.0294010 0.3428571 0.0590719 0.1102102 0.3094241 0.1142857 0.6631172 0.0285714 0.1102102 0.0284295
200pmol-50pmol: difference -0.4163558 2.1869680 1.2932062 0.5320553 -0.3659995 -1.7046973 -0.0822154 0.00000 0.6237535 -0.1836205 -0.3482386 0.5345694 -0.3589887 -0.4434153 0.6441472 0.1554905 0.3494352 -0.2439139 -0.2273586 0.5364280 0.6679676 0.4982461 -1.4649585 -0.0361156 -0.2256591 -0.1893458 0.4171878 -0.7046356 -2.2869237 -0.1478371 1.2419082 0.7089535 0.0565885 0.0624383 0.6159244 -0.3719685 0.6258644 1.2199033
200pmol-50pmol: p-value 0.2425256 0.3094241 0.2425256 0.1831502 0.6572552 0.0285714 0.8845494 NaN 0.1858767 0.5589857 0.0530079 0.1440506 0.0575470 0.0285714 0.3428571 0.0814291 0.0795941 0.3778216 0.1831502 0.0274686 0.0396087 0.0265187 0.0294010 1.0000000 0.2817179 0.4595974 0.3035251 0.0265187 0.0265187 0.7715034 0.0294010 0.2000000 1.0000000 1.0000000 0.6631172 0.6857143 0.2425256 0.0294010

Details

The Wilcoxon test is a non-parametric alternative to the two-sample t-test. If paired = TRUE, a Wilcoxon signed-rank test is performed to test the null hypothesis that the distribution of the difference between the two conditions for the protein is symmetric about zero. If paired = FALSE, a Wilcoxon rank-sum test (also known as Mann-Whitney test) is performed to test the null hypothesis that the distribution of the two conditions for the protein are the same.

MA

Example 

anlys_ma <- analyze.ma(dataImput, ref = "50pmol")
#> $`100pmol-50pmol`
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
100pmol mean 10.6532858 12.086207 10.6232407 8.264437 8.8486965 6.876432 8.1789684 16.75777 13.094180 14.0233390 9.0659703 10.0672927 9.9408634 7.9781022 7.8297313 9.8230915 7.4096023 8.5986327 10.0905795 15.1604531 13.6952175 14.3757288 8.892464 9.873519 8.2974346 6.7681823 11.7142153 14.7560796 6.384893 7.9228836 9.6214990 10.6043210 7.0043419 10.0418355 8.6778910 6.809229 7.818831 7.5025180
50pmol mean 11.0652499 7.812534 9.9553033 7.918507 8.6105032 8.451282 8.3913880 16.75777 12.623371 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.6152277 13.4461368 14.2433512 10.278260 10.209335 8.6777501 7.2864883 11.7603897 15.1244369 8.393428 7.6429483 8.6746639 9.8868633 6.7178161 9.3454998 8.2512331 7.957136 6.619060 6.8997407
A 10.8592679 9.949371 10.2892720 8.091472 8.7295998 7.663857 8.2851782 16.75777 12.858776 13.9748106 9.3356327 9.8370568 10.1488706 8.2547879 7.3743495 9.8545943 7.2906242 8.6217036 10.2796765 14.8878404 13.5706772 14.3095400 9.585362 10.041427 8.4875924 7.0273353 11.7373025 14.9402583 7.389161 7.7829160 9.1480815 10.2455921 6.8610790 9.6936677 8.4645621 7.383183 7.218945 7.2011293
M -0.4119641 4.273673 0.6679373 0.345929 0.2381933 -1.574849 -0.2124196 0.00000 0.470809 0.0970569 -0.5393248 0.4604719 -0.4160144 -0.5533715 0.9107635 -0.0630057 0.2379563 -0.0461417 -0.3781941 0.5452255 0.2490808 0.1323776 -1.385796 -0.335816 -0.3803155 -0.5183059 -0.0461744 -0.3683573 -2.008535 0.2799353 0.9468351 0.7174576 0.2865258 0.6963357 0.4266578 -1.147908 1.199771 0.6027773 
#> $`200pmol-50pmol`
NUD4B_HUMAN A0A7P0T808_HUMAN A0A8I5KU53_HUMAN ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN C9JNU9_HUMAN ALBU_BOVIN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN H7C3M7_HUMAN TLR3_HUMAN LRIG2_HUMAN RAB3D_HUMAN ADH1_YEAST LYSC_CHICK BGAL_ECOLI CYTA_HUMAN KPCB_HUMAN LIPL_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
200pmol mean 10.6488941 9.999502 11.248510 8.4505628 8.2445037 6.746584 8.3091726 16.75777 13.2471248 13.7426617 9.2570565 10.1413903 9.9978891 8.0880583 7.5631150 10.0415877 7.5210813 8.4008606 10.2414149 15.151656 14.1141044 14.7415973 8.813301 10.1732192 8.4520910 7.0971425 12.1775775 14.4198014 6.106505 7.4951113 9.916572 10.5958169 6.7744045 9.4079382 8.8671576 7.5851679 7.2449244 8.119644
50pmol mean 11.0652499 7.812534 9.955303 7.9185075 8.6105032 8.451282 8.3913880 16.75777 12.6233713 13.9262822 9.6052951 9.6068208 10.3568778 8.5314736 6.9189678 9.8860972 7.1716461 8.6447744 10.4687736 14.615228 13.4461368 14.2433512 10.278260 10.2093348 8.6777501 7.2864883 11.7603897 15.1244369 8.393428 7.6429483 8.674664 9.8868633 6.7178161 9.3454998 8.2512331 7.9571364 6.6190600 6.899741
A 10.8570720 8.906018 10.601906 8.1845351 8.4275034 7.598933 8.3502803 16.75777 12.9352480 13.8344719 9.4311758 9.8741055 10.1773834 8.3097660 7.2410414 9.9638424 7.3463637 8.5228175 10.3550942 14.883442 13.7801206 14.4924743 9.545781 10.1912770 8.5649206 7.1918154 11.9689836 14.7721191 7.249967 7.5690298 9.295618 10.2413401 6.7461103 9.3767190 8.5591954 7.7711521 6.9319922 7.509692
M -0.4163558 2.186968 1.293206 0.5320553 -0.3659995 -1.704697 -0.0822154 0.00000 0.6237535 -0.1836205 -0.3482386 0.5345694 -0.3589887 -0.4434153 0.6441472 0.1554905 0.3494352 -0.2439139 -0.2273586 0.536428 0.6679676 0.4982461 -1.464959 -0.0361156 -0.2256591 -0.1893458 0.4171878 -0.7046356 -2.286924 -0.1478371 1.241908 0.7089535 0.0565885 0.0624383 0.6159244 -0.3719685 0.6258644 1.219903

Details

The result of method = "MA" is to generate the data for an MA plot, which plots the average fold change between two conditions (y-axis) against the average abundance of that protein (x-axis). This is helpful for evaluating whether a fold-change difference is being enhanced by low overall intensities (e.g. a change from 200 to 400 is the same fold-change as from 20,000 to 40,000, but the latter is a more robust measurement and less susceptible to noise interference).

PCA

Example

In the case of dataImput, one protein, namely “ALBU_BOVIN”, has constant values, leading to the error message. We choose to remove this protein in the principal component analysis (PCA).

names(dataImput)[sapply(dataImput, function(col) length(unique(col)) == 1)]
#> [1] "ALBU_BOVIN"
dataPCA <- dataImput[, colnames(dataImput) != "ALBU_BOVIN"]
anlys_pca <- analyze.pca(dataPCA, center = TRUE, scale = TRUE)
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12
NUD4B_HUMAN 0.1188769 -0.1772058 0.3013469 0.1166326 -0.1171230 -0.1140154 0.0852809 -0.0052137 0.2575418 -0.0874986 0.2653179 -0.0443054
A0A7P0T808_HUMAN -0.1923637 0.2673578 -0.0130253 0.0704243 0.1390564 -0.1433468 -0.0613349 -0.0192361 -0.0110135 -0.1744425 0.0072387 -0.1197070
A0A8I5KU53_HUMAN -0.1289231 -0.2613306 0.0670152 -0.3011696 -0.0801726 0.0031264 0.0131632 -0.0692313 -0.0814145 -0.0598182 -0.2562817 0.1123945
ZN840_HUMAN -0.0946885 0.1276385 -0.1185999 -0.0799084 -0.2528080 -0.0920193 -0.4545132 0.1824215 -0.0575034 0.2849940 0.1804638 0.1344338
CC85C_HUMAN 0.0279395 0.1453424 0.2659663 -0.0335283 -0.2857038 0.2540437 0.3190762 -0.0065456 -0.0636165 0.0124279 -0.0867440 0.1428931
C9JEV0_HUMAN 0.2195074 0.0888077 -0.0259365 0.1759781 0.0826490 0.1627861 -0.1151784 0.2401487 0.1698661 -0.0474144 0.0727410 0.1134314
C9JNU9_HUMAN 0.0506167 -0.1477131 0.0540367 -0.3813773 0.0086479 -0.0297355 0.2233856 0.1898863 -0.1766131 -0.1663948 0.3947327 -0.2030588
CYC_BOVIN -0.1529865 -0.2605458 0.1716647 0.2067954 -0.0597879 0.0664754 0.0508447 -0.0598310 -0.0361334 -0.0545473 0.1969247 0.0133054
TRFE_BOVIN 0.0166056 -0.0168069 0.2563064 0.2120388 0.3158137 0.0055484 -0.1816376 -0.3163176 -0.1781309 0.0867631 0.1887143 0.1707446
F8W0H2_HUMAN 0.2324368 -0.2037737 -0.0520720 -0.0262713 -0.0057226 0.0016452 -0.1483219 -0.0509623 -0.1060480 0.1500850 0.0055537 -0.2234328
H0Y7V7_HUMAN -0.1988603 0.0693049 -0.1140835 0.1625452 -0.2104495 0.2552643 -0.0405976 0.1477780 -0.0798505 -0.0086801 0.0640366 0.1886111
H0YD14_HUMAN 0.1759816 -0.1299604 0.1545766 0.1813077 -0.1487485 -0.1570639 -0.2451852 0.1617803 -0.0081617 -0.1506331 -0.0924136 -0.3311737
H3BUF6_HUMAN 0.2391701 -0.0562345 -0.0842242 -0.0083829 0.2109445 -0.0478691 -0.0051651 0.1629583 0.0829339 0.0852264 -0.2389092 -0.2428304
H7C1W4_HUMAN -0.1571658 0.1583107 -0.0869895 0.1324719 0.0195078 0.4070862 0.0582437 -0.1056046 0.2157584 -0.0357477 -0.0274390 -0.1202732
H7C3M7_HUMAN -0.0268758 -0.1273659 -0.1694855 -0.1822225 0.2810524 0.1921604 0.2311994 0.2416035 -0.0435478 0.3395591 0.3337349 0.0890861
TLR3_HUMAN -0.1357144 0.1117065 -0.2380256 0.1189789 -0.1890502 -0.1435597 0.1431369 -0.3576237 -0.0887085 0.0761335 0.1526797 -0.2647170
LRIG2_HUMAN 0.0995903 0.1751785 0.1182372 -0.1571006 -0.1962309 0.3532671 -0.2061901 0.0078915 0.2643580 0.1422136 0.0010136 -0.1246348
RAB3D_HUMAN 0.2023449 -0.1505532 0.0064984 0.0888776 -0.2543353 -0.1184410 0.1012599 0.0188862 -0.0559570 0.3159153 -0.1320459 0.1398686
ADH1_YEAST -0.2668869 0.0081800 -0.0189212 0.1051980 0.0259162 -0.0238403 0.0829892 0.0157430 -0.0507490 -0.0663319 -0.1758915 0.2232915
LYSC_CHICK -0.1849233 -0.2564786 -0.0640937 0.1743153 -0.0146319 -0.0206389 0.0603589 -0.0655267 0.2384278 -0.0317316 0.1313226 0.0503470
BGAL_ECOLI -0.1785988 -0.1870954 -0.2022390 0.1424334 -0.0116692 0.0681519 0.1151700 0.1944421 0.2250153 0.0457059 0.1041234 -0.0946921
CYTA_HUMAN 0.2559654 0.0494146 0.0060060 0.1543365 -0.0138220 -0.0088571 -0.0246560 0.1585144 -0.0276568 0.1505360 0.0409124 0.0175259
KPCB_HUMAN 0.1253089 -0.0808968 -0.1725579 0.0478656 -0.2166705 -0.3758153 0.1601399 -0.1157743 0.2303199 0.2291294 -0.0922683 0.3101692
LIPL_HUMAN 0.1525946 -0.1743527 -0.0549491 -0.0480936 0.2202094 0.1896217 -0.1268498 -0.2373904 0.4209711 0.0006371 0.0513403 0.1113975
CO6_HUMAN 0.1695935 -0.1593740 -0.2012358 -0.1658137 -0.0579254 0.0942043 0.0302497 -0.3395673 0.1237697 -0.0386718 -0.0644920 -0.0086929
BGAL_HUMAN -0.0549373 -0.3398065 0.1489976 0.1531309 -0.1653884 0.1216312 0.0174027 0.2036603 0.0402230 -0.0405328 -0.0633152 0.0726581
SYTC_HUMAN 0.2388616 0.1058242 0.1738849 0.0106153 0.0994714 0.0475180 -0.0305461 -0.0496722 -0.1463871 -0.1240736 0.0653873 0.4425293
CASPE_HUMAN 0.2394326 0.0905518 -0.0019253 0.2112729 -0.0194369 0.0094043 -0.0243169 0.0389020 -0.0373315 -0.0237263 0.2765286 0.0461540
DCAF6_HUMAN -0.0381553 0.1591755 0.2198207 -0.1190328 0.3275744 -0.2690164 0.1868800 0.0709852 0.1486062 0.1974166 0.0071470 0.0559504
DALD3_HUMAN -0.2266936 -0.0794761 0.0331701 -0.0648178 0.0502899 -0.1128253 -0.1004736 0.3813898 0.0760953 -0.0233357 -0.1372768 0.0977357
HGNAT_HUMAN -0.1494193 -0.2325232 0.1921014 -0.0404533 -0.0723579 0.0148836 -0.2920131 -0.1432496 -0.2087894 0.0202744 0.1469799 -0.0225062
RFFL_HUMAN -0.0806236 -0.1075798 0.1876089 0.2489770 0.2560138 0.1844149 0.0279090 0.0055965 -0.1577143 0.3487386 -0.3588672 -0.1740691
RN185_HUMAN -0.0969748 0.0449635 0.3444740 -0.1393251 -0.1458840 0.0858452 0.1314834 -0.1094733 0.0092404 0.4236179 0.0600972 -0.1655283
ZN462_HUMAN -0.1012649 -0.1599062 0.0717821 -0.3698936 0.0658706 0.0399805 -0.2972022 -0.0645871 0.1494307 -0.0475510 -0.0870126 0.1314355
ALKB7_HUMAN 0.1400932 -0.2796786 -0.1450878 0.1117422 0.1001606 0.1285143 0.1473570 0.0045354 -0.2482360 -0.1677388 -0.0951268 0.0521427
POLK_HUMAN -0.1657505 0.0162727 0.2882023 0.1061381 0.0512940 -0.2214101 0.0338060 0.0168708 0.3273576 -0.0500830 0.0379469 -0.0507182
ACAD8_HUMAN -0.1876209 -0.1271427 -0.2161112 0.1170599 0.1377752 -0.0681053 -0.1983427 -0.0601201 -0.0648194 0.2567213 0.1180605 0.0196439

Details

PCA is a powerful technique used in data analysis to simplify and reduce the dimensionality of large datasets. It transforms original variables into uncorrelated components that capture the maximum variance. By selecting a subset of these components, PCA projects the data points onto these key directions, enabling visualization and analysis in a lower-dimensional space. This aids in identifying patterns and relationships within complex datasets.

For PCA, the arguments center and scale are used to center the data to zero mean and scale to unit variance, with default setting at TRUE.

Note: Data scaling is done to ensure that the scale differences between different features do not affect the results of PCA. If not scaled, features with larger scales will dominate the computation of principal components (PCs).
Note: The most common error message for the PCA is “Cannot rescale a constant/zero column to unit variance.” This clearly occurs when columns representing proteins contain only zeros or have constant values. Typically, there are two ways to address this error: one is to remove these proteins, and the other is to set scale = FALSE.

PLS-DA

Example 

anlys_plsda <- analyze.plsda(dataImput, method = "kernelpls",
                             center = TRUE, scale = FALSE)
Comp 1 Comp 2 Comp 3 Comp 4 Comp 5 Comp 6 Comp 7 Comp 8 Comp 9 Comp 10 Comp 11
NUD4B_HUMAN -0.0721647 -0.0120098 0.1953417 -0.1265834 -0.2770654 0.2088323 -0.2009317 -0.0648547 0.0819363 -0.0454658 0.0636382
A0A7P0T808_HUMAN 0.7186598 0.6655256 -0.3729955 -0.0614608 0.2153595 0.0844135 -0.1686950 -0.0100014 -0.1347304 0.0754667 0.0196661
A0A8I5KU53_HUMAN 0.1319886 -0.5206130 0.3662075 -0.1771716 0.1953444 -0.1257639 -0.0554701 0.0647629 -0.2340413 0.1951236 -0.0178531
ZN840_HUMAN 0.0759383 0.0051305 -0.1438278 -0.1839000 -0.0285629 -0.1132148 0.7376771 -0.5636673 -0.0141927 0.0786254 0.0553176
CC85C_HUMAN -0.0193422 0.0726843 0.2930632 0.2616026 -0.2469929 -0.1845150 0.0807948 0.0392711 -0.2827831 0.0400909 -0.0442734
C9JEV0_HUMAN -0.2703730 0.2837151 -0.1926834 0.0625934 -0.0185917 0.1284127 -0.0099575 -0.2120263 0.2746758 0.3386334 -0.5534373
C9JNU9_HUMAN -0.0210992 -0.0655873 0.0920396 -0.1411416 0.0807395 -0.0782736 -0.0859163 -0.0237348 -0.1772656 -0.3822360 -0.1003000
ALBU_BOVIN 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
CYC_BOVIN 0.0630095 -0.1487569 0.0827505 0.2645965 -0.3231046 0.3509137 -0.2245885 -0.0907853 0.0138095 -0.1201481 0.0574934
TRFE_BOVIN 0.0003603 0.0327004 0.0563023 0.0921118 0.0790271 0.1782056 -0.0577101 -0.0134763 0.1384664 -0.0601031 0.0685727
F8W0H2_HUMAN -0.0836310 -0.0409287 -0.0097821 -0.0467141 0.0900126 0.0470618 0.0413209 -0.0304372 0.0481555 0.0222324 0.0601126
H0Y7V7_HUMAN 0.0661155 -0.0130440 -0.1035646 0.2440707 -0.1735985 -0.0442730 0.1092398 -0.1508836 -0.1183611 0.0666902 -0.0792505
H0YD14_HUMAN -0.0643955 0.0066205 0.0315022 -0.1133884 -0.0765332 0.1873019 0.0368123 -0.1086267 -0.0615209 0.2608078 0.0311635
H3BUF6_HUMAN -0.0817169 0.0172734 -0.0456413 -0.1147691 0.1181330 0.0299963 0.0002947 0.1473198 0.0783225 0.0595555 -0.0939223
H7C1W4_HUMAN 0.1101453 0.0652563 -0.1173529 0.5945244 -0.1593862 -0.2350004 -0.1490996 -0.0677561 0.2601399 0.2009991 -0.2223581
H7C3M7_HUMAN -0.0034187 -0.0629444 -0.0597674 0.0426410 0.0879519 -0.0625672 0.0024389 0.1157673 0.0775421 -0.4824154 -0.2049414
TLR3_HUMAN 0.0441759 0.0098471 -0.1081594 0.0667498 -0.0814554 -0.0948429 -0.0060339 0.0128601 -0.0473093 -0.0720274 0.2667861
LRIG2_HUMAN -0.0260332 0.0370571 0.0887307 0.0548165 -0.0055628 -0.1406393 0.1311827 -0.1666427 0.1044586 0.1446907 -0.0941133
RAB3D_HUMAN -0.0636594 -0.0205192 0.0081626 -0.0408032 -0.0886833 0.0241637 0.1057128 0.0619038 -0.0443293 0.0400687 0.1096532
ADH1_YEAST 0.0886732 -0.0249521 -0.0403430 0.0903633 -0.0779594 0.0441123 -0.0239604 0.0747446 -0.0543856 0.0371257 -0.0134305
LYSC_CHICK 0.0559316 -0.1146442 -0.0857625 0.0670823 -0.2123522 0.1175924 -0.1913011 0.0088550 0.1390133 -0.0373356 0.0392854
BGAL_ECOLI 0.0354376 -0.0711086 -0.1189625 0.0522730 -0.1471152 0.0179722 -0.0593368 0.0304717 0.0460744 -0.0709726 -0.0830799
CYTA_HUMAN -0.2408724 0.1808954 -0.0650776 -0.0752154 -0.0405000 0.1463552 0.2409196 0.0368326 -0.0089404 0.0389971 -0.0296202
KPCB_HUMAN -0.0378512 -0.0135789 -0.0613165 -0.1643575 -0.1085606 -0.0451864 0.0301777 0.1125958 0.0218511 0.0296297 0.1979495
LIPL_HUMAN -0.0574665 -0.0353568 -0.0050660 0.0030499 0.1003712 -0.0147210 -0.1869732 -0.0219275 0.3410786 0.0676615 -0.0420835
CO6_HUMAN -0.0732950 -0.0688572 -0.0320353 -0.0213450 0.1139149 -0.1666744 -0.1351959 0.0021596 0.1066948 0.0883733 0.1395078
BGAL_HUMAN -0.0143422 -0.2101695 0.0838454 0.1545052 -0.3928935 0.3207376 -0.0666900 -0.0715498 -0.0912390 0.1973253 -0.1710358
SYTC_HUMAN -0.0756364 0.0885254 0.0966614 -0.0037612 0.1212615 0.0467116 -0.0428996 -0.0263406 -0.0298009 0.0069434 -0.0031882
CASPE_HUMAN -0.3483297 0.3750765 -0.1535783 0.0477597 -0.1098289 0.2295415 -0.0611326 -0.2841158 -0.0494692 -0.1608693 0.1757337
DCAF6_HUMAN 0.0418068 0.0849396 0.1310571 -0.1964884 0.0685438 0.0520614 -0.0021802 0.2694899 0.1406201 -0.3056558 -0.0866475
DALD3_HUMAN 0.1896420 -0.1432009 -0.0340628 -0.2470246 -0.1349039 0.1876184 0.1936865 0.0137017 -0.0889047 0.0389107 -0.4605911
HGNAT_HUMAN 0.1046402 -0.2416903 0.2325612 0.1301691 0.0327822 0.3781608 0.1030650 -0.4802290 0.0558312 -0.0006821 0.1799745
RFFL_HUMAN 0.0181685 -0.0152259 0.0389704 0.2295137 -0.0023865 0.2135595 0.0617714 0.1891520 0.1219770 0.0433609 -0.0823849
RN185_HUMAN 0.0585569 -0.0112455 0.3784554 0.1343171 -0.1741584 -0.0543047 0.2908758 0.0307732 0.0956419 -0.2721382 0.0447837
ZN462_HUMAN 0.0964568 -0.2647605 0.2505166 -0.3012533 0.4054542 -0.1195386 0.0206231 -0.3445674 0.3837608 0.2115073 -0.2285650
ALKB7_HUMAN -0.1715043 -0.1780380 -0.2018088 0.2414476 0.1568775 0.2092464 -0.4265611 0.2582000 -0.2462521 -0.0188007 -0.0232842
POLK_HUMAN 0.1889136 0.0622625 0.2948281 -0.2461720 -0.4850179 0.3996914 -0.2316628 0.1115249 0.3279035 0.0315702 -0.0332998
ACAD8_HUMAN 0.1306602 -0.1492646 -0.3520092 0.1200877 0.0612338 0.2134321 0.1391003 -0.0406605 0.3262590 -0.2431010 0.1337232

Details

Partial least squares-discriminant analysis (PLS-DA) adapts PLS regression for supervised classification. Rather than simply finding directions of maximal variances in the predictors as PCA does, PLS-DA extracts latent components that maximize the covariance between predictors and dummy-coded group labels. This ensures that the resulting components optimally separate predefined groups and yields variable-importance scores directly tied to classification.

For PLS-DA, the argument method specifies which multivariate regression algorithm to use:

  1. “kernelpls”: Kernel algorithm (Dayal and MacGregor 1997).

  2. “widekernelpls”: Wide kernel algorithm (Rännar et al. 1994).

  3. “simpls”: SIMPLS algorithm (Jong 1993).

  4. “oscorespls”: NIPALS algorithm (classical orthogonal scores algorithm) (Martens and Næs 1989).

The argument ncomp sets the number of components to include in the model. It defaults to min(n-1, p). The arguments center and scale control whether the data are centered to zero mean and scaled to unit variance, respectively.

Reference

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Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society: Series B (Methodological) 57 (1): 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.
Benjamini, Yoav, and Daniel Yekutieli. 2001. “The Control of the False Discovery Rate in Multiple Testing Under Dependency.” The Annals of Statistics 29 (4): 1165–88. https://doi.org/10.1214/aos/1013699998.
Bonferroni, C. E. 1936. “Teoria Statistica Delle Classi e Calcolo Delle Probabilità.” Pubblicazioni Del R Istituto Superiore Di Scienze Economiche e Commerciali Di Firenze 8: 3–62.
Dayal, Bhupinder S., and John F. MacGregor. 1997. “Improved PLS Algorithms.” Journal of Chemometrics 11 (1): 73–85. https://doi.org/10.1002/(SICI)1099-128X(199701)11:1<73::AID-CEM435>3.0.CO;2-\%23.
Hochberg, Yosef. 1988. “A Sharper Bonferroni Procedure for Multiple Tests of Significance.” Biometrika 75 (4): 800–802. https://doi.org/10.1093/biomet/75.4.800.
Holm, Sture. 1979. “A Simple Sequentially Rejective Multiple Test Procedure.” Scandinavian Journal of Statistics 6 (2): 65–70. https://www.jstor.org/stable/4615733.
Hommel, Gerhard. 1988. “A Stagewise Rejective Multiple Test Procedure Based on a Modified Bonferroni Test.” Biometrika 75 (2): 383–86. https://doi.org/10.1093/biomet/75.2.383.
Jong, Sijmen de. 1993. “SIMPLS: An Alternative Approach to Partial Least Squares Regression.” Chemometrics and Intelligent Laboratory Systems 18 (3): 251–63. https://doi.org/10.1016/0169-7439(93)85002-X.
Martens, Harald, and Tormod Næs. 1989. Multivariate Calibration. Wiley, New York, USA: Chichester.
Rännar, Stefan, Fredrik Lindgren, Paul Geladi, and Svante Wold. 1994. “A PLS Kernel Algorithm for Data Sets with Many Variables and Fewer Objects. Part 1: Theory and Algorithm.” Journal of Chemometrics 8 (2): 111–25. https://doi.org/10.1002/cem.1180080204.
Smyth, Gordon K. 2004. “Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments.” Statistical Applications in Genetics and Molecular Biology 3 (1). https://doi.org/10.2202/1544-6115.1027.