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Imputation

Source: vignettes/imputation.qmd

Preliminary 

## load R package
library(msDiaLogue)
## preprocessing
fileName <- "../inst/extdata/Toy_Spectronaut_Data.csv"
dataSet <- preprocessing(fileName,
                         filterNaN = TRUE, filterUnique = 2,
                         replaceBlank = TRUE, saveRm = TRUE)
## transformation
dataTran <- transform(dataSet, logFold = 2)
## annotation-based filtering
dataFiltAnno <- filterOutIn(dataTran, listName = "ALBU_BOVIN",
                            removeList = TRUE, saveRm = TRUE)
## normalization
dataNorm <- normalize(dataFiltAnno, normalizeType = "median")
## data-driven filtering
dataFilt <- filterNA(dataNorm, minProp = 0.51, by = "cond", saveRm = TRUE)

Examples 

dataImput <- impute.min_local(dataFilt)
R.Condition R.Replicate NUD4B_HUMAN (+1) A0A7P0T808_HUMAN (+1) A0A8I5KU53_HUMAN (+1) ZN840_HUMAN CC85C_HUMAN C9JEV0_HUMAN (+1) C9JNU9_HUMAN CYC_BOVIN TRFE_BOVIN F8W0H2_HUMAN H0Y7V7_HUMAN (+1) H0YD14_HUMAN H3BUF6_HUMAN H7C1W4_HUMAN (+1) 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 1 0.6578963 1.6912275 1.523093 -1.6229297 -0.9854966 -2.2020970 -2.0487396 3.452154 3.943761 -0.6386260 0.4190686 0.3832438 -1.8057427 -1.711792 0.2348458 -2.451893 -0.9267417 0.3315355 4.814073 3.813956 4.602821 -0.6759724 0.0138251 -1.1567811 -2.703667 1.866666 4.775565 -3.1627881 -2.212775 0.3976973 1.0009580 -2.370005 0.1730515 0.0000000 -2.4413680 -2.300598 -2.2255399
100pmol 2 1.1618220 2.6069381 0.499450 -0.8939165 -0.4318767 -2.0224822 -1.2582828 3.826245 4.398260 -0.3855094 0.5688423 0.7856583 -1.4665636 -1.598602 0.4582302 -1.817571 -0.6094305 0.6593645 5.179413 4.207712 4.995489 -0.2306371 0.5203989 -1.0431798 -2.550131 2.183688 5.153782 -2.2734413 -1.107578 0.5054164 1.0064713 -2.287126 0.7952927 -0.9506921 -2.6135205 0.000000 -1.6024466
100pmol 3 0.6700794 2.3944773 1.292374 -1.6925457 -0.6687828 -2.9987418 -0.8270544 3.584108 4.078495 -0.5356376 0.4615256 0.0895033 -1.5213077 -1.715882 1.0240181 -2.147069 -1.3776219 0.4651160 4.902153 3.948069 4.690354 -0.6886192 0.4043134 -1.4787373 -2.907444 1.976459 4.876785 -3.2755551 -1.478305 0.1742765 0.5943610 -2.635191 0.6053144 -1.0476748 -2.6195940 -2.377350 -2.0024438
100pmol 4 0.8459180 2.0861542 1.194119 -1.2273728 -0.6919248 -3.4192914 -1.4298346 3.588195 4.071733 -0.0242533 0.3495332 0.2761703 -1.8057427 -1.974565 0.0000000 -1.904616 -1.1083001 0.4016035 4.859260 3.951123 4.629191 -1.0397232 0.3669244 -1.3587551 -2.708964 1.949330 4.844947 -3.3525778 -2.049782 -0.8802224 0.9334560 -3.173078 0.1359572 -1.1958590 -2.7445785 -2.634663 -2.0589152
200pmol 1 1.2367495 2.7209140 1.643059 -0.9241627 -2.0376118 -2.1284865 -1.4310495 4.926798 5.071045 -0.3843617 0.9573775 0.7511402 -1.2486171 -1.348386 1.1799089 -1.519906 -1.0325902 0.8746494 6.245210 5.245063 6.005343 -0.8155394 0.9062960 -0.7355826 -2.086968 2.468791 5.354433 -2.9824358 -1.791057 0.5501207 1.7437348 -2.291354 -0.7523574 -0.5793031 -1.4223468 -2.145883 -0.5956163
200pmol 2 0.8920522 2.1320303 1.448951 -0.9241627 -0.5354481 -2.5546421 -1.4310495 4.653112 4.847591 -0.2522428 0.4211597 0.7511402 -1.4825147 -1.620216 0.3960272 -2.067634 -0.8169825 0.6131741 5.965971 4.954881 5.748865 -0.3324563 0.6299749 -0.9458767 -2.553825 2.208374 5.114606 -3.5195148 -1.671696 0.3551539 -0.1366535 -2.779264 0.1366535 -0.8642755 -2.5798709 -2.309546 -2.3627650
200pmol 3 1.2852038 -0.8407850 1.836982 -0.7498530 -0.1779857 -2.6275134 -0.4297874 5.025134 5.020476 -0.1217261 1.1101593 0.9043385 -1.1300491 -1.793222 0.8158324 -1.870606 -0.5628205 0.9300210 6.311564 5.279995 6.070587 -0.5160321 0.7621903 -0.8359278 -2.306285 2.467474 5.407801 -3.3621142 -2.223375 0.8759602 1.3922679 -2.542126 0.5557834 -0.1662571 -1.0158409 -2.121038 -1.6854347
200pmol 4 1.5207481 -0.6040897 2.060276 -0.3758423 -0.8639327 -2.4855064 -0.7430913 5.224020 5.377023 0.0161123 0.7924755 1.0651086 -0.7232350 -2.448600 0.8442237 -1.693849 -1.0325902 1.1502570 6.655618 5.628997 6.343191 -0.0161123 1.0727957 -0.6382337 -2.135701 2.815138 5.691298 -2.9208223 -1.419815 0.9018856 1.6702296 -2.088800 0.1983234 -0.0738239 -1.3827316 -1.718714 -0.5015723
50pmol 1 1.3569066 -0.0418239 -1.605374 -1.1289594 -0.9034872 0.0539109 -1.5868197 3.547765 4.397999 0.0418239 0.6383018 1.0090953 -0.9876086 -1.713483 0.1019217 -1.921549 -0.9836494 1.0378691 4.827457 3.633069 4.671661 2.3687002 0.8333762 -1.0023667 -2.155108 2.618091 5.473048 0.5514687 -1.981854 -0.8838660 -0.3248609 -2.734260 -0.6292096 -3.5325733 -1.2902634 -4.787183 -2.0214147
50pmol 2 1.6024612 -3.4526216 1.579083 -0.5872764 -0.4786125 -0.9419543 -0.6649410 3.779592 4.677255 0.2521855 0.1196002 1.1937745 -0.7693976 -2.240260 0.7951258 -1.798939 -0.4338096 1.3649676 5.070432 3.993905 4.912229 0.8090999 0.8333762 -0.4372525 -1.552151 2.850557 5.750686 -1.2356313 -1.562935 -0.8838660 1.2757206 -2.381622 0.6504456 0.0000000 -1.7357928 -2.468666 -2.2680179
50pmol 3 1.6576732 -3.4526216 1.552080 -2.4702967 -0.6505471 -0.8524562 -0.7371056 3.729220 4.679342 0.2920380 0.0000000 1.1969443 -0.6623836 -2.395087 0.6664780 -2.058139 -1.0248051 1.3371078 5.157961 3.997182 4.927047 0.9774672 1.0924818 -0.5422253 -1.947256 2.893225 5.804690 -1.1925628 -1.811837 -0.6752742 1.0031749 -2.315473 0.2331366 -0.8188381 -0.5723308 -2.056650 -2.4453921
50pmol 4 1.3361532 0.6443309 1.275179 -1.1057418 -1.2905885 -1.5849506 -0.6332468 3.345342 4.341111 0.0418239 0.0000000 1.1344349 -0.8056352 -4.298759 0.5579245 -2.058139 -1.0248051 0.9560929 4.778818 3.668827 4.637064 0.5802164 0.9408524 -1.0023667 -2.155108 2.591638 5.452341 -1.6387928 -1.429612 0.1904210 0.7490866 -4.231605 -0.1802510 -0.3981666 -1.8530176 -2.042691 -2.1323101

Details

The two primary MS/MS acquisition types implemented in large scale MS-based proteomics have unique advantages and disadvantages. Traditional data-dependent acquisition (DDA) methods favor specificity in MS/MS sampling over comprehensive proteome coverage. Small peptide isolation windows (< 3 m/z) result in MS/MS spectra that contain fragmentation data from ideally only one peptide. This specificity promotes clear peptide identifications but comes at the expense of added scan time. In DDA experiments, the number of peptides that can be selected for MS/MS is limited by instrument scan speeds and is therefore prioritized by highest peptide abundance. Low abundance peptides are sampled less frequently for MS/MS and this can result in variable peptide coverage and many missing protein data across large sample datasets.

Data-independent acquisition (DIA) methods promote comprehensive peptide coverage over specificity by sampling many peptides for MS/MS simultaneously. Sequential and large mass isolation windows (4-50 m/z) are used to isolate large numbers of peptides at once for concurrent MS/MS. This produces complicated fragmentation spectra, but these spectra contain data on every observable peptide. A major disadvantage with this type of acquisition is that DIA MS/MS spectra are incredibly complex and difficult to deconvolve. Powerful and relatively new software programs like Spectronaut are capable of successfully parsing out which fragment ions came from each co-fragmented peptide using custom libraries, machine learning algorithms, and precisely determined retention times or measured ion mobility data. Because all observable ions are sampled for MS/MS, DIA reduces missingness substantially compared to DDA, though not entirely.

Various imputation methods have been developed to address the missing-value issue and assign a reasonable guess of quantitative value to proteins with missing values. So far, this package provides the following imputation methods:

  1. impute.min_local(): Replaces missing values with the lowest measured value for that protein in that condition.

  2. impute.min_global(): Replaces missing values with the lowest measured value from any protein found within the entire dataset.

  3. impute.knn(): Replaces missing values using the k-nearest neighbors algorithm (Troyanskaya et al. 2001).

  4. impute.knn_seq(): Replaces missing values using the sequential k-nearest neighbors algorithm (Kim, Kim, and Yi 2004).

  5. impute.knn_trunc(): Replaces missing values using the truncated k-nearest neighbors algorithm (Shah et al. 2017).

  6. impute.nuc_norm(): Replaces missing values using the nuclear-norm regularization (Hastie et al. 2015).

  7. impute.mice_cart(): Replaces missing values using the classification and regression trees (Breiman et al. 1984; Doove, van Buuren, and Dusseldorp 2014; van Buuren 2018).

  8. impute.mice_norm(): Replaces missing values using the Bayesian linear regression (Rubin 1987; Schafer 1997; van Buuren and Groothuis-Oudshoorn 2011).

  9. impute.pca_bayes(): Replaces missing values using the Bayesian principal components analysis (Oba et al. 2003).

  10. impute.pca_prob(): Replaces missing values using the probabilistic principal components analysis (Stacklies et al. 2007).

Additional methods will be added later.

Reference

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Breiman, L., J. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. New York, NY, USA: Routledge.
Doove, Lisa L., Stef van Buuren, and Elise Dusseldorp. 2014. “Recursive Partitioning for Missing Data Imputation in the Presence of Interaction Effects.” Computational Statistics & Data Analysis 72: 92–104. https://doi.org/10.1016/j.csda.2013.10.025.
Hastie, Trevor, Rahul Mazumder, Jason D. Lee, and Reza Zadeh. 2015. “Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares.” Journal of Machine Learning Research 16 (104): 3367—3402. http://jmlr.org/papers/v16/hastie15a.html.
Kim, Ki-Yeol, Byoung-Jin Kim, and Gwan-Su Yi. 2004. “Reuse of Imputed Data in Microarray Analysis Increases Imputation Efficiency.” BMC Bioinformatics 5: 160. https://doi.org/10.1186/1471-2105-5-160.
Oba, Shigeyuki, Masa-aki Sato, Ichiro Takemasa, Morito Monden, Ken-ichi Matsubara, and Shin Ishii. 2003. “A Bayesian Missing Value Estimation Method for Gene Expression Profile Data.” Bioinformatics 19 (16): 2088–96. https://doi.org/10.1093/bioinformatics/btg287.
Rubin, Donald B. 1987. Multiple Imputation for Nonresponse in Surveys. New York, NY, USA: John Wiley & Sons.
Schafer, Joseph L. 1997. Analysis of Incomplete Multivariate Data. New York, NY, USA: Chapman & Hall/CRC.
Shah, Jasmit S., Shesh N. Rai, Andrew P. DeFilippis, Bradford G. Hill, Aruni Bhatnagar, and Guy N. Brock. 2017. “Distribution Based Nearest Neighbor Imputation for Truncated High Dimensional Data with Applications to Pre-Clinical and Clinical Metabolomics Studies.” BMC Bioinformatics 18: 114. https://doi.org/10.1186/s12859-017-1547-6.
Stacklies, Wolfram, Henning Redestig, Matthias Scholz, Dirk Walther, and Joachim Selbig. 2007. “pcaMethods–a Bioconductor Package Providing PCA Methods for Incomplete Data.” Bioinformatics 23 (9): 1164–67. https://doi.org/10.1093/bioinformatics/btm069.
Troyanskaya, Olga, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein, and Russ B. Altman. 2001. “Missing Value Estimation Methods for DNA Microarrays.” Bioinformatics 17 (6): 520–25. https://doi.org/10.1093/bioinformatics/17.6.520.
van Buuren, Stef. 2018. Flexible Imputation of Missing Data. New York, NY, USA: Chapman & Hall/CRC.
van Buuren, Stef, and Karin Groothuis-Oudshoorn. 2011. “Mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software 45 (3): 1–67. https://doi.org/10.18637/jss.v045.i03.