NAGuide R methods

NAGuide R methods#

Setup basic methods and packages used for all methods

  • BiocManager could be moved to methods who are installed from BioConductor

Hide code cell source

 
# options("install.lock"=FALSE)

packages_base_R <-
  c("BiocManager", "reshape2", "data.table", "readr", "tibble")

install_rpackage  <- function(pkg) {
  # If not installed, install the package
  if (!require(pkg, character.only = TRUE)) {
    install.packages(pkg)
    library(pkg, character.only = TRUE)
  }
  
}

# used in the large imputation function for two packages
install_bioconductor  <- function(pkg) {
  # If not installed, install the package
  if (!require(pkg, character.only = TRUE)) {
    BiocManager::install(pkg)
    library(pkg, character.only = TRUE)
  }
  
}


for (package in packages_base_R) {
  # Check if the package is already installed
  install_rpackage(pkg = package)
}

Loading required package: BiocManager
Loading required package: reshape2
Loading required package: data.table

Attaching package: ‘data.table’
The following objects are masked from ‘package:reshape2’:

    dcast, melt
Loading required package: readr
Loading required package: tibble

setup can be tricky… trying to integrate as much as possible into conda environment

Copied from NAGuideR’s github RShiny application. Adapted to run as standalone function in context of the Snakemake workflow.

  • df and df1 ?

  • seems quite hacky

  • code is only slightly adapted from repo to run here, mainly to install packages on the fly

Hide code cell source

 
nafunctions <- function(x, method = "zero") {
  df <- df1 <- as.data.frame(x)
  method <- tolower(method)
  if (method == "zero") {
    df[is.na(df)] <- 0
  }
  else if (method == "minimum") {
    df[is.na(df)] <- min(df1, na.rm = TRUE)
  }
  else if (method == "colmedian") {
    install_rpackage('e1071')
    df <- impute(df1, what = "median")
  }
  else if (method == "rowmedian") {
    install_rpackage('e1071')
    dfx <- impute(t(df1), what = "median")
    df <- t(dfx)
  }
  else if (method == "knn_impute") {
    install_bioconductor('impute')
    data_zero1 <-
      impute.knn(as.matrix(df1),
                 k = 10,
                 rowmax = 1,
                 colmax = 1)#rowmax = 0.9, colmax = 0.9
    df <- data_zero1$data
  }
  else if (method == "seqknn") {
    if (!require(SeqKnn)) {
      install.packages("src/R_NAGuideR/SeqKnn_1.0.1.tar.gz",
                       repos = NULL,
                       type = "source")
      library(SeqKnn)
    }
    df <- SeqKNN(df1, k = 10)
  }
  else if (method == "bpca") {
    install_bioconductor('pcaMethods')
    data_zero1 <-
      pcaMethods::pca(
        as.matrix(df1),
        nPcs = ncol(df1) - 1,
        method = "bpca",
        maxSteps = 100
      )
    df <- completeObs(data_zero1)
  }
  else if (method == "svdmethod") {
    install_bioconductor('pcaMethods')
    data_zero1 <-
      pcaMethods::pca(as.matrix(df1),
                      nPcs = ncol(df1) - 1,
                      method = "svdImpute")
    df <- completeObs(data_zero1)
  }
  else if (method == "lls") {
    install_bioconductor('pcaMethods')
    data_zero1 <- llsImpute(t(df1), k = 10)
    df <- t(completeObs(data_zero1))
  }
  else if (method == "mle") {
    install_rpackage('norm')
    xxm <- as.matrix(df1)
    ss <- norm::prelim.norm(xxm)
    thx <- norm::em.norm(ss)
    norm::rngseed(123)
    df <- norm::imp.norm(ss, thx, xxm)
  }
  else if (method == "qrilc") {
    install_bioconductor("impute")
    install_bioconductor("pcaMethods")
    install_rpackage('gmm')
    install_rpackage('imputeLCMD')
    xxm <- t(df1)
    data_zero1 <-
      imputeLCMD::impute.QRILC(xxm, tune.sigma = 1)[[1]]
    df <- t(data_zero1)
  }
  else if (method == "mindet") {
    install_bioconductor("impute")
    install_bioconductor("pcaMethods")
    install_rpackage('gmm')
    install_rpackage('imputeLCMD')
    xxm <- as.matrix(df1)
    df <- imputeLCMD::impute.MinDet(xxm, q = 0.01)
  }
  else if (method == "minprob") {
    install_bioconductor("impute")
    install_bioconductor("pcaMethods")
    install_rpackage('gmm')
    install_rpackage('imputeLCMD')
    xxm <- as.matrix(df1)
    df <-
      imputeLCMD::impute.MinProb(xxm, q = 0.01, tune.sigma = 1)
  }
  else if (method == "irm") {
    install_rpackage('VIM')
    df <- irmi(df1, trace = TRUE, imp_var = FALSE)
    rownames(df) <- rownames(df1)
  }
  else if (method == "impseq") {
    install_rpackage('rrcovNA')
    df <- impSeq(df1)
  }
  else if (method == "impseqrob") {
    install_rpackage('rrcovNA')
    data_zero1 <- impSeqRob(df1, alpha = 0.9)
    df <- data_zero1$x
  }
  else if (method == "mice-norm") {
    install_rpackage('mice')
    minum <- 5
    datareadmi <- mice(df1,
                       m = minum,
                       seed = 1234,
                       method = "norm")
    newdatareadmi <- 0
    for (i in 1:minum) {
      newdatareadmi <- complete(datareadmi, action = i) + newdatareadmi
    }
    df <- newdatareadmi / minum
    rownames(df) <- rownames(df1)
  }
  else if (method == "mice-cart") {
    install_rpackage('mice')
    minum <- 5
    datareadmi <- mice(df1,
                       m = minum,
                       seed = 1234,
                       method = "cart")
    newdatareadmi <- 0
    for (i in 1:minum) {
      newdatareadmi <- complete(datareadmi, action = i) + newdatareadmi
    }
    df <- newdatareadmi / minum
    rownames(df) <- rownames(df1)
  }
  else if (method == "trknn") {
    source('src/R_NAGuideR/Imput_funcs.r')
    # sim_trKNN_wrapper <- function(data) {
    #   result <- data %>% as.matrix %>% t %>% imputeKNN(., k=10, distance='truncation', perc=0) %>% t
    #   return(result)
    # }
    # df1x <- sim_trKNN_wrapper(t(df1))
    # df<-as.data.frame(t(df1x))
    df <-
      imputeKNN(as.matrix(df),
                k = 10,
                distance = 'truncation',
                perc = 0)
    df <- as.data.frame(df)
  }
  else if (method == "rf") {
    install_rpackage("missForest")
    data_zero1 <- missForest(
      t(df1),
      maxiter = 10,
      ntree = 20 # input$rfntrees
      ,
      mtry = floor(nrow(df1) ^ (1 / 3)),
      verbose = TRUE
    )
    df <- t(data_zero1$ximp)
  }
  else if (method == "pi") {
    width <- 0.3 # input$piwidth
    downshift <- 1.8 # input$pidownshift
    for (i in 1:ncol(df1)) {
      temp <- df1[[i]]
      if (sum(is.na(temp)) > 0) {
        temp.sd <- width * sd(temp[!is.na(temp)], na.rm = TRUE)
        temp.mean <-
          mean(temp[!is.na(temp)], na.rm = TRUE) - downshift * sd(temp[!is.na(temp)], na.rm = TRUE)
        n.missing <- sum(is.na(temp))
        temp[is.na(temp)] <-
          rnorm(n.missing, mean = temp.mean, sd = temp.sd)
        df[[i]] <- temp
      }
    }
    df
  }
  # else if(method=="grr"){
  #   library(DreamAI)
  #   df<-impute.RegImpute(data=as.matrix(df1), fillmethod = "row_mean", maxiter_RegImpute = 10,conv_nrmse = 1e-03)
  # }
  else if (method == "gms") {
    # install.packages('GMSimpute')
    if (!require(GMSimpute)) {
      install.packages(
        "src/R_NAGuideR/GMSimpute_0.0.1.1.tar.gz",
        repos = NULL,
        type = "source"
      )
      
      library(GMSimpute)
    }
    
    df <- GMS.Lasso(df1,
                    nfolds = 3,
                    log.scale = FALSE,
                    TS.Lasso = TRUE)
  }
  else if (method == "msimpute") {
    install_bioconductor("msImpute")
    df <- msImpute(as.matrix(df),
                   method = 'v2')
    df <- as.data.frame(df)
  }
  else if (method == "msimpute_mnar") {
    install_bioconductor("msImpute")
    df <-
      msImpute(as.matrix(df),
               method = 'v2-mnar',
               group = rep(1, dim(df)[2]))
    df <- as.data.frame(df)
  }
  else if (method == "gsimp") {
    options(stringsAsFactors = F)
    # dependencies parly for sourced file
    
    install_bioconductor("impute")
    install_bioconductor("pcaMethods")
    install_rpackage('gmm')
    install_rpackage('imputeLCMD')
    install_rpackage("magrittr")
    install_rpackage("glmnet")
    install_rpackage("abind")
    install_rpackage("foreach")
    install_rpackage("doParallel")
    source('src/R_NAGuideR/GSimp.R')
    
    # wrapper function with data pre-processing
    pre_processing_GS_wrapper <- function(data_raw_log) {
      # samples in rows, features in columns #
      # Initialization #
      data_raw_log_qrilc <- as.data.frame(data_raw_log) %>%
        impute.QRILC() %>% extract2(1)
      # Centralization and scaling #
      data_raw_log_qrilc_sc <-
        scale_recover(data_raw_log_qrilc, method = 'scale')
      # Data after centralization and scaling #
      data_raw_log_qrilc_sc_df <- data_raw_log_qrilc_sc[[1]]
      # Parameters for centralization and scaling (for scaling recovery) #
      data_raw_log_qrilc_sc_df_param <- data_raw_log_qrilc_sc[[2]]
      # NA position #
      NA_pos <- which(is.na(data_raw_log), arr.ind = T)
      # NA introduced to log-scaled-initialized data #
      data_raw_log_sc <- data_raw_log_qrilc_sc_df
      data_raw_log_sc[NA_pos] <- NA
      # Feed initialized and missing data into GSimp imputation #
      result <-
        data_raw_log_sc %>% GS_impute(
          .,
          iters_each = 50,
          iters_all = 10,
          initial = data_raw_log_qrilc_sc_df,
          lo = -Inf,
          hi = 'min',
          n_cores = 1,
          imp_model = 'glmnet_pred'
        )
      data_imp_log_sc <- result$data_imp
      # Data recovery #
      data_imp <- data_imp_log_sc %>%
        scale_recover(., method = 'recover',
                      param_df = data_raw_log_qrilc_sc_df_param) %>%
        extract2(1)
      return(data_imp)
    }
    df <- t(df) # samples in rows, feature in columns
    df <- pre_processing_GS_wrapper(df)
    df <- t(df) # features in rows, samples in columns
    
  }
  else{
    stop(paste("Unspported methods so far: ", method))
  }
  df <- as.data.frame(df)
  df
}

Parameters#

Choose one of the available methods. Some methods might fail for your dataset for unknown reasons (and the error won’t always be easy to understand)

method = 'ZERO'
method = 'MINIMUM'
method = 'COLMEDIAN'
method = 'ROWMEDIAN'
method = 'KNN_IMPUTE'
method = 'SEQKNN'
method = 'BPCA'
method = 'SVDMETHOD'
method = 'LLS'
method = 'MLE'
mehtod = 'LLS'
method = 'QRILC'
method = 'MINDET'
method = 'MINPROB'
method = 'IRM'
method = 'IMPSEQ'
method = 'IMPSEQROB'
method = 'MICE-NORM'
method = 'MICE-CART'
method = 'RF'
method = 'PI'
method = 'GMS'
method = 'TRKNN',
method = 'MSIMPUTE'
method = 'MSIMPUTE_MNAR'
method = 'GSIMP'

train_split = 'runs/example/data/data_wide_sample_cols.csv' # test
folder_experiment = 'runs/example/'
method = 'KNN_IMPUTE'

# Parameters
train_split = "runs/alzheimer_study/data/data_wide_sample_cols.csv"
method = "QRILC"
folder_experiment = "runs/alzheimer_study"

Dump predictions#

df <-
  utils::read.csv(
    train_split,
    row.names = 1,
    header = TRUE,
    stringsAsFactors = FALSE
  )
df
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  • data.frame does not allow abritary column names, but only valid column names…

  • tibbles don’t support rownames, and the imputation methods rely on normal data.frames. Save the header row for later use.

original_header <- colnames(readr::read_csv(
  train_split,
  n_max = 1,
  col_names = TRUE,
  skip = 0
))
feat_name <- original_header[1]
original_header[1:5]

Rows: 1 Columns: 211
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr   (1): protein groups
dbl (180): Sample_000, Sample_002, Sample_003, Sample_004, Sample_005, Sampl...
lgl  (30): Sample_001, Sample_009, Sample_012, Sample_015, Sample_017, Sampl...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
  1. 'protein groups'
  2. 'Sample_000'
  3. 'Sample_001'
  4. 'Sample_002'
  5. 'Sample_003'

Uncomment to test certain methods (only for debugging, as at least one method per package is tested using Github Actions)

Hide code cell source

 
# to_test <- c(
# 'ZERO',
# 'MINIMUM',
# 'COLMEDIAN',
# 'ROWMEDIAN',
# 'KNN_IMPUTE',
# 'SEQKNN',
# 'BPCA',
# 'SVDMETHOD',
# 'LLS',
# 'MLE',
# 'LLS',
# 'QRILC',
# 'MINDET',
# 'MINPROB',
# 'IRM',
# 'IMPSEQ',
# 'IMPSEQROB',
# 'MICE-NORM',
# 'MICE-CART',
# 'RF',
# 'PI',
# 'GMS', # fails to install on Windows
# 'TRKNN',
# 'MSIMPUTE'
# 'MSIMPUTE_MNAR'
# 'GSIMP'
# )

# for (method in to_test) {
#     print(method)
#     pred <- nafunctions(df, method)
# }

Impute and save predictions with original feature and column names

pred <- nafunctions(df, method)
pred <- tibble::as_tibble(cbind(rownames(pred), pred))
names(pred) <- original_header
pred

Loading required package: impute
Loading required package: pcaMethods
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: ‘generics’
The following objects are masked from ‘package:base’:

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union

Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:

    anyDuplicated, aperm, append, as.data.frame, basename, cbind,
    colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
    get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Attaching package: ‘pcaMethods’
The following object is masked from ‘package:stats’:

    loadings
Loading required package: gmm
Loading required package: sandwich

Attaching package: ‘sandwich’
The following object is masked from ‘package:generics’:

    estfun
Loading required package: imputeLCMD
Loading required package: tmvtnorm
Loading required package: mvtnorm
Loading required package: Matrix
Loading required package: stats4
Loading required package: norm
This package has some major limitations
(for example, it does not work reliably when
the number of variables exceeds 30),
and has been superseded by the norm2 package.
A tibble: 1421 × 211
protein groupsSample_000Sample_001Sample_002Sample_003Sample_004Sample_005Sample_006Sample_007Sample_008⋯Sample_200Sample_201Sample_202Sample_203Sample_204Sample_205Sample_206Sample_207Sample_208Sample_209
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⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
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Q9Y6R7 18.0508520.2993319.5978519.42880419.5819819.13033118.6903018.9960519.993178⋯19.23304418.81636518.90212218.8128217.56885817.92184618.70814018.7247819.41113319.27470
Q9Y6X5 13.1007115.5555815.7352213.73277415.3282814.14341713.9747013.3446613.913571⋯15.89469415.56188616.08877415.6691115.01192716.34016013.69681516.1382715.80689815.73176
Q9Y6Y8;Q9Y6Y8-2 19.5628219.3857520.4473220.21596418.3117619.63319220.0568618.6804420.023229⋯17.48349319.80947920.20435719.4182317.84743319.92766519.43260119.5991219.54500119.57720
Q9Y6Y9 10.0476813.9699712.6364110.31958713.14453 9.10704412.8174112.8965810.590076⋯ 9.009445 9.75730912.70737612.9781212.28749512.92941010.78826113.6370510.50269611.04203
S4R3U6 12.8052112.4424512.5046612.444615 9.29327 8.885840 9.0081410.1460913.007934⋯ 8.70531410.132652 8.45109210.4976910.563347 8.522099 8.57497711.17371 6.47881511.79130

Transform predictions to long format

pred <- reshape2::melt(pred, id.vars = feat_name)
names(pred) <- c(feat_name, 'Sample ID', method)
pred <- pred[reshape2::melt(is.na(df))['value'] == TRUE, ]
pred
A data.frame: 71601 × 3
protein groupsSample IDQRILC
<chr><fct><dbl>
11A0A075B6J9 Sample_00015.13409
14A0A075B6P5;P01615 Sample_00017.38456
15A0A075B6Q5 Sample_00014.06765
16A0A075B6R2 Sample_00015.60997
19A0A075B6S5 Sample_00014.21878
24A0A087WSY4 Sample_00014.77500
34A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2 Sample_00015.07196
39A0A087WW87;A0A087X0Q4;P01614 Sample_00011.41634
40A0A087WWA5 Sample_00012.17141
42A0A087WWT2;Q9NPD7 Sample_00016.59693
43A0A087WX80;P24043 Sample_00015.11425
45A0A087WXE9;E9PQ70;Q6UXH9;Q6UXH9-2;Q6UXH9-3 Sample_00013.77784
48A0A087WYK9;Q02985;Q02985-2;Q6NSD3 Sample_00014.09576
52A0A087WZR4 Sample_00014.74269
53A0A087X089;Q16627;Q16627-2 Sample_00015.91068
56A0A087X0M8 Sample_00014.61873
59A0A087X117;A0A0G2JN29;J3KN36;P69849;Q15155;Q5JPE7;Q5JPE7-2 Sample_00013.97871
69A0A0A0MQS9;A0A0A0MTC7;Q16363;Q16363-2 Sample_00012.25286
78A0A0A0MS20;A0A0A0MSZ8;A0A0G2JM38;A0A0G2JM43;A0A0G2JM57;A0A0G2JM84;A0A0G2JMH7;A0A0G2JML1;A0A0G2JNE9;A0A0G2JNL1;A0A0G2JP25;A0A0G2JP84;A0A0G2JPA9;A0A0G2JPC7;A0A0G2JPU4;A0A0G2JPX5;A0A0G2JQ10;A0A0G2JQ20;A8MUE1;C9JST2;Q8NHJ6;Q8NHJ6-2;Q8NHJ6-3Sample_00012.02223
85A0A0A0MT32;P38571;P38571-2 Sample_00013.99503
87A0A0A0MT66 Sample_00013.82368
90A0A0A0MTC8;Q9NQ36;Q9NQ36-2;Q9NQ36-3 Sample_00011.64713
96A0A0A6YY99;O43508-2 Sample_00013.62312
101A0A0B4J1V2 Sample_00011.58697
102A0A0B4J1V6 Sample_00013.81351
105A0A0B4J1Z1;C9JAB2;Q16629;Q16629-2;Q16629-3;Q16629-4 Sample_00014.30886
108A0A0B4J2B5;S4R460 Sample_00020.61787
109A0A0B4J2C3;P13693;Q5W0H4 Sample_00011.44121
111A0A0C4DFP6;Q9NQ79;Q9NQ79-2 Sample_00019.61568
113A0A0C4DG76;Q5JZ08;Q9BQS7;Q9BQS7-2;Q9BQS7-3;Q9BQS7-4 Sample_00012.23914
⋮⋮⋮⋮
298242Q96PQ0 Sample_20915.989723
298244Q96RW7;Q96RW7-2 Sample_20912.188502
298246Q96SM3 Sample_20910.783104
298249Q99538 Sample_20911.977092
298252Q99650;Q99650-2 Sample_20912.601650
298261Q9BQT9;Q9BQT9-2 Sample_20918.530599
298266Q9BT88 Sample_20912.782897
298273Q9BX67 Sample_20917.204187
298277Q9BXP8 Sample_209 7.755163
298287Q9H3T2;Q9H3T2-3 Sample_20912.542139
298288Q9H3T3;Q9H3T3-3 Sample_20914.298468
298289Q9H492;Q9H492-2 Sample_20911.805117
298297Q9H8L6 Sample_20915.757678
298299Q9HBR0 Sample_20914.014470
298302Q9HC38-2 Sample_20912.329363
298303Q9HC57 Sample_20911.996417
298310Q9NPH3;Q9NPH3-2;Q9NPH3-5Sample_20913.592119
298314Q9NRB3 Sample_20912.116293
298316Q9NS00;Q9NS00-2 Sample_209 9.199453
298317Q9NS84 Sample_20910.247187
298328Q9NY97;Q9NY97-2 Sample_20915.113583
298341Q9P1W8;Q9P1W8-2;Q9P1W8-4Sample_20914.847175
298349Q9UGM5;Q9UGM5-2 Sample_20915.844813
298355Q9UI40;Q9UI40-2 Sample_20912.433239
298357Q9UIW2 Sample_20911.963730
298358Q9UJ14 Sample_20912.950174
298377Q9UMX0;Q9UMX0-2;Q9UMX0-4Sample_209 8.052035
298384Q9UP79 Sample_20914.553313
298388Q9Y240 Sample_20915.451265
298398Q9Y5Y7 Sample_20917.052800

Check dimension of long format dataframe

Hide code cell source

 
dim(pred)
  1. 71601
  2. 3

Save predictions to disk

Hide code cell source

 
fname = file.path(folder_experiment,
                  'preds',
                  paste0('pred_all_', toupper(method), '.csv'))
write_csv(pred, fname)
fname
'runs/alzheimer_study/preds/pred_all_QRILC.csv'
Contents