NAGuide R methods#

Setup basic methods and packages used for all methods

BiocManager could be moved to methods who are installed from BioConductor

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

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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 = "RF"
folder_experiment = "runs/alzheimer_study"

Dump predictions#

df <-
  utils::read.csv(
    train_split,
    row.names = 1,
    header = TRUE,
    stringsAsFactors = FALSE
  )
df

A data.frame: 1421 × 210
	Sample_000	Sample_001	Sample_002	Sample_003	Sample_004	Sample_005	Sample_006	Sample_007	Sample_008	Sample_009	⋯	Sample_200	Sample_201	Sample_202	Sample_203	Sample_204	Sample_205	Sample_206	Sample_207	Sample_208	Sample_209
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
A0A024QZX5;A0A087X1N8;P35237	15.91237	NA	16.11149	16.10697	15.60321	15.81161	15.49966	15.22075	15.98013	NA	⋯	NA	15.86264	15.65594	15.40126	NA	15.68225	15.79804	15.73949	15.47682	NA
A0A024R0T9;K7ER74;P02655	16.85194	16.87369	NA	17.03151	15.33051	18.61407	17.40917	17.68391	16.38625	16.58972	⋯	17.30955	16.61480	17.95339	18.19938	17.27911	16.88635	17.55400	NA	16.77948	17.26137
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	15.57047	15.51892	15.93532	15.80187	15.37522	15.62420	15.91185	15.38499	15.89447	15.37538	⋯	NA	15.93179	14.85871	15.12451	NA	14.91006	15.59966	15.46896	14.99496	15.17487
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	16.48108	16.38694	16.41577	16.97862	16.67906	15.95764	16.23389	16.41793	16.27134	16.20970	⋯	15.95402	16.02959	16.49753	16.51284	16.51269	16.48232	15.93782	16.89773	16.13246	16.23505
A0A075B6H7	17.30138	NA	18.17480	15.96322	NA	18.31720	NA	17.21393	17.79403	NA	⋯	NA	16.48262	14.83467	13.56893	14.48257	NA	NA	NA	NA	NA
A0A075B6H9	20.24646	19.94110	19.25090	19.62797	20.44983	18.68569	20.34722	20.79604	18.99523	19.24667	⋯	NA	NA	18.26986	19.80062	20.18315	17.70485	18.15450	18.63631	14.90754	17.89344
A0A075B6I0	16.76446	18.78624	16.83217	17.85209	18.68185	17.86543	18.24995	20.30936	17.97843	19.41047	⋯	18.00550	18.54142	17.80748	19.89865	19.67420	17.03945	18.15179	17.95040	NA	17.74443
A0A075B6I1	17.58357	17.14388	15.67073	18.87749	17.08131	14.82369	18.51969	19.35640	18.56449	17.96310	⋯	16.41402	17.50510	14.01911	17.50574	20.25111	NA	16.50267	16.32073	NA	16.37054
A0A075B6I6	16.98778	NA	17.01183	14.18153	14.14021	17.04179	17.37560	16.99794	17.20311	16.75839	⋯	15.72543	17.06758	16.45849	16.32778	16.33372	16.41304	16.86003	16.40071	16.11868	15.77958
A0A075B6I9	20.05416	19.06733	18.56906	18.98506	19.68564	19.37580	19.39885	20.54359	18.84397	18.89975	⋯	21.85263	20.14650	19.53459	19.86713	20.76626	19.10211	18.53796	18.84897	18.36776	18.80581
A0A075B6J9	NA	16.18799	NA	13.43813	14.49484	15.90875	16.18436	NA	16.17019	16.32362	⋯	NA	NA	16.63193	17.43574	19.77811	16.06423	NA	17.58044	NA	16.53239
A0A075B6K4	16.14850	16.12727	15.38672	16.56502	16.41771	16.45498	NA	17.04677	16.03210	16.14908	⋯	18.58162	18.51737	16.48354	16.06338	17.19123	15.35033	16.58173	NA	NA	16.33787
A0A075B6K5	17.34261	17.41722	17.23633	16.26717	17.39020	18.33604	17.21765	18.32488	17.52458	18.77608	⋯	19.51566	18.98906	17.80614	16.74819	18.41254	17.15384	17.90184	17.51899	17.50220	16.98874
A0A075B6P5;P01615	NA	NA	NA	16.98965	17.49330	18.26923	17.78989	18.15334	17.78703	18.35213	⋯	19.92434	NA	NA	19.84136	19.26620	17.17528	19.57506	19.30588	18.97651	18.68810
A0A075B6Q5	NA	NA	15.12836	NA	NA	NA	NA	16.73949	NA	NA	⋯	NA	17.02598	16.10287	15.87972	NA	NA	NA	16.88355	NA	15.68019
A0A075B6R2	NA	NA	16.27981	16.77744	17.49744	17.05396	16.91770	19.77222	16.73705	17.95670	⋯	17.99822	18.12131	19.19184	NA	17.28589	16.59777	17.07044	16.06459	NA	16.03648
A0A075B6R9;A0A0C4DH68	19.51640	19.37284	NA	19.00923	19.54147	19.56719	20.18507	20.56454	19.52324	19.38716	⋯	20.12749	20.37360	19.49983	18.97739	20.58832	18.07337	18.84673	19.62054	19.33926	20.39256
A0A075B6S2	15.51124	16.20824	14.84570	15.23242	NA	16.28653	14.65632	NA	14.51212	16.56925	⋯	15.07423	15.91304	16.65963	15.80117	17.45378	14.10153	16.61213	16.21984	15.69582	16.38320
A0A075B6S5	NA	NA	NA	NA	NA	15.96016	16.15277	17.07842	16.84659	16.33385	⋯	15.51315	16.14929	NA	15.35133	15.91161	15.65170	16.03424	NA	NA	NA
A0A075B6S9	16.55765	17.51597	17.93999	16.95147	17.08795	17.36099	16.42103	19.79080	17.13399	15.47317	⋯	17.25958	20.50218	19.26507	18.42468	18.19725	16.14563	19.50373	18.42501	17.75190	16.21995
A0A075B7B8	14.91594	NA	14.65865	NA	15.34886	14.50111	15.12196	14.51142	15.31218	NA	⋯	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
A0A075B7D0	18.35754	NA	18.23018	19.26103	19.78637	19.65551	18.51593	20.50008	19.50572	20.00935	⋯	NA	19.23066	NA	18.07711	19.43528	19.04498	17.02163	18.08262	18.73767	17.31493
A0A087WSV8;V9HW75	15.67587	14.50492	NA	15.55301	15.52087	NA	15.21659	NA	13.85270	NA	⋯	NA	15.52576	15.20934	15.53814	13.98360	15.14917	15.89044	NA	15.41872	14.79854
A0A087WSY4	NA	17.14993	15.65052	NA	NA	NA	NA	NA	NA	NA	⋯	15.85031	NA	16.20781	15.33813	NA	15.12749	NA	NA	NA	20.34338
A0A087WSY5;Q96IY4	17.86323	18.04130	17.77668	18.34178	18.42888	17.85131	17.68953	19.12984	18.01868	18.23196	⋯	17.86117	17.12731	17.28784	18.35734	18.09213	17.11496	17.19541	17.94426	18.08478	17.34736
A0A087WSY6	20.11686	20.27450	19.28080	19.78190	19.72658	20.79580	19.63239	20.59759	19.96610	18.72439	⋯	21.28155	19.51359	19.24474	19.73663	19.37543	NA	19.79855	18.65092	18.43422	19.15982
A0A087WSZ0;A0A0G2JQJ0	16.68463	NA	NA	NA	NA	13.03775	NA	15.25869	15.59578	13.63975	⋯	NA	15.97835	14.31120	NA	17.32747	14.88972	15.63658	13.74259	16.47652	13.66795
A0A087WTA1;A0A0A0MR20;E9PDN6;F5H107;Q9C0A0;Q9C0A0-2	17.59377	18.01240	17.82921	18.26850	17.55706	17.38051	17.31082	16.40859	17.37506	18.02908	⋯	16.94249	17.74655	NA	17.69916	NA	18.08000	17.48057	17.81535	18.18624	17.91903
A0A087WTA8;P08123	17.61908	18.21685	17.54445	17.50225	17.80288	16.99184	17.97739	17.70994	17.53213	17.35045	⋯	17.56219	16.57279	16.90066	NA	NA	16.93402	16.74856	NA	17.17757	17.26857
A0A087WTK0;A0A087WVC6;Q12913	15.50121	15.30696	15.81471	15.55156	15.32131	16.04415	16.18863	NA	15.56486	15.37830	⋯	NA	15.76029	15.21911	NA	NA	NA	15.39396	15.24665	NA	NA
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋱	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Q9UN70;Q9UN70-2	16.15734	15.72455	16.43182	16.23052	15.95639	NA	16.43843	15.63146	15.72126	15.74455	⋯	NA	15.68391	15.49913	15.98585	15.02979	15.48571	15.52798	16.08012	15.63165	15.77433
Q9UNN8	17.56056	17.30159	17.76464	NA	17.11591	18.19042	17.97682	17.00159	17.09973	NA	⋯	16.77195	NA	17.77967	17.59030	17.37821	16.45713	16.45075	17.89332	17.63618	17.39555
Q9UNW1	16.95997	16.51290	16.14734	NA	16.50818	15.64714	16.41814	16.48681	16.31438	17.10669	⋯	NA	16.21768	NA	16.06726	NA	16.31734	16.21004	16.57037	16.17868	16.53072
Q9UP79	15.15310	NA	16.03621	15.41609	15.50277	16.33833	15.89802	14.59940	NA	15.94205	⋯	NA	NA	15.08015	NA	15.26104	15.73977	NA	NA	16.10664	NA
Q9UPU3	18.32540	18.23025	NA	18.10040	18.17738	18.46288	17.90076	17.75121	18.74321	17.74624	⋯	16.78448	18.47047	19.03005	18.64856	18.11089	18.42196	18.36937	17.64059	17.24210	18.27864
Q9UQ52	15.34901	15.54989	15.71187	NA	15.62809	15.53902	15.40031	14.55045	15.08724	15.21233	⋯	15.81437	15.64994	15.60246	NA	15.04666	15.71637	16.12330	15.68571	15.83485	16.18904
Q9UQM7	16.77983	16.63557	16.92800	17.36468	17.26139	16.57292	16.34126	NA	17.18529	NA	⋯	15.44938	17.13915	17.46598	15.95446	16.01651	17.90825	16.22326	17.08973	16.38845	17.38407
Q9Y240	16.25700	16.27154	16.57780	17.13331	NA	16.76109	16.90810	16.87980	15.26393	16.66807	⋯	16.74831	16.53842	16.90567	16.72346	16.61396	16.71442	16.25572	16.50802	17.28611	NA
Q9Y279;Q9Y279-2	17.16794	17.44556	17.36345	17.29289	17.22047	16.98754	17.17998	NA	17.16304	16.55641	⋯	15.02893	16.54589	16.58450	16.58755	17.65963	16.61656	16.70633	NA	NA	16.95138
Q9Y281;Q9Y281-3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	⋯	NA	12.30069	NA	NA	NA	13.01033	13.07666	NA	NA	12.54090
Q9Y287	17.66289	17.58819	NA	17.69614	17.73541	17.03094	16.95083	18.26680	NA	16.89611	⋯	NA	NA	16.57866	17.97074	17.10728	18.27608	16.47716	16.54354	16.17378	17.82544
Q9Y2I2;Q9Y2I2-1;Q9Y2I2-2;Q9Y2I2-4;Q9Y2I2-5;Q9Y2I2-6	16.92776	17.22074	17.42595	17.29861	17.82816	16.74181	16.72069	15.90499	16.39611	17.36935	⋯	16.22026	16.70635	17.89304	17.70659	17.09718	NA	16.95730	18.37587	17.57333	17.93369
Q9Y2T3;Q9Y2T3-3	18.46767	17.72113	18.44012	18.86565	18.73226	18.63877	NA	17.11557	19.33203	18.09338	⋯	16.52746	18.16354	17.65092	17.52026	17.41693	18.62477	18.26107	18.51419	18.00650	17.90217
Q9Y490	NA	13.96193	NA	NA	13.62690	13.38121	13.74195	NA	NA	13.37095	⋯	NA	NA	NA	NA	NA	12.93233	13.29442	NA	13.84086	13.52534
Q9Y4L1	18.59795	18.47622	NA	18.55964	18.30465	18.52379	18.36021	17.87471	18.67258	18.43131	⋯	17.18673	18.41406	18.26710	18.08557	17.59792	18.28991	17.97720	18.14875	17.88055	18.12515
Q9Y5F6;Q9Y5F6-2	16.46929	15.78240	NA	16.52899	16.28480	16.86344	15.89953	16.10952	17.40132	16.34367	⋯	15.45926	16.42884	16.15950	15.63156	14.73569	15.96821	16.88481	15.87772	15.55424	16.57524
Q9Y5I4;Q9Y5I4-2	17.18701	17.44717	17.41043	17.54514	17.29667	17.39728	17.14532	16.40482	17.32308	17.45753	⋯	15.89731	17.13489	NA	17.08588	15.87386	17.10354	17.10902	16.93840	17.15508	16.77629
Q9Y5Y7	18.83957	19.19495	19.08820	18.71497	18.66824	19.05133	19.08711	18.80245	18.71439	18.78745	⋯	17.13452	17.83727	18.78562	18.61367	18.73747	18.72639	18.46011	19.50151	NA	NA
Q9Y617	16.85897	16.79927	16.28782	17.07490	NA	16.49952	16.70264	15.92398	16.63775	16.44277	⋯	14.58263	14.93988	15.86970	15.71177	15.46507	15.80831	15.03529	16.28306	15.91960	15.71292
Q9Y646	19.32196	19.19029	19.70194	19.76013	19.62369	19.28628	19.62219	19.01644	19.15012	19.51652	⋯	19.86156	20.24484	19.20731	20.30281	19.43507	19.89388	20.01457	20.30614	20.20342	20.04164
Q9Y653;Q9Y653-2;Q9Y653-3	16.01243	15.52794	15.22871	15.49484	14.75668	15.08563	15.78890	NA	15.63408	NA	⋯	NA	14.95201	15.53361	15.06014	15.66794	NA	15.42228	15.80796	15.15740	15.23671
Q9Y696	15.17778	15.57553	14.72770	14.59034	NA	14.85995	16.13632	14.94928	15.26568	14.63326	⋯	NA	15.84479	15.80784	16.16574	15.91448	15.68387	16.10633	16.09780	16.71176	15.65174
Q9Y6C2	NA	NA	13.75658	14.68157	NA	NA	NA	14.31805	NA	NA	⋯	NA	NA	NA	NA	14.20407	14.23561	NA	14.40272	NA	15.21141
Q9Y6N6	15.05021	14.83290	15.11828	15.14018	15.25588	14.69813	NA	NA	15.29271	NA	⋯	14.74282	15.12482	15.24568	15.28502	15.02461	15.41504	15.34536	15.71532	14.63983	14.20491
Q9Y6N7;Q9Y6N7-2;Q9Y6N7-4	16.84221	16.59739	17.43952	17.35608	17.07495	17.08803	16.71735	NA	17.45319	16.91617	⋯	NA	17.03425	16.74779	16.39020	NA	17.55092	17.08447	NA	16.53276	16.74913
Q9Y6R7	NA	20.29933	19.59785	19.42880	19.58198	19.13033	18.69030	18.99605	19.99318	18.55034	⋯	19.23304	18.81636	18.90212	18.81282	NA	17.92185	18.70814	18.72478	19.41113	19.27470
Q9Y6X5	NA	15.55558	15.73522	NA	15.32828	NA	NA	NA	NA	NA	⋯	15.89469	15.56189	16.08877	15.66911	15.01193	16.34016	NA	16.13827	15.80690	15.73176
Q9Y6Y8;Q9Y6Y8-2	19.56282	19.38575	20.44732	20.21596	NA	19.63319	20.05686	18.68044	20.02323	19.94839	⋯	NA	19.80948	20.20436	19.41823	17.84743	19.92766	19.43260	19.59912	19.54500	19.57720
Q9Y6Y9	NA	13.96997	12.63641	NA	13.14453	NA	12.81741	12.89658	NA	13.68470	⋯	NA	NA	12.70738	12.97812	12.28750	12.92941	NA	13.63705	NA	11.04203
S4R3U6	12.80521	12.44245	12.50466	12.44461	NA	NA	NA	NA	13.00793	NA	⋯	NA	10.13265	NA	10.49769	10.56335	NA	NA	11.17371	NA	11.79130

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.

'protein groups'
'Sample_000'
'Sample_001'
'Sample_002'
'Sample_003'

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

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

  missForest iteration 1 in progress...done!
    estimated error(s): 0.2705111 
    difference(s): 0.0001504112 
    time: 58.137 seconds

  missForest iteration 2 in progress...done!
    estimated error(s): 0.2693195 
    difference(s): 4.612918e-05 
    time: 59.706 seconds

  missForest iteration 3 in progress...done!
    estimated error(s): 0.2684073 
    difference(s): 4.494503e-05 
    time: 59.441 seconds

  missForest iteration 4 in progress...done!
    estimated error(s): 0.2685591 
    difference(s): 4.488579e-05 
    time: 59.574 seconds

  missForest iteration 5 in progress...done!
    estimated error(s): 0.2683091 
    difference(s): 4.381284e-05 
    time: 59.575 seconds

  missForest iteration 6 in progress...done!
    estimated error(s): 0.2676976 
    difference(s): 4.480632e-05 
    time: 59.425 seconds

Loading required package: missForest

A tibble: 1421 × 211
protein groups	Sample_000	Sample_001	Sample_002	Sample_003	Sample_004	Sample_005	Sample_006	Sample_007	Sample_008	⋯	Sample_200	Sample_201	Sample_202	Sample_203	Sample_204	Sample_205	Sample_206	Sample_207	Sample_208	Sample_209
<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
A0A024QZX5;A0A087X1N8;P35237	15.91237	15.84029	16.11149	16.10697	15.60321	15.81161	15.49966	15.22075	15.98013	⋯	15.08749	15.86264	15.65594	15.40126	15.17372	15.68225	15.79804	15.73949	15.47682	15.60476
A0A024R0T9;K7ER74;P02655	16.85194	16.87369	16.78992	17.03151	15.33051	18.61407	17.40917	17.68391	16.38625	⋯	17.30955	16.61480	17.95339	18.19938	17.27911	16.88635	17.55400	17.22079	16.77948	17.26137
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	15.57047	15.51892	15.93532	15.80187	15.37522	15.62420	15.91185	15.38499	15.89447	⋯	15.44458	15.93179	14.85871	15.12451	15.33222	14.91006	15.59966	15.46896	14.99496	15.17487
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	16.48108	16.38694	16.41577	16.97862	16.67906	15.95764	16.23389	16.41793	16.27134	⋯	15.95402	16.02959	16.49753	16.51284	16.51269	16.48232	15.93782	16.89773	16.13246	16.23505
A0A075B6H7	17.30138	16.95711	18.17480	15.96322	16.64522	18.31720	16.95049	17.21393	17.79403	⋯	15.76348	16.48262	14.83467	13.56893	14.48257	15.55366	15.92702	15.60091	15.38474	15.25431
A0A075B6H9	20.24646	19.94110	19.25090	19.62797	20.44983	18.68569	20.34722	20.79604	18.99523	⋯	18.96869	18.71669	18.26986	19.80062	20.18315	17.70485	18.15450	18.63631	14.90754	17.89344
A0A075B6I0	16.76446	18.78624	16.83217	17.85209	18.68185	17.86543	18.24995	20.30936	17.97843	⋯	18.00550	18.54142	17.80748	19.89865	19.67420	17.03945	18.15179	17.95040	18.35456	17.74443
A0A075B6I1	17.58357	17.14388	15.67073	18.87749	17.08131	14.82369	18.51969	19.35640	18.56449	⋯	16.41402	17.50510	14.01911	17.50574	20.25111	16.05828	16.50267	16.32073	16.51767	16.37054
A0A075B6I6	16.98778	16.66234	17.01183	14.18153	14.14021	17.04179	17.37560	16.99794	17.20311	⋯	15.72543	17.06758	16.45849	16.32778	16.33372	16.41304	16.86003	16.40071	16.11868	15.77958
A0A075B6I9	20.05416	19.06733	18.56906	18.98506	19.68564	19.37580	19.39885	20.54359	18.84397	⋯	21.85263	20.14650	19.53459	19.86713	20.76626	19.10211	18.53796	18.84897	18.36776	18.80581
A0A075B6J9	16.01476	16.18799	16.07672	13.43813	14.49484	15.90875	16.18436	16.86180	16.17019	⋯	17.33854	16.73617	16.63193	17.43574	19.77811	16.06423	16.60457	17.58044	16.33456	16.53239
A0A075B6K4	16.14850	16.12727	15.38672	16.56502	16.41771	16.45498	16.51324	17.04677	16.03210	⋯	18.58162	18.51737	16.48354	16.06338	17.19123	15.35033	16.58173	16.29325	16.54161	16.33787
A0A075B6K5	17.34261	17.41722	17.23633	16.26717	17.39020	18.33604	17.21765	18.32488	17.52458	⋯	19.51566	18.98906	17.80614	16.74819	18.41254	17.15384	17.90184	17.51899	17.50220	16.98874
A0A075B6P5;P01615	17.45691	17.84878	17.84511	16.98965	17.49330	18.26923	17.78989	18.15334	17.78703	⋯	19.92434	19.39035	18.92380	19.84136	19.26620	17.17528	19.57506	19.30588	18.97651	18.68810
A0A075B6Q5	16.22468	16.39511	15.12836	16.20109	16.29774	16.12936	16.15064	16.73949	16.10624	⋯	17.45611	17.02598	16.10287	15.87972	17.12288	16.00441	16.46571	16.88355	16.58935	15.68019
A0A075B6R2	16.67987	16.94435	16.27981	16.77744	17.49744	17.05396	16.91770	19.77222	16.73705	⋯	17.99822	18.12131	19.19184	17.09879	17.28589	16.59777	17.07044	16.06459	16.88341	16.03648
A0A075B6R9;A0A0C4DH68	19.51640	19.37284	19.55195	19.00923	19.54147	19.56719	20.18507	20.56454	19.52324	⋯	20.12749	20.37360	19.49983	18.97739	20.58832	18.07337	18.84673	19.62054	19.33926	20.39256
A0A075B6S2	15.51124	16.20824	14.84570	15.23242	15.46329	16.28653	14.65632	16.35820	14.51212	⋯	15.07423	15.91304	16.65963	15.80117	17.45378	14.10153	16.61213	16.21984	15.69582	16.38320
A0A075B6S5	16.22195	16.35152	16.28406	16.26576	16.11085	15.96016	16.15277	17.07842	16.84659	⋯	15.51315	16.14929	15.77607	15.35133	15.91161	15.65170	16.03424	15.79294	15.99561	15.93857
A0A075B6S9	16.55765	17.51597	17.93999	16.95147	17.08795	17.36099	16.42103	19.79080	17.13399	⋯	17.25958	20.50218	19.26507	18.42468	18.19725	16.14563	19.50373	18.42501	17.75190	16.21995
A0A075B7B8	14.91594	15.19535	14.65865	14.93874	15.34886	14.50111	15.12196	14.51142	15.31218	⋯	15.60905	15.37295	14.89707	15.21907	15.78340	14.90643	15.28243	15.15067	15.47140	15.08330
A0A075B7D0	18.35754	19.01161	18.23018	19.26103	19.78637	19.65551	18.51593	20.50008	19.50572	⋯	19.52879	19.23066	18.68018	18.07711	19.43528	19.04498	17.02163	18.08262	18.73767	17.31493
A0A087WSV8;V9HW75	15.67587	14.50492	15.06367	15.55301	15.52087	15.06898	15.21659	14.94962	13.85270	⋯	14.79390	15.52576	15.20934	15.53814	13.98360	15.14917	15.89044	15.25727	15.41872	14.79854
A0A087WSY4	16.20230	17.14993	15.65052	16.22730	16.20415	16.07931	16.15073	16.02920	16.13697	⋯	15.85031	16.31375	16.20781	15.33813	15.76914	15.12749	16.13627	15.50597	15.80377	20.34338
A0A087WSY5;Q96IY4	17.86323	18.04130	17.77668	18.34178	18.42888	17.85131	17.68953	19.12984	18.01868	⋯	17.86117	17.12731	17.28784	18.35734	18.09213	17.11496	17.19541	17.94426	18.08478	17.34736
A0A087WSY6	20.11686	20.27450	19.28080	19.78190	19.72658	20.79580	19.63239	20.59759	19.96610	⋯	21.28155	19.51359	19.24474	19.73663	19.37543	18.73426	19.79855	18.65092	18.43422	19.15982
A0A087WSZ0;A0A0G2JQJ0	16.68463	16.07521	14.90847	15.67622	16.12736	13.03775	15.71738	15.25869	15.59578	⋯	15.96259	15.97835	14.31120	15.35819	17.32747	14.88972	15.63658	13.74259	16.47652	13.66795
A0A087WTA1;A0A0A0MR20;E9PDN6;F5H107;Q9C0A0;Q9C0A0-2	17.59377	18.01240	17.82921	18.26850	17.55706	17.38051	17.31082	16.40859	17.37506	⋯	16.94249	17.74655	17.94640	17.69916	16.91318	18.08000	17.48057	17.81535	18.18624	17.91903
A0A087WTA8;P08123	17.61908	18.21685	17.54445	17.50225	17.80288	16.99184	17.97739	17.70994	17.53213	⋯	17.56219	16.57279	16.90066	17.13542	17.30567	16.93402	16.74856	17.26398	17.17757	17.26857
A0A087WTK0;A0A087WVC6;Q12913	15.50121	15.30696	15.81471	15.55156	15.32131	16.04415	16.18863	15.64121	15.56486	⋯	15.04983	15.76029	15.21911	14.96765	15.09241	15.24389	15.39396	15.24665	14.93088	14.92098
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋱	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Q9UN70;Q9UN70-2	16.15734	15.72455	16.43182	16.23052	15.95639	16.12606	16.43843	15.63146	15.72126	⋯	15.48897	15.68391	15.49913	15.98585	15.02979	15.48571	15.52798	16.08012	15.63165	15.77433
Q9UNN8	17.56056	17.30159	17.76464	17.28216	17.11591	18.19042	17.97682	17.00159	17.09973	⋯	16.77195	16.78080	17.77967	17.59030	17.37821	16.45713	16.45075	17.89332	17.63618	17.39555
Q9UNW1	16.95997	16.51290	16.14734	16.45280	16.50818	15.64714	16.41814	16.48681	16.31438	⋯	15.95512	16.21768	16.35601	16.06726	16.14016	16.31734	16.21004	16.57037	16.17868	16.53072
Q9UP79	15.15310	15.69241	16.03621	15.41609	15.50277	16.33833	15.89802	14.59940	15.75504	⋯	15.33888	15.47446	15.08015	15.50880	15.26104	15.73977	15.58982	15.70117	16.10664	15.81932
Q9UPU3	18.32540	18.23025	18.54636	18.10040	18.17738	18.46288	17.90076	17.75121	18.74321	⋯	16.78448	18.47047	19.03005	18.64856	18.11089	18.42196	18.36937	17.64059	17.24210	18.27864
Q9UQ52	15.34901	15.54989	15.71187	15.47890	15.62809	15.53902	15.40031	14.55045	15.08724	⋯	15.81437	15.64994	15.60246	15.82914	15.04666	15.71637	16.12330	15.68571	15.83485	16.18904
Q9UQM7	16.77983	16.63557	16.92800	17.36468	17.26139	16.57292	16.34126	15.99672	17.18529	⋯	15.44938	17.13915	17.46598	15.95446	16.01651	17.90825	16.22326	17.08973	16.38845	17.38407
Q9Y240	16.25700	16.27154	16.57780	17.13331	16.38201	16.76109	16.90810	16.87980	15.26393	⋯	16.74831	16.53842	16.90567	16.72346	16.61396	16.71442	16.25572	16.50802	17.28611	16.40021
Q9Y279;Q9Y279-2	17.16794	17.44556	17.36345	17.29289	17.22047	16.98754	17.17998	16.77255	17.16304	⋯	15.02893	16.54589	16.58450	16.58755	17.65963	16.61656	16.70633	16.58847	16.66235	16.95138
Q9Y281;Q9Y281-3	12.68059	12.69959	12.73054	12.80831	12.77504	12.65363	12.73370	12.71726	12.78950	⋯	12.58062	12.30069	12.65079	12.67463	12.77706	13.01033	13.07666	12.66105	12.53400	12.54090
Q9Y287	17.66289	17.58819	17.51150	17.69614	17.73541	17.03094	16.95083	18.26680	17.33407	⋯	16.60311	17.17921	16.57866	17.97074	17.10728	18.27608	16.47716	16.54354	16.17378	17.82544
Q9Y2I2;Q9Y2I2-1;Q9Y2I2-2;Q9Y2I2-4;Q9Y2I2-5;Q9Y2I2-6	16.92776	17.22074	17.42595	17.29861	17.82816	16.74181	16.72069	15.90499	16.39611	⋯	16.22026	16.70635	17.89304	17.70659	17.09718	17.63592	16.95730	18.37587	17.57333	17.93369
Q9Y2T3;Q9Y2T3-3	18.46767	17.72113	18.44012	18.86565	18.73226	18.63877	17.70220	17.11557	19.33203	⋯	16.52746	18.16354	17.65092	17.52026	17.41693	18.62477	18.26107	18.51419	18.00650	17.90217
Q9Y490	13.30881	13.96193	13.30410	13.32088	13.62690	13.38121	13.74195	13.15199	12.96621	⋯	12.61075	12.60659	13.20322	13.40470	12.89103	12.93233	13.29442	13.36376	13.84086	13.52534
Q9Y4L1	18.59795	18.47622	18.48751	18.55964	18.30465	18.52379	18.36021	17.87471	18.67258	⋯	17.18673	18.41406	18.26710	18.08557	17.59792	18.28991	17.97720	18.14875	17.88055	18.12515
Q9Y5F6;Q9Y5F6-2	16.46929	15.78240	16.65586	16.52899	16.28480	16.86344	15.89953	16.10952	17.40132	⋯	15.45926	16.42884	16.15950	15.63156	14.73569	15.96821	16.88481	15.87772	15.55424	16.57524
Q9Y5I4;Q9Y5I4-2	17.18701	17.44717	17.41043	17.54514	17.29667	17.39728	17.14532	16.40482	17.32308	⋯	15.89731	17.13489	17.00432	17.08588	15.87386	17.10354	17.10902	16.93840	17.15508	16.77629
Q9Y5Y7	18.83957	19.19495	19.08820	18.71497	18.66824	19.05133	19.08711	18.80245	18.71439	⋯	17.13452	17.83727	18.78562	18.61367	18.73747	18.72639	18.46011	19.50151	18.95844	18.92175
Q9Y617	16.85897	16.79927	16.28782	17.07490	16.73074	16.49952	16.70264	15.92398	16.63775	⋯	14.58263	14.93988	15.86970	15.71177	15.46507	15.80831	15.03529	16.28306	15.91960	15.71292
Q9Y646	19.32196	19.19029	19.70194	19.76013	19.62369	19.28628	19.62219	19.01644	19.15012	⋯	19.86156	20.24484	19.20731	20.30281	19.43507	19.89388	20.01457	20.30614	20.20342	20.04164
Q9Y653;Q9Y653-2;Q9Y653-3	16.01243	15.52794	15.22871	15.49484	14.75668	15.08563	15.78890	14.96222	15.63408	⋯	14.85800	14.95201	15.53361	15.06014	15.66794	15.53845	15.42228	15.80796	15.15740	15.23671
Q9Y696	15.17778	15.57553	14.72770	14.59034	14.97339	14.85995	16.13632	14.94928	15.26568	⋯	15.37105	15.84479	15.80784	16.16574	15.91448	15.68387	16.10633	16.09780	16.71176	15.65174
Q9Y6C2	14.02034	14.10191	13.75658	14.68157	14.20023	14.13762	14.32272	14.31805	14.12170	⋯	14.61378	14.33752	14.33924	14.28994	14.20407	14.23561	14.53347	14.40272	14.41849	15.21141
Q9Y6N6	15.05021	14.83290	15.11828	15.14018	15.25588	14.69813	15.21567	14.91985	15.29271	⋯	14.74282	15.12482	15.24568	15.28502	15.02461	15.41504	15.34536	15.71532	14.63983	14.20491
Q9Y6N7;Q9Y6N7-2;Q9Y6N7-4	16.84221	16.59739	17.43952	17.35608	17.07495	17.08803	16.71735	16.29283	17.45319	⋯	15.83474	17.03425	16.74779	16.39020	15.91796	17.55092	17.08447	16.84368	16.53276	16.74913
Q9Y6R7	19.55106	20.29933	19.59785	19.42880	19.58198	19.13033	18.69030	18.99605	19.99318	⋯	19.23304	18.81636	18.90212	18.81282	19.29485	17.92185	18.70814	18.72478	19.41113	19.27470
Q9Y6X5	15.66029	15.55558	15.73522	15.75633	15.32828	15.47462	15.54544	15.08669	15.36511	⋯	15.89469	15.56189	16.08877	15.66911	15.01193	16.34016	15.23758	16.13827	15.80690	15.73176
Q9Y6Y8;Q9Y6Y8-2	19.56282	19.38575	20.44732	20.21596	19.80653	19.63319	20.05686	18.68044	20.02323	⋯	18.74485	19.80948	20.20436	19.41823	17.84743	19.92766	19.43260	19.59912	19.54500	19.57720
Q9Y6Y9	12.83380	13.96997	12.63641	12.94764	13.14453	12.56456	12.81741	12.89658	12.09813	⋯	11.65707	12.02445	12.70738	12.97812	12.28750	12.92941	12.11197	13.63705	12.45287	11.04203
S4R3U6	12.80521	12.44245	12.50466	12.44461	12.00246	11.84962	11.65440	10.95838	13.00793	⋯	10.40804	10.13265	11.69686	10.49769	10.56335	11.73817	11.02197	11.17371	11.31758	11.79130

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 groups	Sample ID	RF
	<chr>	<fct>	<dbl>
11	A0A075B6J9	Sample_000	16.01476
14	A0A075B6P5;P01615	Sample_000	17.45691
15	A0A075B6Q5	Sample_000	16.22468
16	A0A075B6R2	Sample_000	16.67987
19	A0A075B6S5	Sample_000	16.22195
24	A0A087WSY4	Sample_000	16.20230
34	A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2	Sample_000	15.85156
39	A0A087WW87;A0A087X0Q4;P01614	Sample_000	12.87723
40	A0A087WWA5	Sample_000	13.78484
42	A0A087WWT2;Q9NPD7	Sample_000	17.84627
43	A0A087WX80;P24043	Sample_000	15.99879
45	A0A087WXE9;E9PQ70;Q6UXH9;Q6UXH9-2;Q6UXH9-3	Sample_000	14.44208
48	A0A087WYK9;Q02985;Q02985-2;Q6NSD3	Sample_000	15.68448
52	A0A087WZR4	Sample_000	15.15797
53	A0A087X089;Q16627;Q16627-2	Sample_000	17.67530
56	A0A087X0M8	Sample_000	16.33499
59	A0A087X117;A0A0G2JN29;J3KN36;P69849;Q15155;Q5JPE7;Q5JPE7-2	Sample_000	15.28430
69	A0A0A0MQS9;A0A0A0MTC7;Q16363;Q16363-2	Sample_000	14.33491
78	A0A0A0MS20;A0A0A0MSZ8;A0A0G2JM38;A0A0G2JM43;A0A0G2JM57;A0A0G2JM84;A0A0G2JMH7;A0A0G2JML1;A0A0G2JNE9;A0A0G2JNL1;A0A0G2JP25;A0A0G2JP84;A0A0G2JPA9;A0A0G2JPC7;A0A0G2JPU4;A0A0G2JPX5;A0A0G2JQ10;A0A0G2JQ20;A8MUE1;C9JST2;Q8NHJ6;Q8NHJ6-2;Q8NHJ6-3	Sample_000	14.23276
85	A0A0A0MT32;P38571;P38571-2	Sample_000	15.06992
87	A0A0A0MT66	Sample_000	14.85113
90	A0A0A0MTC8;Q9NQ36;Q9NQ36-2;Q9NQ36-3	Sample_000	14.54506
96	A0A0A6YY99;O43508-2	Sample_000	14.43242
101	A0A0B4J1V2	Sample_000	14.48490
102	A0A0B4J1V6	Sample_000	15.42640
105	A0A0B4J1Z1;C9JAB2;Q16629;Q16629-2;Q16629-3;Q16629-4	Sample_000	15.65114
108	A0A0B4J2B5;S4R460	Sample_000	22.16119
109	A0A0B4J2C3;P13693;Q5W0H4	Sample_000	13.09376
111	A0A0C4DFP6;Q9NQ79;Q9NQ79-2	Sample_000	21.14948
113	A0A0C4DG76;Q5JZ08;Q9BQS7;Q9BQS7-2;Q9BQS7-3;Q9BQS7-4	Sample_000	15.77492
⋮	⋮	⋮	⋮
298242	Q96PQ0	Sample_209	17.83108
298244	Q96RW7;Q96RW7-2	Sample_209	14.72009
298246	Q96SM3	Sample_209	16.26275
298249	Q99538	Sample_209	16.09649
298252	Q99650;Q99650-2	Sample_209	13.89059
298261	Q9BQT9;Q9BQT9-2	Sample_209	19.56167
298266	Q9BT88	Sample_209	14.91044
298273	Q9BX67	Sample_209	18.41394
298277	Q9BXP8	Sample_209	14.97515
298287	Q9H3T2;Q9H3T2-3	Sample_209	13.72666
298288	Q9H3T3;Q9H3T3-3	Sample_209	15.93382
298289	Q9H492;Q9H492-2	Sample_209	13.38755
298297	Q9H8L6	Sample_209	17.06941
298299	Q9HBR0	Sample_209	14.48416
298302	Q9HC38-2	Sample_209	13.94528
298303	Q9HC57	Sample_209	14.65698
298310	Q9NPH3;Q9NPH3-2;Q9NPH3-5	Sample_209	15.16223
298314	Q9NRB3	Sample_209	14.63423
298316	Q9NS00;Q9NS00-2	Sample_209	13.30051
298317	Q9NS84	Sample_209	12.50508
298328	Q9NY97;Q9NY97-2	Sample_209	17.10187
298341	Q9P1W8;Q9P1W8-2;Q9P1W8-4	Sample_209	16.22233
298349	Q9UGM5;Q9UGM5-2	Sample_209	16.42199
298355	Q9UI40;Q9UI40-2	Sample_209	16.61194
298357	Q9UIW2	Sample_209	16.30249
298358	Q9UJ14	Sample_209	14.22005
298377	Q9UMX0;Q9UMX0-2;Q9UMX0-4	Sample_209	13.33208
298384	Q9UP79	Sample_209	15.81932
298388	Q9Y240	Sample_209	16.40021
298398	Q9Y5Y7	Sample_209	18.92175

Check dimension of long format dataframe

71601
3

Save predictions to disk

'runs/alzheimer_study/preds/pred_all_RF.csv'

NAGuide R methods

Contents

NAGuide R methods#

Parameters#

Dump predictions#