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 = "PI"
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

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	11.41296	16.11149	16.10697	15.60321	15.81161	15.49966	15.22075	15.98013	⋯	12.46941	15.86264	15.65594	15.40126	12.96634	15.68225	15.79804	15.73949	15.47682	12.16677
A0A024R0T9;K7ER74;P02655	16.85194	16.87369	13.83512	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	13.84641	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	⋯	13.45219	15.93179	14.85871	15.12451	12.43086	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	12.37810	18.17480	15.96322	13.51290	18.31720	12.39943	17.21393	17.79403	⋯	12.00550	16.48262	14.83467	13.56893	14.48257	12.33601	12.27390	12.78968	13.07583	12.32100
A0A075B6H9	20.24646	19.94110	19.25090	19.62797	20.44983	18.68569	20.34722	20.79604	18.99523	⋯	12.51220	13.47115	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	13.90326	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	13.68822	16.50267	16.32073	13.15046	16.37054
A0A075B6I6	16.98778	13.30905	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	13.41153	16.18799	13.02152	13.43813	14.49484	15.90875	16.18436	13.12031	16.17019	⋯	11.99204	13.60476	16.63193	17.43574	19.77811	16.06423	12.94660	17.58044	12.33596	16.53239
A0A075B6K4	16.14850	16.12727	15.38672	16.56502	16.41771	16.45498	13.32874	17.04677	16.03210	⋯	18.58162	18.51737	16.48354	16.06338	17.19123	15.35033	16.58173	12.96146	12.61743	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	13.55415	12.47817	12.71989	16.98965	17.49330	18.26923	17.78989	18.15334	17.78703	⋯	19.92434	11.90822	13.12237	19.84136	19.26620	17.17528	19.57506	19.30588	18.97651	18.68810
A0A075B6Q5	13.96710	12.82597	15.12836	13.89871	13.81746	13.69240	12.41169	16.73949	12.35393	⋯	11.47951	17.02598	16.10287	15.87972	12.44597	13.25976	12.61999	16.88355	13.62132	15.68019
A0A075B6R2	12.05290	13.34262	16.27981	16.77744	17.49744	17.05396	16.91770	19.77222	16.73705	⋯	17.99822	18.12131	19.19184	12.93506	17.28589	16.59777	17.07044	16.06459	12.56790	16.03648
A0A075B6R9;A0A0C4DH68	19.51640	19.37284	13.20493	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	13.01147	16.28653	14.65632	13.19173	14.51212	⋯	15.07423	15.91304	16.65963	15.80117	17.45378	14.10153	16.61213	16.21984	15.69582	16.38320
A0A075B6S5	13.41881	13.76385	14.26212	12.11896	12.62876	15.96016	16.15277	17.07842	16.84659	⋯	15.51315	16.14929	11.54997	15.35133	15.91161	15.65170	16.03424	13.49547	11.54106	11.96333
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	12.27493	14.65865	13.83981	15.34886	14.50111	15.12196	14.51142	15.31218	⋯	12.58340	11.66221	12.02568	13.13886	11.83793	12.10401	13.73964	13.99573	11.65649	12.55987
A0A075B7D0	18.35754	13.13026	18.23018	19.26103	19.78637	19.65551	18.51593	20.50008	19.50572	⋯	13.04697	19.23066	13.65742	18.07711	19.43528	19.04498	17.02163	18.08262	18.73767	17.31493
A0A087WSV8;V9HW75	15.67587	14.50492	13.52935	15.55301	15.52087	12.44174	15.21659	14.60517	13.85270	⋯	13.61361	15.52576	15.20934	15.53814	13.98360	15.14917	15.89044	13.03523	15.41872	14.79854
A0A087WSY4	14.25563	17.14993	15.65052	12.78220	11.75895	12.48918	11.45200	13.62120	11.93420	⋯	15.85031	12.27089	16.20781	15.33813	11.40212	15.12749	11.63482	12.78222	11.74675	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	11.46220	19.79855	18.65092	18.43422	19.15982
A0A087WSZ0;A0A0G2JQJ0	16.68463	12.73639	11.04410	13.18457	12.57095	13.03775	12.41901	15.25869	15.59578	⋯	13.94346	15.97835	14.31120	12.08372	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	12.60355	17.69916	11.56718	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	11.97153	12.46846	16.93402	16.74856	13.58981	17.17757	17.26857
A0A087WTK0;A0A087WVC6;Q12913	15.50121	15.30696	15.81471	15.55156	15.32131	16.04415	16.18863	13.42902	15.56486	⋯	13.24850	15.76029	15.21911	12.56328	11.08753	14.17279	15.39396	15.24665	14.15537	11.51414
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋱	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Q9UN70;Q9UN70-2	16.15734	15.72455	16.43182	16.23052	15.95639	13.83156	16.43843	15.63146	15.72126	⋯	12.56072	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	11.26723	17.11591	18.19042	17.97682	17.00159	17.09973	⋯	16.77195	13.40415	17.77967	17.59030	17.37821	16.45713	16.45075	17.89332	17.63618	17.39555
Q9UNW1	16.95997	16.51290	16.14734	13.30216	16.50818	15.64714	16.41814	16.48681	16.31438	⋯	12.64268	16.21768	12.22321	16.06726	12.68658	16.31734	16.21004	16.57037	16.17868	16.53072
Q9UP79	15.15310	12.02199	16.03621	15.41609	15.50277	16.33833	15.89802	14.59940	9.96168	⋯	12.33799	13.92096	15.08015	12.25921	15.26104	15.73977	13.99591	12.83617	16.10664	12.55311
Q9UPU3	18.32540	18.23025	13.39966	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	13.74537	15.62809	15.53902	15.40031	14.55045	15.08724	⋯	15.81437	15.64994	15.60246	13.69615	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	12.99953	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	12.19253	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	11.07577
Q9Y279;Q9Y279-2	17.16794	17.44556	17.36345	17.29289	17.22047	16.98754	17.17998	13.13621	17.16304	⋯	15.02893	16.54589	16.58450	16.58755	17.65963	16.61656	16.70633	12.13001	11.59368	16.95138
Q9Y281;Q9Y281-3	12.72225	12.72736	12.23188	13.49767	13.26643	12.41354	12.66758	12.73594	13.59958	⋯	12.55531	12.30069	11.20707	12.54991	12.86694	13.01033	13.07666	13.77543	12.08604	12.54090
Q9Y287	17.66289	17.58819	14.10068	17.69614	17.73541	17.03094	16.95083	18.26680	12.40274	⋯	12.97861	12.87763	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	12.30338	16.95730	18.37587	17.57333	17.93369
Q9Y2T3;Q9Y2T3-3	18.46767	17.72113	18.44012	18.86565	18.73226	18.63877	13.48391	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	13.16435	13.96193	13.05627	12.97275	13.62690	13.38121	13.74195	12.60086	12.42629	⋯	12.85175	12.74427	12.56165	12.79573	12.65074	12.93233	13.29442	12.88628	13.84086	13.52534
Q9Y4L1	18.59795	18.47622	10.88483	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	13.07256	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	12.30848	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	12.35334	12.24519
Q9Y617	16.85897	16.79927	16.28782	17.07490	14.01302	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	14.02020	15.63408	⋯	13.02647	14.95201	15.53361	15.06014	15.66794	13.85172	15.42228	15.80796	15.15740	15.23671
Q9Y696	15.17778	15.57553	14.72770	14.59034	12.89461	14.85995	16.13632	14.94928	15.26568	⋯	11.75103	15.84479	15.80784	16.16574	15.91448	15.68387	16.10633	16.09780	16.71176	15.65174
Q9Y6C2	12.60491	12.21071	13.75658	14.68157	12.18729	11.48473	12.57323	14.31805	12.19763	⋯	10.51546	11.92335	13.94349	11.90306	14.20407	14.23561	12.64189	14.40272	11.35396	15.21141
Q9Y6N6	15.05021	14.83290	15.11828	15.14018	15.25588	14.69813	13.15380	13.64435	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	12.70848	17.45319	⋯	11.06897	17.03425	16.74779	16.39020	13.52262	17.55092	17.08447	12.78977	16.53276	16.74913
Q9Y6R7	13.62010	20.29933	19.59785	19.42880	19.58198	19.13033	18.69030	18.99605	19.99318	⋯	19.23304	18.81636	18.90212	18.81282	11.56105	17.92185	18.70814	18.72478	19.41113	19.27470
Q9Y6X5	12.95360	15.55558	15.73522	12.51493	15.32828	13.23881	12.82369	13.35971	12.86682	⋯	15.89469	15.56189	16.08877	15.66911	15.01193	16.34016	12.53778	16.13827	15.80690	15.73176
Q9Y6Y8;Q9Y6Y8-2	19.56282	19.38575	20.44732	20.21596	12.70708	19.63319	20.05686	18.68044	20.02323	⋯	11.65525	19.80948	20.20436	19.41823	17.84743	19.92766	19.43260	19.59912	19.54500	19.57720
Q9Y6Y9	12.43593	13.96997	12.63641	12.76603	13.14453	11.83580	12.81741	12.89658	12.99352	⋯	12.79779	11.98187	12.70738	12.97812	12.28750	12.92941	12.93063	13.63705	12.72582	11.04203
S4R3U6	12.80521	12.44245	12.50466	12.44461	11.85610	13.92179	13.83987	10.88204	13.00793	⋯	13.25111	10.13265	12.05156	10.49769	10.56335	12.73789	12.37140	11.17371	12.61866	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	PI
	<chr>	<fct>	<dbl>
11	A0A075B6J9	Sample_000	13.41153
14	A0A075B6P5;P01615	Sample_000	13.55415
15	A0A075B6Q5	Sample_000	13.96710
16	A0A075B6R2	Sample_000	12.05290
19	A0A075B6S5	Sample_000	13.41881
24	A0A087WSY4	Sample_000	14.25563
34	A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2	Sample_000	12.09579
39	A0A087WW87;A0A087X0Q4;P01614	Sample_000	11.54184
40	A0A087WWA5	Sample_000	14.03446
42	A0A087WWT2;Q9NPD7	Sample_000	12.17343
43	A0A087WX80;P24043	Sample_000	12.20409
45	A0A087WXE9;E9PQ70;Q6UXH9;Q6UXH9-2;Q6UXH9-3	Sample_000	13.64749
48	A0A087WYK9;Q02985;Q02985-2;Q6NSD3	Sample_000	13.18781
52	A0A087WZR4	Sample_000	12.58107
53	A0A087X089;Q16627;Q16627-2	Sample_000	12.37590
56	A0A087X0M8	Sample_000	11.91548
59	A0A087X117;A0A0G2JN29;J3KN36;P69849;Q15155;Q5JPE7;Q5JPE7-2	Sample_000	13.34308
69	A0A0A0MQS9;A0A0A0MTC7;Q16363;Q16363-2	Sample_000	11.92651
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	13.22806
85	A0A0A0MT32;P38571;P38571-2	Sample_000	12.39764
87	A0A0A0MT66	Sample_000	13.70503
90	A0A0A0MTC8;Q9NQ36;Q9NQ36-2;Q9NQ36-3	Sample_000	13.50943
96	A0A0A6YY99;O43508-2	Sample_000	12.15219
101	A0A0B4J1V2	Sample_000	12.69792
102	A0A0B4J1V6	Sample_000	12.90929
105	A0A0B4J1Z1;C9JAB2;Q16629;Q16629-2;Q16629-3;Q16629-4	Sample_000	12.19075
108	A0A0B4J2B5;S4R460	Sample_000	13.90198
109	A0A0B4J2C3;P13693;Q5W0H4	Sample_000	13.78604
111	A0A0C4DFP6;Q9NQ79;Q9NQ79-2	Sample_000	13.45485
113	A0A0C4DG76;Q5JZ08;Q9BQS7;Q9BQS7-2;Q9BQS7-3;Q9BQS7-4	Sample_000	12.66149
⋮	⋮	⋮	⋮
298242	Q96PQ0	Sample_209	13.76743
298244	Q96RW7;Q96RW7-2	Sample_209	12.77663
298246	Q96SM3	Sample_209	12.74675
298249	Q99538	Sample_209	12.31367
298252	Q99650;Q99650-2	Sample_209	13.26045
298261	Q9BQT9;Q9BQT9-2	Sample_209	12.64939
298266	Q9BT88	Sample_209	11.54715
298273	Q9BX67	Sample_209	11.81845
298277	Q9BXP8	Sample_209	12.57196
298287	Q9H3T2;Q9H3T2-3	Sample_209	11.81209
298288	Q9H3T3;Q9H3T3-3	Sample_209	11.99796
298289	Q9H492;Q9H492-2	Sample_209	12.77538
298297	Q9H8L6	Sample_209	13.06893
298299	Q9HBR0	Sample_209	12.40398
298302	Q9HC38-2	Sample_209	11.65559
298303	Q9HC57	Sample_209	12.20477
298310	Q9NPH3;Q9NPH3-2;Q9NPH3-5	Sample_209	13.76125
298314	Q9NRB3	Sample_209	12.27028
298316	Q9NS00;Q9NS00-2	Sample_209	12.40202
298317	Q9NS84	Sample_209	12.46862
298328	Q9NY97;Q9NY97-2	Sample_209	13.39984
298341	Q9P1W8;Q9P1W8-2;Q9P1W8-4	Sample_209	12.53982
298349	Q9UGM5;Q9UGM5-2	Sample_209	13.37656
298355	Q9UI40;Q9UI40-2	Sample_209	11.81050
298357	Q9UIW2	Sample_209	12.70399
298358	Q9UJ14	Sample_209	13.76294
298377	Q9UMX0;Q9UMX0-2;Q9UMX0-4	Sample_209	11.62637
298384	Q9UP79	Sample_209	12.55311
298388	Q9Y240	Sample_209	11.07577
298398	Q9Y5Y7	Sample_209	12.24519

Check dimension of long format dataframe

71601
3

Save predictions to disk

Warning message:
“The `path` argument of `write_csv()` is deprecated as of readr 1.4.0.
ℹ Please use the `file` argument instead.”

'runs/alzheimer_study/preds/pred_all_PI.csv'

NAGuide R methods

Contents

NAGuide R methods#

Parameters#

Dump predictions#