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 = "MLE"
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

Iterations of EM: 
1...2...

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.
Warning message in norm::prelim.norm(xxm):
“NAs introduced by coercion to integer range”

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	15.49990	16.11149	16.10697	15.60321	15.81161	15.49966	15.22075	15.98013	⋯	21.79484	15.86264	15.65594	15.40126	16.35012	15.68225	15.79804	15.73949	15.47682	16.77878
A0A024R0T9;K7ER74;P02655	16.85194	16.87369	2448.50299	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	2466.87026	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	⋯	15.72827	15.93179	14.85871	15.12451	13.36253	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	18.50171	18.17480	15.96322	2442.09704	18.31720	2508.99869	17.21393	17.79403	⋯	22.64528	16.48262	14.83467	13.56893	14.48257	2434.52179	2454.50832	2466.87026	2468.91932	12.77627
A0A075B6H9	20.24646	19.94110	19.25090	19.62797	20.44983	18.68569	20.34722	20.79604	18.99523	⋯	26.52955	2545.15500	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	2468.91932	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	2434.52179	16.50267	16.32073	2468.91932	16.37054
A0A075B6I6	16.98778	16.98320	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	2505.22635	16.18799	2448.50299	13.43813	14.49484	15.90875	16.18436	2714.83693	16.17019	⋯	17.57673	2545.15500	16.63193	17.43574	19.77811	16.06423	2454.50832	17.58044	2468.91932	16.53239
A0A075B6K4	16.14850	16.12727	15.38672	16.56502	16.41771	16.45498	2508.99869	17.04677	16.03210	⋯	18.58162	18.51737	16.48354	16.06338	17.19123	15.35033	16.58173	2466.87026	2468.91932	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	2505.22635	15.88718	2448.50299	16.98965	17.49330	18.26923	17.78989	18.15334	17.78703	⋯	19.92434	2545.15500	2480.85687	19.84136	19.26620	17.17528	19.57506	19.30588	18.97651	18.68810
A0A075B6Q5	2505.22635	18.98019	15.12836	2468.89077	2442.09704	2495.81782	2508.99869	16.73949	2555.08130	⋯	17.17138	17.02598	16.10287	15.87972	14.21727	2434.52179	2454.50832	16.88355	2468.91932	15.68019
A0A075B6R2	2505.22635	17.24170	16.27981	16.77744	17.49744	17.05396	16.91770	19.77222	16.73705	⋯	17.99822	18.12131	19.19184	2511.80507	17.28589	16.59777	17.07044	16.06459	2468.91932	16.03648
A0A075B6R9;A0A0C4DH68	19.51640	19.37284	2448.50299	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	2442.09704	16.28653	14.65632	2714.83693	14.51212	⋯	15.07423	15.91304	16.65963	15.80117	17.45378	14.10153	16.61213	16.21984	15.69582	16.38320
A0A075B6S5	2505.22635	17.12660	2448.50299	2468.89077	2442.09704	15.96016	16.15277	17.07842	16.84659	⋯	15.51315	16.14929	2480.85687	15.35133	15.91161	15.65170	16.03424	2466.87026	2468.91932	14.57619
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	13.01654	14.65865	2468.89077	15.34886	14.50111	15.12196	14.51142	15.31218	⋯	14.58959	2545.15500	2480.85687	2511.80507	16.36240	2434.52179	2454.50832	2466.87026	2468.91932	17.44275
A0A075B7D0	18.35754	17.14157	18.23018	19.26103	19.78637	19.65551	18.51593	20.50008	19.50572	⋯	14.25291	19.23066	2480.85687	18.07711	19.43528	19.04498	17.02163	18.08262	18.73767	17.31493
A0A087WSV8;V9HW75	15.67587	14.50492	2448.50299	15.55301	15.52087	2495.81782	15.21659	2714.83693	13.85270	⋯	16.49812	15.52576	15.20934	15.53814	13.98360	15.14917	15.89044	2466.87026	15.41872	14.79854
A0A087WSY4	2505.22635	17.14993	15.65052	2468.89077	2442.09704	2495.81782	2508.99869	2714.83693	2555.08130	⋯	15.85031	2545.15500	16.20781	15.33813	18.35346	15.12749	2454.50832	2466.87026	2468.91932	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	2434.52179	19.79855	18.65092	18.43422	19.15982
A0A087WSZ0;A0A0G2JQJ0	16.68463	18.60493	2448.50299	2468.89077	2442.09704	13.03775	2508.99869	15.25869	15.59578	⋯	16.06485	15.97835	14.31120	2511.80507	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	2480.85687	17.69916	16.74664	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	2511.80507	16.85046	16.93402	16.74856	2466.87026	17.17757	17.26857
A0A087WTK0;A0A087WVC6;Q12913	15.50121	15.30696	15.81471	15.55156	15.32131	16.04415	16.18863	2714.83693	15.56486	⋯	17.67999	15.76029	15.21911	2511.80507	17.04500	2434.52179	15.39396	15.24665	2468.91932	15.21836
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋱	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Q9UN70;Q9UN70-2	16.15734	15.72455	16.43182	16.23052	15.95639	2495.81782	16.43843	15.63146	15.72126	⋯	22.44797	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	2468.89077	17.11591	18.19042	17.97682	17.00159	17.09973	⋯	16.77195	2545.15500	17.77967	17.59030	17.37821	16.45713	16.45075	17.89332	17.63618	17.39555
Q9UNW1	16.95997	16.51290	16.14734	2468.89077	16.50818	15.64714	16.41814	16.48681	16.31438	⋯	14.77712	16.21768	2480.85687	16.06726	15.66282	16.31734	16.21004	16.57037	16.17868	16.53072
Q9UP79	15.15310	16.86541	16.03621	15.41609	15.50277	16.33833	15.89802	14.59940	2555.08130	⋯	20.46396	2545.15500	15.08015	2511.80507	15.26104	15.73977	2454.50832	2466.87026	16.10664	18.12435
Q9UPU3	18.32540	18.23025	2448.50299	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	2468.89077	15.62809	15.53902	15.40031	14.55045	15.08724	⋯	15.81437	15.64994	15.60246	2511.80507	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	2714.83693	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	2442.09704	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	17.32832
Q9Y279;Q9Y279-2	17.16794	17.44556	17.36345	17.29289	17.22047	16.98754	17.17998	2714.83693	17.16304	⋯	15.02893	16.54589	16.58450	16.58755	17.65963	16.61656	16.70633	2466.87026	2468.91932	16.95138
Q9Y281;Q9Y281-3	2505.22635	17.46077	2448.50299	2468.89077	2442.09704	2495.81782	2508.99869	2714.83693	2555.08130	⋯	11.47444	12.30069	2480.85687	2511.80507	18.21637	13.01033	13.07666	2466.87026	2468.91932	12.54090
Q9Y287	17.66289	17.58819	2448.50299	17.69614	17.73541	17.03094	16.95083	18.26680	2555.08130	⋯	16.73068	2545.15500	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	2434.52179	16.95730	18.37587	17.57333	17.93369
Q9Y2T3;Q9Y2T3-3	18.46767	17.72113	18.44012	18.86565	18.73226	18.63877	2508.99869	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	2505.22635	13.96193	2448.50299	2468.89077	13.62690	13.38121	13.74195	2714.83693	2555.08130	⋯	13.04149	2545.15500	2480.85687	2511.80507	18.02870	12.93233	13.29442	2466.87026	13.84086	13.52534
Q9Y4L1	18.59795	18.47622	2448.50299	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	2448.50299	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	2480.85687	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	2468.91932	18.37891
Q9Y617	16.85897	16.79927	16.28782	17.07490	2442.09704	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	2714.83693	15.63408	⋯	17.49762	14.95201	15.53361	15.06014	15.66794	2434.52179	15.42228	15.80796	15.15740	15.23671
Q9Y696	15.17778	15.57553	14.72770	14.59034	2442.09704	14.85995	16.13632	14.94928	15.26568	⋯	19.40806	15.84479	15.80784	16.16574	15.91448	15.68387	16.10633	16.09780	16.71176	15.65174
Q9Y6C2	2505.22635	16.74583	13.75658	14.68157	2442.09704	2495.81782	2508.99869	14.31805	2555.08130	⋯	19.22806	2545.15500	2480.85687	2511.80507	14.20407	14.23561	2454.50832	14.40272	2468.91932	15.21141
Q9Y6N6	15.05021	14.83290	15.11828	15.14018	15.25588	14.69813	2508.99869	2714.83693	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	2714.83693	17.45319	⋯	18.13057	17.03425	16.74779	16.39020	16.07370	17.55092	17.08447	2466.87026	16.53276	16.74913
Q9Y6R7	2505.22635	20.29933	19.59785	19.42880	19.58198	19.13033	18.69030	18.99605	19.99318	⋯	19.23304	18.81636	18.90212	18.81282	12.42800	17.92185	18.70814	18.72478	19.41113	19.27470
Q9Y6X5	2505.22635	15.55558	15.73522	2468.89077	15.32828	2495.81782	2508.99869	2714.83693	2555.08130	⋯	15.89469	15.56189	16.08877	15.66911	15.01193	16.34016	2454.50832	16.13827	15.80690	15.73176
Q9Y6Y8;Q9Y6Y8-2	19.56282	19.38575	20.44732	20.21596	2442.09704	19.63319	20.05686	18.68044	20.02323	⋯	19.43061	19.80948	20.20436	19.41823	17.84743	19.92766	19.43260	19.59912	19.54500	19.57720
Q9Y6Y9	2505.22635	13.96997	12.63641	2468.89077	13.14453	2495.81782	12.81741	12.89658	2555.08130	⋯	17.16109	2545.15500	12.70738	12.97812	12.28750	12.92941	2454.50832	13.63705	2468.91932	11.04203
S4R3U6	12.80521	12.44245	12.50466	12.44461	2442.09704	2495.81782	2508.99869	2714.83693	13.00793	⋯	17.70124	10.13265	2480.85687	10.49769	10.56335	2434.52179	2454.50832	11.17371	2468.91932	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	MLE
	<chr>	<fct>	<dbl>
11	A0A075B6J9	Sample_000	2505.226
14	A0A075B6P5;P01615	Sample_000	2505.226
15	A0A075B6Q5	Sample_000	2505.226
16	A0A075B6R2	Sample_000	2505.226
19	A0A075B6S5	Sample_000	2505.226
24	A0A087WSY4	Sample_000	2505.226
34	A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2	Sample_000	2505.226
39	A0A087WW87;A0A087X0Q4;P01614	Sample_000	2505.226
40	A0A087WWA5	Sample_000	2505.226
42	A0A087WWT2;Q9NPD7	Sample_000	2505.226
43	A0A087WX80;P24043	Sample_000	2505.226
45	A0A087WXE9;E9PQ70;Q6UXH9;Q6UXH9-2;Q6UXH9-3	Sample_000	2505.226
48	A0A087WYK9;Q02985;Q02985-2;Q6NSD3	Sample_000	2505.226
52	A0A087WZR4	Sample_000	2505.226
53	A0A087X089;Q16627;Q16627-2	Sample_000	2505.226
56	A0A087X0M8	Sample_000	2505.226
59	A0A087X117;A0A0G2JN29;J3KN36;P69849;Q15155;Q5JPE7;Q5JPE7-2	Sample_000	2505.226
69	A0A0A0MQS9;A0A0A0MTC7;Q16363;Q16363-2	Sample_000	2505.226
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	2505.226
85	A0A0A0MT32;P38571;P38571-2	Sample_000	2505.226
87	A0A0A0MT66	Sample_000	2505.226
90	A0A0A0MTC8;Q9NQ36;Q9NQ36-2;Q9NQ36-3	Sample_000	2505.226
96	A0A0A6YY99;O43508-2	Sample_000	2505.226
101	A0A0B4J1V2	Sample_000	2505.226
102	A0A0B4J1V6	Sample_000	2505.226
105	A0A0B4J1Z1;C9JAB2;Q16629;Q16629-2;Q16629-3;Q16629-4	Sample_000	2505.226
108	A0A0B4J2B5;S4R460	Sample_000	2505.226
109	A0A0B4J2C3;P13693;Q5W0H4	Sample_000	2505.226
111	A0A0C4DFP6;Q9NQ79;Q9NQ79-2	Sample_000	2505.226
113	A0A0C4DG76;Q5JZ08;Q9BQS7;Q9BQS7-2;Q9BQS7-3;Q9BQS7-4	Sample_000	2505.226
⋮	⋮	⋮	⋮
298242	Q96PQ0	Sample_209	11.72617
298244	Q96RW7;Q96RW7-2	Sample_209	21.51925
298246	Q96SM3	Sample_209	16.05074
298249	Q99538	Sample_209	15.77972
298252	Q99650;Q99650-2	Sample_209	18.56947
298261	Q9BQT9;Q9BQT9-2	Sample_209	16.33259
298266	Q9BT88	Sample_209	22.67178
298273	Q9BX67	Sample_209	18.45231
298277	Q9BXP8	Sample_209	12.11186
298287	Q9H3T2;Q9H3T2-3	Sample_209	19.93308
298288	Q9H3T3;Q9H3T3-3	Sample_209	19.87412
298289	Q9H492;Q9H492-2	Sample_209	19.07592
298297	Q9H8L6	Sample_209	13.31142
298299	Q9HBR0	Sample_209	15.08597
298302	Q9HC38-2	Sample_209	21.23231
298303	Q9HC57	Sample_209	19.64863
298310	Q9NPH3;Q9NPH3-2;Q9NPH3-5	Sample_209	16.12520
298314	Q9NRB3	Sample_209	17.35880
298316	Q9NS00;Q9NS00-2	Sample_209	18.61479
298317	Q9NS84	Sample_209	18.63378
298328	Q9NY97;Q9NY97-2	Sample_209	16.54474
298341	Q9P1W8;Q9P1W8-2;Q9P1W8-4	Sample_209	11.23278
298349	Q9UGM5;Q9UGM5-2	Sample_209	13.60756
298355	Q9UI40;Q9UI40-2	Sample_209	18.03998
298357	Q9UIW2	Sample_209	20.47834
298358	Q9UJ14	Sample_209	17.15138
298377	Q9UMX0;Q9UMX0-2;Q9UMX0-4	Sample_209	22.33701
298384	Q9UP79	Sample_209	18.12435
298388	Q9Y240	Sample_209	17.32832
298398	Q9Y5Y7	Sample_209	18.37891

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_MLE.csv'

NAGuide R methods

Contents

NAGuide R methods#

Parameters#

Dump predictions#