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 = "IMPSEQROB"
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

Not enough complete observations: only  0 complete, needed atleast  234 ...

Trying to impute the minimum necessary complete observations using the multivariate normal model ...

Loading required package: rrcovNA
Loading required package: rrcov
Loading required package: robustbase
Scalable Robust Estimators with High Breakdown Point (version 1.7-7)
Scalable Robust Estimators with High Breakdown Point for
Incomplete Data (version 0.5-3)
Warning message in prelim.norm(x):
“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	15.912372	24.015395	16.111492	16.1069686	15.603214	15.81161	15.499663	15.22075	15.98013	⋯	93.012460	15.86264	15.65594	15.4012577	87.83193	15.682253	15.798040	15.7394864	15.476823	13.483870
A0A024R0T9;K7ER74;P02655	16.851936	16.873692	2448.502994	17.0315136	15.330507	18.61407	17.409166	17.68391	16.38625	⋯	17.309554	16.61480	17.95339	18.1993788	17.27911	16.886350	17.554003	2466.8702636	16.779484	17.261372
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	15.570475	15.518923	15.935318	15.8018702	15.375223	15.62420	15.911849	15.38499	15.89447	⋯	20.950428	15.93179	14.85871	15.1245137	73.71449	14.910061	15.599656	15.4689589	14.994959	15.174868
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	16.481080	16.386944	16.415769	16.9786215	16.679056	15.95764	16.233887	16.41793	16.27134	⋯	15.954024	16.02959	16.49753	16.5128352	16.51269	16.482320	15.937820	16.8977338	16.132456	16.235049
A0A075B6H7	17.301384	3.183314	18.174802	15.9632183	-44.254803	18.31720	19.866970	17.21393	17.79403	⋯	37.864865	16.48262	14.83467	13.5689315	14.48257	11.887785	17.225955	0.0870051	34.967631	39.956443
A0A075B6H9	20.246458	19.941099	19.250897	19.6279665	20.449826	18.68569	20.347225	20.79604	18.99523	⋯	103.654249	119.42909	18.26986	19.8006194	20.18315	17.704854	18.154495	18.6363064	14.907540	17.893438
A0A075B6I0	16.764464	18.786238	16.832174	17.8520920	18.681846	17.86543	18.249949	20.30936	17.97843	⋯	18.005504	18.54142	17.80748	19.8986549	19.67420	17.039452	18.151788	17.9503970	133.864811	17.744434
A0A075B6I1	17.583571	17.143882	15.670730	18.8774933	17.081308	14.82369	18.519687	19.35640	18.56449	⋯	16.414016	17.50510	14.01911	17.5057385	20.25111	-2.975943	16.502674	16.3207298	-168.440817	16.370537
A0A075B6I6	16.987778	29.228599	17.011825	14.1815331	14.140209	17.04179	17.375604	16.99794	17.20311	⋯	15.725431	17.06758	16.45849	16.3277774	16.33372	16.413036	16.860030	16.4007101	16.118675	15.779584
A0A075B6I9	20.054163	19.067330	18.569057	18.9850649	19.685643	19.37580	19.398847	20.54359	18.84397	⋯	21.852634	20.14650	19.53459	19.8671301	20.76626	19.102113	18.537958	18.8489675	18.367762	18.805807
A0A075B6J9	22.549259	16.187994	-26.399943	13.4381272	14.494838	15.90875	16.184364	-29.81738	16.17019	⋯	81.643058	65.86547	16.63193	17.4357361	19.77811	16.064228	-5.225605	17.5804429	48.972347	16.532394
A0A075B6K4	16.148495	16.127274	15.386717	16.5650213	16.417706	16.45498	4.878698	17.04677	16.03210	⋯	18.581617	18.51737	16.48354	16.0633821	17.19123	15.350327	16.581730	155.4939261	20.519220	16.337871
A0A075B6K5	17.342608	17.417224	17.236328	16.2671697	17.390204	18.33604	17.217651	18.32488	17.52458	⋯	19.515657	18.98906	17.80614	16.7481938	18.41254	17.153839	17.901842	17.5189909	17.502204	16.988744
A0A075B6P5;P01615	229.376220	172.088294	-77.120892	16.9896457	17.493302	18.26923	17.789886	18.15334	17.78703	⋯	19.924336	285.46723	42.78669	19.8413613	19.26620	17.175275	19.575064	19.3058775	18.976508	18.688095
A0A075B6Q5	9.648014	22.323836	15.128364	26.6896725	11.441860	29.89393	19.569066	16.73949	16.85870	⋯	28.683906	17.02598	16.10287	15.8797234	35.65169	22.349601	17.038776	16.8835495	40.891862	15.680192
A0A075B6R2	31.885333	24.438378	16.279812	16.7774424	17.497440	17.05396	16.917702	19.77222	16.73705	⋯	17.998216	18.12131	19.19184	19.8244173	17.28589	16.597767	17.070441	16.0645948	-17.601571	16.036479
A0A075B6R9;A0A0C4DH68	19.516404	19.372845	2448.502994	19.0092301	19.541470	19.56719	20.185071	20.56454	19.52324	⋯	20.127490	20.37360	19.49983	18.9773918	20.58832	18.073367	18.846728	19.6205356	19.339257	20.392558
A0A075B6S2	15.511237	16.208238	14.845699	15.2324214	-71.133180	16.28653	14.656319	-32.51624	14.51212	⋯	15.074225	15.91304	16.65963	15.8011735	17.45378	14.101533	16.612134	16.2198399	15.695820	16.383202
A0A075B6S5	4.910113	46.853612	-7.172031	-30.1981658	-19.921572	15.96016	16.152768	17.07842	16.84659	⋯	15.513146	16.14929	21.76293	15.3513346	15.91161	15.651702	16.034236	-18.5303439	8.505286	14.044892
A0A075B6S9	16.557646	17.515965	17.939992	16.9514660	17.087950	17.36099	16.421033	19.79080	17.13399	⋯	17.259580	20.50218	19.26507	18.4246797	18.19725	16.145628	19.503731	18.4250135	17.751901	16.219953
A0A075B7B8	14.915938	18.638071	14.658653	-0.2287049	15.348856	14.50111	15.121960	14.51142	15.31218	⋯	62.690113	88.37904	22.48674	0.9953016	36.82646	53.801376	16.180251	17.4702564	36.192677	2.737746
A0A075B7D0	18.357541	2489.135535	18.230181	19.2610263	19.786373	19.65551	18.515926	20.50008	19.50572	⋯	2672.832006	19.23066	2480.85687	18.0771127	19.43528	19.044976	17.021626	18.0826243	18.737668	17.314927
A0A087WSV8;V9HW75	15.675870	14.504916	17.115920	15.5530121	15.520870	12.88733	15.216594	57.29870	13.85270	⋯	51.570104	15.52576	15.20934	15.5381403	13.98360	15.149174	15.890440	-9.4856206	15.418721	14.798540
A0A087WSY4	32.059051	17.149932	15.650525	26.5739303	-2.727062	17.23574	3.424836	16.77903	13.35640	⋯	15.850311	40.24557	16.20781	15.3381303	35.21665	15.127494	4.927357	23.2291888	14.115095	20.343378
A0A087WSY5;Q96IY4	17.863227	18.041304	17.776680	18.3417780	18.428885	17.85131	17.689533	19.12984	18.01868	⋯	17.861174	17.12731	17.28784	18.3573356	18.09213	17.114959	17.195406	17.9442620	18.084779	17.347361
A0A087WSY6	20.116861	20.274504	19.280797	19.7818950	19.726581	20.79580	19.632388	20.59759	19.96610	⋯	21.281549	19.51359	19.24474	19.7366264	19.37543	2434.521786	19.798549	18.6509177	18.434218	19.159821
A0A087WSZ0;A0A0G2JQJ0	16.684627	35.360591	5.130564	49.1357335	50.828725	13.03775	17.214073	15.25869	15.59578	⋯	41.891149	15.97835	14.31120	7.1674737	17.32747	14.889720	15.636581	13.7425927	16.476517	13.667954
A0A087WTA1;A0A0A0MR20;E9PDN6;F5H107;Q9C0A0;Q9C0A0-2	17.593775	18.012397	17.829210	18.2684969	17.557059	17.38051	17.310825	16.40859	17.37506	⋯	16.942487	17.74655	2480.85687	17.6991610	2710.74936	18.079999	17.480566	17.8153519	18.186245	17.919028
A0A087WTA8;P08123	17.619078	18.216848	17.544449	17.5022512	17.802884	16.99184	17.977386	17.70994	17.53213	⋯	17.562187	16.57279	16.90066	-39.4956184	57.14146	16.934015	16.748555	-0.3245493	17.177573	17.268568
A0A087WTK0;A0A087WVC6;Q12913	15.501210	15.306962	15.814712	15.5515585	15.321311	16.04415	16.188633	53.48032	15.56486	⋯	3.715981	15.76029	15.21911	4.1275474	42.47212	15.332895	15.393958	15.2466522	36.702969	45.579004
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋱	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
Q9UN70;Q9UN70-2	16.15734	15.72455	16.4318189	16.23052	15.95639	42.876641	16.438433	15.631459	15.721263	⋯	52.63689	15.68391	15.4991290	15.985846	15.02979	15.48571	15.527984	16.08012	15.631650	15.77433
Q9UNN8	17.56056	17.30159	17.7646351	36.57193	17.11591	18.190423	17.976816	17.001593	17.099726	⋯	16.77195	96.22036	17.7796736	17.590300	17.37821	16.45713	16.450753	17.89332	17.636178	17.39555
Q9UNW1	16.95997	16.51290	16.1473424	56.74216	16.50818	15.647136	16.418141	16.486807	16.314377	⋯	76.84278	16.21768	47.8195933	16.067259	89.64230	16.31734	16.210038	16.57037	16.178681	16.53072
Q9UP79	15.15310	24.78908	16.0362115	15.41609	15.50277	16.338332	15.898021	14.599405	14.237242	⋯	43.60646	28.14658	15.0801489	13.563055	15.26104	15.73977	2.566064	-10.64512	16.106640	13.71919
Q9UPU3	18.32540	18.23025	-50.1541225	18.10040	18.17738	18.462883	17.900758	17.751209	18.743208	⋯	16.78448	18.47047	19.0300505	18.648557	18.11089	18.42196	18.369369	17.64059	17.242103	18.27864
Q9UQ52	15.34901	15.54989	15.7118664	19.12104	15.62809	15.539016	15.400309	14.550448	15.087244	⋯	15.81437	15.64994	15.6024594	3.061395	15.04666	15.71637	16.123303	15.68571	15.834854	16.18904
Q9UQM7	16.77983	16.63557	16.9280038	17.36468	17.26139	16.572919	16.341255	2714.836933	17.185289	⋯	15.44938	17.13915	17.4659776	15.954462	16.01651	17.90825	16.223256	17.08973	16.388449	17.38407
Q9Y240	16.25700	16.27154	16.5777963	17.13331	10.86837	16.761094	16.908104	16.879802	15.263930	⋯	16.74831	16.53842	16.9056711	16.723459	16.61396	16.71442	16.255719	16.50802	17.286111	18.31175
Q9Y279;Q9Y279-2	17.16794	17.44556	17.3634454	17.29289	17.22047	16.987538	17.179980	90.057178	17.163038	⋯	15.02893	16.54589	16.5845030	16.587548	17.65963	16.61656	16.706326	-133.89269	150.184178	16.95138
Q9Y281;Q9Y281-3	21.23690	16.93783	21.8644157	21.77716	15.18167	23.637466	22.757632	49.683337	26.550558	⋯	34.65150	12.30069	22.4071481	13.704601	33.83110	13.01033	13.076657	19.94714	28.328849	12.54090
Q9Y287	17.66289	17.58819	0.6895076	17.69614	17.73541	17.030942	16.950826	18.266799	2.304339	⋯	19.05278	22.10505	16.5786558	17.970738	17.10728	18.27608	16.477157	16.54354	16.173777	17.82544
Q9Y2I2;Q9Y2I2-1;Q9Y2I2-2;Q9Y2I2-4;Q9Y2I2-5;Q9Y2I2-6	16.92776	17.22074	17.4259506	17.29861	17.82816	16.741809	16.720691	15.904992	16.396113	⋯	16.22026	16.70635	17.8930432	17.706591	17.09718	2434.52179	16.957301	18.37587	17.573326	17.93369
Q9Y2T3;Q9Y2T3-3	18.46767	17.72113	18.4401185	18.86565	18.73226	18.638770	2508.998686	17.115567	19.332031	⋯	16.52746	18.16354	17.6509248	17.520264	17.41693	18.62477	18.261075	18.51419	18.006500	17.90217
Q9Y490	28.93782	13.96193	14.7149449	18.64180	13.62690	13.381209	13.741950	37.074735	35.876376	⋯	41.52903	23.28777	20.2872241	20.596232	32.89036	12.93233	13.294424	7.53955	13.840863	13.52534
Q9Y4L1	18.59795	18.47622	2448.5029943	18.55964	18.30465	18.523787	18.360212	17.874711	18.672576	⋯	17.18673	18.41406	18.2670951	18.085571	17.59792	18.28991	17.977196	18.14875	17.880553	18.12515
Q9Y5F6;Q9Y5F6-2	16.46929	15.78240	24.4834325	16.52899	16.28480	16.863440	15.899533	16.109523	17.401321	⋯	15.45926	16.42884	16.1594997	15.631557	14.73569	15.96821	16.884808	15.87772	15.554235	16.57524
Q9Y5I4;Q9Y5I4-2	17.18701	17.44717	17.4104277	17.54514	17.29667	17.397283	17.145318	16.404825	17.323081	⋯	15.89731	17.13489	2480.8568685	17.085883	15.87386	17.10354	17.109020	16.93840	17.155076	16.77629
Q9Y5Y7	18.83957	19.19495	19.0881985	18.71497	18.66824	19.051331	19.087113	18.802445	18.714393	⋯	17.13452	17.83727	18.7856233	18.613673	18.73747	18.72639	18.460107	19.50151	30.374074	52.68642
Q9Y617	16.85897	16.79927	16.2878203	17.07490	51.37172	16.499523	16.702643	15.923976	16.637746	⋯	14.58263	14.93988	15.8696992	15.711766	15.46507	15.80831	15.035290	16.28306	15.919601	15.71292
Q9Y646	19.32196	19.19029	19.7019442	19.76013	19.62369	19.286283	19.622194	19.016436	19.150121	⋯	19.86156	20.24484	19.2073057	20.302813	19.43507	19.89388	20.014574	20.30614	20.203423	20.04164
Q9Y653;Q9Y653-2;Q9Y653-3	16.01243	15.52794	15.2287063	15.49484	14.75668	15.085631	15.788897	78.231815	15.634082	⋯	68.89886	14.95201	15.5336055	15.060137	15.66794	68.23269	15.422277	15.80796	15.157397	15.23671
Q9Y696	15.17778	15.57553	14.7276985	14.59034	-19.09627	14.859949	16.136319	14.949276	15.265679	⋯	40.15599	15.84479	15.8078362	16.165739	15.91448	15.68387	16.106326	16.09780	16.711761	15.65174
Q9Y6C2	23.18153	-36.37856	13.7565848	14.68157	-11.29280	15.494045	-17.867060	14.318050	19.142746	⋯	59.49093	62.70575	0.4013806	21.884468	14.20407	14.23561	29.460820	14.40272	-5.132213	15.21141
Q9Y6N6	15.05021	14.83290	15.1182754	15.14018	15.25588	14.698128	-64.641719	41.728209	15.292710	⋯	14.74282	15.12482	15.2456815	15.285017	15.02461	15.41504	15.345359	15.71532	14.639834	14.20491
Q9Y6N7;Q9Y6N7-2;Q9Y6N7-4	16.84221	16.59739	17.4395179	17.35608	17.07495	17.088025	16.717351	129.418157	17.453192	⋯	67.27959	17.03425	16.7477864	16.390199	68.87326	17.55092	17.084469	-66.46037	16.532757	16.74913
Q9Y6R7	-21.35931	20.29933	19.5978521	19.42880	19.58198	19.130331	18.690297	18.996045	19.993178	⋯	19.23304	18.81636	18.9021220	18.812816	73.46057	17.92185	18.708140	18.72478	19.411133	19.27470
Q9Y6X5	-43.93008	15.55558	15.7352198	61.90527	15.32828	7.054381	-79.063847	9.778554	50.256424	⋯	15.89469	15.56189	16.0887740	15.669115	15.01193	16.34016	4.618698	16.13827	15.806898	15.73176
Q9Y6Y8;Q9Y6Y8-2	19.56282	19.38575	20.4473226	20.21596	2442.09704	19.633192	20.056856	18.680442	20.023229	⋯	2672.83201	19.80948	20.2043571	19.418228	17.84743	19.92766	19.432601	19.59912	19.545001	19.57720
Q9Y6Y9	37.79734	13.96997	12.6364106	26.74010	13.14453	33.903539	12.817411	12.896584	21.678273	⋯	54.37252	26.25692	12.7073758	12.978118	12.28750	12.92941	5.775096	13.63705	-11.236355	11.04203
S4R3U6	12.80521	12.44245	12.5046586	12.44461	-16.72950	19.738981	-9.048466	8.080544	13.007934	⋯	21.48114	10.13265	17.6652455	10.497688	10.56335	-19.39250	1.130181	11.17371	-20.300880	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	IMPSEQROB
	<chr>	<fct>	<dbl>
11	A0A075B6J9	Sample_000	22.549259
14	A0A075B6P5;P01615	Sample_000	229.376220
15	A0A075B6Q5	Sample_000	9.648014
16	A0A075B6R2	Sample_000	31.885333
19	A0A075B6S5	Sample_000	4.910113
24	A0A087WSY4	Sample_000	32.059051
34	A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2	Sample_000	10.861618
39	A0A087WW87;A0A087X0Q4;P01614	Sample_000	20.570299
40	A0A087WWA5	Sample_000	17.307890
42	A0A087WWT2;Q9NPD7	Sample_000	-38.300407
43	A0A087WX80;P24043	Sample_000	33.352281
45	A0A087WXE9;E9PQ70;Q6UXH9;Q6UXH9-2;Q6UXH9-3	Sample_000	21.643285
48	A0A087WYK9;Q02985;Q02985-2;Q6NSD3	Sample_000	2.023343
52	A0A087WZR4	Sample_000	-5.400181
53	A0A087X089;Q16627;Q16627-2	Sample_000	-20.318992
56	A0A087X0M8	Sample_000	1.937164
59	A0A087X117;A0A0G2JN29;J3KN36;P69849;Q15155;Q5JPE7;Q5JPE7-2	Sample_000	8.335643
69	A0A0A0MQS9;A0A0A0MTC7;Q16363;Q16363-2	Sample_000	10.595814
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	-26.924722
85	A0A0A0MT32;P38571;P38571-2	Sample_000	71.923995
87	A0A0A0MT66	Sample_000	91.479196
90	A0A0A0MTC8;Q9NQ36;Q9NQ36-2;Q9NQ36-3	Sample_000	28.409840
96	A0A0A6YY99;O43508-2	Sample_000	28.510031
101	A0A0B4J1V2	Sample_000	26.206911
102	A0A0B4J1V6	Sample_000	4.900139
105	A0A0B4J1Z1;C9JAB2;Q16629;Q16629-2;Q16629-3;Q16629-4	Sample_000	12.212937
108	A0A0B4J2B5;S4R460	Sample_000	-10.897600
109	A0A0B4J2C3;P13693;Q5W0H4	Sample_000	45.462745
111	A0A0C4DFP6;Q9NQ79;Q9NQ79-2	Sample_000	-115.758848
113	A0A0C4DG76;Q5JZ08;Q9BQS7;Q9BQS7-2;Q9BQS7-3;Q9BQS7-4	Sample_000	94.752310
⋮	⋮	⋮	⋮
298242	Q96PQ0	Sample_209	-73.349415
298244	Q96RW7;Q96RW7-2	Sample_209	33.539427
298246	Q96SM3	Sample_209	-7.173576
298249	Q99538	Sample_209	24.773795
298252	Q99650;Q99650-2	Sample_209	39.513398
298261	Q9BQT9;Q9BQT9-2	Sample_209	-54.870119
298266	Q9BT88	Sample_209	8.270931
298273	Q9BX67	Sample_209	-10.500592
298277	Q9BXP8	Sample_209	17.917862
298287	Q9H3T2;Q9H3T2-3	Sample_209	16.372845
298288	Q9H3T3;Q9H3T3-3	Sample_209	24.096379
298289	Q9H492;Q9H492-2	Sample_209	14.712643
298297	Q9H8L6	Sample_209	2510.014645
298299	Q9HBR0	Sample_209	16.024154
298302	Q9HC38-2	Sample_209	41.176813
298303	Q9HC57	Sample_209	21.444893
298310	Q9NPH3;Q9NPH3-2;Q9NPH3-5	Sample_209	35.577844
298314	Q9NRB3	Sample_209	-31.586193
298316	Q9NS00;Q9NS00-2	Sample_209	23.643607
298317	Q9NS84	Sample_209	37.610320
298328	Q9NY97;Q9NY97-2	Sample_209	-96.801171
298341	Q9P1W8;Q9P1W8-2;Q9P1W8-4	Sample_209	18.546408
298349	Q9UGM5;Q9UGM5-2	Sample_209	82.601063
298355	Q9UI40;Q9UI40-2	Sample_209	45.122091
298357	Q9UIW2	Sample_209	-30.940084
298358	Q9UJ14	Sample_209	-1.291112
298377	Q9UMX0;Q9UMX0-2;Q9UMX0-4	Sample_209	4.240866
298384	Q9UP79	Sample_209	13.719190
298388	Q9Y240	Sample_209	18.311750
298398	Q9Y5Y7	Sample_209	52.686424

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_IMPSEQROB.csv'

NAGuide R methods

Contents

NAGuide R methods#

Parameters#

Dump predictions#