Imputation using MOFA for 2021 Dataset removed 20 samples, Batch corrected

Load libraries

suppressPackageStartupMessages({library(MOFA2)
library(kableExtra)
library(data.table)
library(ggplot2)
library(tidyverse)
library(here)})

Load data and Generate Mofa object

set.seed(1)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)

imputationTestingList <- list()

add_cols <- function(df, cols) {
  add <- cols[!cols %in% names(df)]
  if(length(add) != 0) df[add] <- NA
  return(as.matrix(df[, sort(cols)]))
}

df_source<- readRDS(here("./data/master_normalized_data_challenge2_train_Aug25.RDS"))

metaDf <- df_source[["subject_specimen"]]
metaDf["age_at_boost"] <- as.numeric(round(difftime(metaDf$date_of_boost, metaDf$year_of_birth,units="weeks")/52, 2))

normalizedData <- list()

normalizedData[["abtiter"]] <- as.data.frame(df_source[["abtiter_wide"]]$batchCorrected_data)
normalizedData[["cytof"]] <- as.data.frame(df_source[["pbmc_cell_frequency"]]$batchCorrected_data)
normalizedData[["rnaseq"]] <- as.data.frame(df_source[["pbmc_gene_expression"]]$batchCorrected_data)
normalizedData[["olink"]] <- as.data.frame(df_source[["plasma_cytokine_concentrations"]]$batchCorrected_data)

int_cols <- Reduce(intersect, lapply(normalizedData[c("abtiter", "cytof", "olink")], colnames))
cols <- unique(c(int_cols, colnames(normalizedData[["rnaseq"]])))

dataset_cols <- metaDf[metaDf$dataset=="2021_dataset", ]$specimen_id
cols <- intersect(cols, dataset_cols)

common_cols <- Reduce(intersect, lapply(normalizedData[c("rnaseq", "abtiter", "cytof", "olink")], colnames))
common_cols <- intersect(common_cols, dataset_cols)
removingNums <- sample(common_cols, 20, replace=FALSE)
# cols <- cols[! cols %in% removingNums]

normalizedData[["abtiter"]][, removingNums] <- NA
normalizedData[["cytof"]][, removingNums] <- NA
normalizedData[["rnaseq"]][, removingNums] <- NA
normalizedData[["olink"]][, removingNums] <- NA


imputationTestingList$actualData[["abtiter"]] <- normalizedData$abtiter[, removingNums]
imputationTestingList$actualData[["cytof"]] <- normalizedData$cytof[, removingNums]
imputationTestingList$actualData[["rnaseq"]] <- normalizedData$rnaseq[, removingNums]
imputationTestingList$actualData[["olink"]] <- normalizedData$olink[, removingNums]

normalizedData[["abtiter"]] <- add_cols(normalizedData[["abtiter"]],  cols)
normalizedData[["cytof"]] <- add_cols(normalizedData[["cytof"]],  cols)
normalizedData[["rnaseq"]] <- add_cols(normalizedData[["rnaseq"]],  cols)
normalizedData[["olink"]] <- add_cols(normalizedData[["olink"]],  cols)

normalizedData[["abtiter"]] <- normalizedData$abtiter[, !duplicated(cols)]
normalizedData[["cytof"]] <- normalizedData$cytof[, !duplicated(cols)]
normalizedData[["rnaseq"]] <- normalizedData$rnaseq[, !duplicated(cols)]

#
MOFAobject <- create_mofa(normalizedData)

Plot multiomic missing values

plot_data_overview(MOFAobject)

metaDf1 <- data.frame(metaDf[metaDf$specimen_id %in% cols, ])
colnames(metaDf1)[colnames(metaDf1)=="specimen_id"] <- "sample"
rownames(metaDf1) <- metaDf1$sample
metaDf1$sample <- as.character(metaDf1$sample)
metaDf1 <- metaDf1[cols,]
samples_metadata(MOFAobject) <- metaDf1

Setting data Options

knitr::opts_chunk$set(warning = FALSE, message = FALSE)

data_opts <- get_default_data_options(MOFAobject)
data_opts

## $scale_views
## [1] FALSE
## 
## $scale_groups
## [1] FALSE
## 
## $center_groups
## [1] TRUE
## 
## $use_float32
## [1] TRUE
## 
## $views
## [1] "abtiter" "cytof"   "rnaseq"  "olink"  
## 
## $groups
## [1] "group1"

Setting model Options

knitr::opts_chunk$set(warning = FALSE, message = FALSE)
model_opts <- get_default_model_options(MOFAobject)
model_opts$num_factors <- 15

model_opts

## $likelihoods
##    abtiter      cytof     rnaseq      olink 
## "gaussian" "gaussian" "gaussian" "gaussian" 
## 
## $num_factors
## [1] 15
## 
## $spikeslab_factors
## [1] FALSE
## 
## $spikeslab_weights
## [1] FALSE
## 
## $ard_factors
## [1] FALSE
## 
## $ard_weights
## [1] TRUE

Setting training Options

knitr::opts_chunk$set(warning = FALSE, message = FALSE)
train_opts <- get_default_training_options(MOFAobject)
train_opts$convergence_mode <- "medium"
train_opts$seed <- 42

train_opts

## $maxiter
## [1] 1000
## 
## $convergence_mode
## [1] "medium"
## 
## $drop_factor_threshold
## [1] -1
## 
## $verbose
## [1] FALSE
## 
## $startELBO
## [1] 1
## 
## $freqELBO
## [1] 5
## 
## $stochastic
## [1] FALSE
## 
## $gpu_mode
## [1] FALSE
## 
## $seed
## [1] 42
## 
## $outfile
## NULL
## 
## $weight_views
## [1] FALSE
## 
## $save_interrupted
## [1] FALSE

Training Model

MOFAobject <- prepare_mofa(MOFAobject,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts
)
MOFAobject <- run_mofa(MOFAobject, outfile=".../MOFA2_2ndChallenge_F40.hdf5", use_basilisk = TRUE)

MOFAobject

## Trained MOFA with the following characteristics: 
##  Number of views: 4 
##  Views names: abtiter cytof rnaseq olink 
##  Number of features (per view): 27 20 10269 30 
##  Number of groups: 1 
##  Groups names: group1 
##  Number of samples (per group): 180 
##  Number of factors: 15

Impute Data

imputed_data <- impute(MOFAobject, views = "all")

Add meta data to Mofa and Imputed objects

metaDf1 <- data.frame(metaDf[metaDf$specimen_id %in% cols, ])
colnames(metaDf1)[colnames(metaDf1)=="specimen_id"] <- "sample"
rownames(metaDf1) <- metaDf1$sample
metaDf1$sample <- as.character(metaDf1$sample)
metaDf1 <- metaDf1[cols,]

samples_metadata(MOFAobject) <- metaDf1
samples_metadata(imputed_data) <- metaDf1

Mofa object

MOFAobject

## Trained MOFA with the following characteristics: 
##  Number of views: 4 
##  Views names: abtiter cytof rnaseq olink 
##  Number of features (per view): 27 20 10269 30 
##  Number of groups: 1 
##  Groups names: group1 
##  Number of samples (per group): 180 
##  Number of factors: 15

slotNames(MOFAobject)

##  [1] "data"               "covariates"         "covariates_warped" 
##  [4] "intercepts"         "imputed_data"       "interpolated_Z"    
##  [7] "samples_metadata"   "features_metadata"  "expectations"      
## [10] "training_stats"     "data_options"       "model_options"     
## [13] "training_options"   "stochastic_options" "mefisto_options"   
## [16] "dimensions"         "on_disk"            "dim_red"           
## [19] "cache"              "status"

Imputed data

imputed_data

## Trained MOFA with the following characteristics: 
##  Number of views: 4 
##  Views names: abtiter cytof rnaseq olink 
##  Number of features (per view): 27 20 10269 30 
##  Number of groups: 1 
##  Groups names: group1 
##  Number of samples (per group): 180 
##  Number of factors: 15

slotNames(imputed_data)

##  [1] "data"               "covariates"         "covariates_warped" 
##  [4] "intercepts"         "imputed_data"       "interpolated_Z"    
##  [7] "samples_metadata"   "features_metadata"  "expectations"      
## [10] "training_stats"     "data_options"       "model_options"     
## [13] "training_options"   "stochastic_options" "mefisto_options"   
## [16] "dimensions"         "on_disk"            "dim_red"           
## [19] "cache"              "status"

Results for Actual Data

plot_object = MOFAobject

plot_factor_cor(plot_object)

plot_factor(plot_object,
  factors = 1,
  color_by = "Factor1"
)

plot_variance_explained(plot_object, max_r2=1)

plot_variance_explained(plot_object, plot_total = T)[[2]]

correlate_factors_with_covariates(plot_object,
  covariates = c("timepoint", "infancy_vac", "biological_sex", "ethnicity", "race"),
  plot="log_pval"
)

plot_weights(plot_object,
 view = "cytof",
 factor = 15,
 nfeatures = 10,     # Top number of features to highlight
 scale = T           # Scale weights from -1 to 1
)

plot_data_heatmap(plot_object,
  view = "cytof",
  factor = 12,
  features = 25,
  cluster_rows = FALSE, cluster_cols = FALSE,
  show_rownames = TRUE, show_colnames = FALSE,
  scale = "row"
)

plot_factor(plot_object,
  factors = 9,
  color_by = "infancy_vac",
  dodge = TRUE,
  add_violin = TRUE
)

plot_factor(plot_object,
  factors = 15,
  color_by = "infancy_vac",
  dodge = TRUE,
  add_violin = TRUE
)

Results for Imputed Data

plot_object = imputed_data

plot_factor_cor(plot_object)

plot_factor(plot_object,
  factors = 1,
  color_by = "Factor1"
)

plot_variance_explained(plot_object, max_r2=1)

plot_variance_explained(plot_object, plot_total = T)[[2]]

correlate_factors_with_covariates(plot_object,
  covariates = c("timepoint", "infancy_vac", "biological_sex", "ethnicity", "race"),
  plot="log_pval"
)

plot_weights(plot_object,
 view = "cytof",
 factor = 15,
 nfeatures = 10,     # Top number of features to highlight
 scale = T           # Scale weights from -1 to 1
)

plot_object = imputed_data
plot_data_heatmap(plot_object,
  view = "cytof",
  factor = 12,
  features = 25,
  cluster_rows = FALSE, cluster_cols = FALSE,
  show_rownames = TRUE, show_colnames = FALSE,
  scale = "row"
)

plot_factor(plot_object,
  factors = 15,
  color_by = "infancy_vac",
  dodge = TRUE,
  add_violin = TRUE
)

plot_factor(plot_object,
  factors = 9,
  color_by = "infancy_vac",
  dodge = TRUE,
  add_violin = TRUE
)

results <- imputed_data@imputed_data

imputationTestingList$imputedData[["abtiter"]] <- results$abtiter$group1[, removingNums]
imputationTestingList$imputedData[["cytof"]] <- results$cytof$group1[, removingNums]
imputationTestingList$imputedData[["rnaseq"]] <- results$rnaseq$group1[, removingNums]
imputationTestingList$imputedData[["olink"]] <- results$olink$group1[, removingNums]

saveRDS(results, file = here("./results/imputedData2021_removed20Samples.RDS"))

saveRDS(imputationTestingList, file = here("./results/2021Data_imputationTest_removed20Samples.RDS"))


knitr::kable(MOFAobject@data$abtiter$group1[1:10,], "html", align = "lccrr", booktabs=TRUE, border_left = T, 
             border_right = T, caption = "Ab-titer Before Imputation")  %>% 
  kable_styling("striped", full_width = T) %>% 
  scroll_box(width = "100%", height = "400px")

Ab-titer Before Imputation
	468	469	470	471	472	475	476	477	478	479	483	484	485	486	487	490	491	492	493	494	498	499	500	501	502	506	507	508	509	510	513	514	515	516	517	521	522	523	524	525	529	530	531	532	533	537	538	539	540	541	546	547	548	549	550	554	555	556	557	558	562	563	564	565	566	569	570	571	572	573	577	578	579	580	581	585	586	587	588	589	593	594	595	596	597	601	602	603	604	605	608	609	610	611	612	616	617	618	619	620	623	624	625	626	627	630	631	632	633	634	636	637	638	639	640	643	644	645	646	647	650	651	652	653	654	657	658	659	660	661	664	665	666	667	668	669	670	671	672	673	674	675	676	677	678	681	682	683	684	685	688	689	690	691	692	695	696	697	698	699	702	703	704	705	706	709	710	711	712	713	716	717	718	719	720	723	724	725	726	727
IgG_PT	-2.8658018	-2.8798156	-2.7466848	-1.8241103	-1.0790439	-2.7518113	-1.3480966	-1.4602070	4.0425410	10.5472746	-1.3153977	0.2050982	0.7376223	10.3464098	13.9876604	-0.6193798	NA	0.5344224	0.2214477	1.2981739	-3.1367352	-3.1367352	-2.9662342	NA	13.2500544	-2.3332779	-2.5388136	-2.5201285	13.3551588	15.3217611	NA	-3.2983603	-3.3170455	-0.4675748	15.9659281	-2.7845459	-2.7962241	-2.7588539	4.8599758	10.7060614	-2.7564938	-2.7424800	NA	NA	6.8266020	-3.5132270	NA	-3.3894386	-3.4454937	-2.0394435	2.9261088	5.0445271	NA	2.8373551	4.6474695	-0.4928002	-1.2518806	-0.5605338	5.0846872	12.3017893	NA	-0.8810062	-0.6964912	5.2546978	8.6904116	-3.4618790	-3.4151664	-3.3684537	-1.5746887	-0.3157835	-3.7813694	-3.8280821	-3.8093970	-3.4310248	-2.6462526	7.2498178	6.4113264	6.5841627	6.6355467	7.7309580	-3.2722018	-3.0666661	NA	-1.8264459	0.8128176	-2.8728089	-2.7793837	NA	NA	3.849139	0.2966430	-0.2685797	0.2499304	NA	5.6031981	-3.5151074	-3.4824085	-3.5337925	-2.6719446	NA	-1.2635586	-1.1981611	-1.1210852	NA	0.9085784	NA	NA	NA	NA	NA	-2.4435191	-2.5439515	-2.4925675	-0.8249266	0.1583743	-3.6930707	-3.3614111	-3.3894386	-2.4925561	0.5180731	-3.6459029	-3.6132040	-3.6178753	-2.9125147	NA	NA	-3.7000890	-0.4278693	0.4876986	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-3.868279	-3.858936	-3.870614	-3.2913780	-2.6677642	-3.6393626	-3.6580474	-3.6346912	-3.2002637	-1.8455975	-2.9690609	-3.1348906	-2.9223480	-1.3925095	0.6348186	NA	-3.711301	-3.6832728	NA	-0.3900325	-1.8287816	-1.9035218	-1.8568091	1.5578842	2.1090932	1.6770015	1.2869511	0.9319348	2.7116861	5.3649635	0.2073982	0.6978807	NA	2.2650895	6.1585875	-3.1834478	-3.0993650	-3.1834478	-2.6088824	-0.3129568
IgG_PRN	-6.3960724	-6.3906784	-6.3902636	-2.3754485	2.0054438	-4.3903923	-2.9090738	-2.4237831	1.9146514	5.4058676	-6.5055351	-6.2949057	-6.2260332	-0.9340280	2.7109764	-3.7507606	NA	-1.5314790	-2.7374449	0.0423617	-4.8259659	-4.8335724	-4.8698063	NA	5.6994057	-0.7837671	-0.9856834	-0.8046502	5.5015001	5.7281723	NA	-5.7092419	-5.8619232	-0.8506650	4.1209021	3.1772897	2.8341701	3.0464590	4.8421307	4.3915539	-2.1353230	-2.0123754	NA	NA	5.4369535	0.7368987	NA	2.4625914	0.7418773	5.0935888	-3.8723252	-2.2697182	NA	6.5695133	6.4632998	4.9748945	5.1971407	5.0325651	5.5312710	5.4406853	NA	-0.7271632	-0.6519287	3.1663640	4.5644274	0.3981745	0.3409188	0.5493350	5.0747499	5.7880955	1.9964545	1.2806196	1.5273447	6.8712120	6.9169884	-6.5055351	-6.5055351	-6.5055351	0.2018888	2.7584813	-2.9441700	-2.8772335	NA	-2.0793874	-0.3823223	-6.1098309	-5.9914465	NA	NA	4.701128	-6.4070997	-6.3813763	-6.4228659	NA	-1.7483649	0.1963201	-0.2975451	-0.3105452	4.0132294	NA	-2.5700717	-2.5408907	-2.6819553	NA	4.7575541	NA	NA	NA	NA	NA	4.0272732	3.6942499	3.8563359	4.7234697	4.5027447	-5.5045280	-3.6037488	-3.4698756	1.5458086	6.5130873	-3.6150210	-3.6450319	-3.0083041	5.6866827	NA	NA	-6.0371485	3.9188468	2.9406593	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-6.430884	-6.268521	-6.218596	3.7884324	4.9414301	0.5639937	0.6613562	0.8125169	4.5681601	6.0995426	-4.2609749	-4.7452974	-4.3660822	3.6982615	5.4257526	NA	-3.181694	-3.4046319	NA	5.1018534	0.3742456	0.3124261	-0.0107783	3.2124145	3.5967467	-1.8947585	-2.0405254	-2.1088450	-0.0260645	4.2654867	-3.2808514	-2.9196148	NA	3.0280650	3.4757388	-3.6459632	-3.1579068	-3.4414194	0.3084154	4.1493893
IgG_FHA	3.6871624	3.7213221	3.7239494	8.0674820	14.2122793	-1.6513464	4.4658613	4.5657120	19.2819595	32.9839249	-5.3629251	-4.2474809	-4.3880610	15.7188425	30.6544991	-2.3726408	NA	0.6452341	1.4492998	20.7074661	1.7006459	2.2629666	3.6543159	NA	15.8414335	5.7643328	1.5521832	3.6398640	9.3287621	14.1952009	NA	-8.0266628	-3.9564080	-1.3418798	3.6033888	-7.3646574	-7.2516680	-1.0766521	2.0423880	4.9026031	-6.2280254	-5.3306775	NA	NA	5.1996956	2.0142484	NA	5.8769178	3.9061680	19.8705559	19.4606400	35.0584679	NA	37.5363579	51.4051781	-8.4709921	-8.4355183	-7.8075066	-8.3067627	-7.4501438	NA	-2.2196867	-0.7849808	-2.5731080	-0.5287833	-8.0964632	-7.2792587	-6.1940322	0.1609788	0.5892887	-0.2030983	-2.4339869	-3.9501505	2.9369631	24.9975357	17.2143898	12.0615358	7.5498314	12.8879366	15.8427477	-8.6724939	-8.5713291	NA	-8.6409616	-8.6593552	-8.6435890	-8.6317654	NA	NA	-8.543738	-8.5485086	-8.6194553	-8.5432529	NA	-8.4118700	-8.6081161	-8.5069504	-8.6107435	-8.5660734	NA	-8.6619835	-8.6107435	-8.5844669	NA	-8.5542488	NA	NA	NA	NA	NA	-7.5614653	-8.5009575	-4.4543509	-1.8897483	-0.9214530	-4.1042728	2.5424094	2.9260483	16.1602898	60.5336914	1.7610826	1.5101404	3.6359224	14.6340208	NA	NA	-8.6020212	-4.5528884	-6.0953283	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-8.690888	-8.690888	-8.690888	-8.6908875	-8.4761610	-7.9346952	-8.6139469	-8.6362820	0.4988003	3.5442657	-7.3541470	-8.6758633	-8.2882824	-8.3513470	2.0896859	NA	-8.690888	-8.6908875	NA	-8.5931787	-8.6220827	-8.6601839	-8.5997477	-8.6036892	-8.5577049	-8.6593552	-8.6304512	-8.6606693	-8.5752697	-8.6041746	-8.3868198	-7.7824564	NA	-2.7386510	1.1870828	-8.5550776	-8.6483593	-8.5248594	-8.5629606	-8.3185873
IgG1_PT	-9.3683338	-7.8277416	-3.9146371	-8.6288490	-10.2310658	-10.7881079	-11.3406954	-11.3406954	12.3207769	47.2922211	-9.1242676	-11.0346022	-10.6648598	58.4461136	76.2553558	-5.9814596	NA	-9.5556335	-3.4548879	0.3349695	-9.8613234	-6.5952678	43.5664177	NA	-10.2926893	-9.5840168	20.4883461	40.4544220	-4.5308738	-9.7996998	NA	-9.4496775	-9.9426670	-3.8419218	86.2211075	-5.9365420	-4.7965040	-5.1046224	22.8109093	66.7794113	-11.3406954	-11.1407108	NA	NA	27.4629135	-11.3406954	NA	-11.3406954	-11.3406954	-10.8497314	-11.2810974	-11.3406954	NA	-8.3847837	-6.9674387	-4.3954520	-3.6251559	11.5034609	48.1079330	53.2535095	NA	-0.7601519	-2.0234375	24.1050072	47.2138901	-11.3406954	-11.3406954	-11.2669916	-4.3651381	0.9961233	-11.3402920	-10.7856789	-8.9061565	-11.0321732	-11.3402920	-0.7718287	-0.0939684	2.5250387	-3.7605777	-1.7269955	-10.0035315	-10.3116493	NA	8.2370825	40.2197762	-11.0511341	-10.3116493	NA	NA	31.130283	0.4111958	0.2263250	2.5372133	NA	40.0352287	-11.3406954	-11.3406954	-11.3406954	-8.8634930	NA	-10.6813917	-10.7430153	-10.4965210	NA	-2.8859949	NA	NA	NA	NA	NA	-11.1135178	-11.2983885	-11.2367649	-4.5197821	0.2252417	-11.3406954	-11.3406954	-11.3406954	-9.9253759	1.2901363	-10.9705496	-9.4299574	-3.8838253	-10.6316195	NA	NA	-11.3406954	-5.1976428	-2.3629532	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-11.340695	-11.340695	-11.340695	-11.3406954	-9.6152315	-11.2639580	-10.8053989	-11.1407108	-7.3236599	-3.1024375	-10.7740021	-11.1437445	-11.1558237	-8.0625591	-1.9310017	NA	-11.340695	-9.7536364	NA	-2.8548598	-8.7091103	-8.9864178	-0.7596540	8.6687708	4.6632309	0.1368055	-1.7735291	-0.9107981	14.9232531	37.7240181	-1.7769432	0.0409565	NA	7.2817392	29.4354553	-11.3406954	-11.3406954	-11.2942286	-7.5382605	-4.7651944
IgG1_PRN	-1.6423014	-1.2185757	-0.0709403	-1.3466283	-1.6928484	-0.8336573	-1.1471577	-1.2196085	0.4619777	2.1087790	-1.6584982	-1.6933919	-1.6858914	0.0909190	0.7241702	-0.3307956	NA	-0.7503905	-0.1163239	0.3620794	-1.4740286	-0.6851685	0.6346531	NA	-1.5404464	-0.5743455	0.1644567	0.7742825	-0.1352926	-0.6385891	NA	-1.6599423	-1.6711388	-0.4700756	0.8567030	1.0065722	0.9992890	0.8761282	1.4045353	1.6544442	-0.5857359	-0.3944724	NA	NA	1.4675615	0.5858996	NA	0.1861974	0.7837396	1.2585015	-0.7218015	-1.3171153	NA	2.4005933	2.6263156	1.3110502	1.3497486	1.3680108	1.4743769	1.7278187	NA	0.2233647	0.0763434	0.7769909	1.2996364	0.3382642	0.2468446	0.3306549	1.2896357	1.7605929	0.0568404	0.5487773	1.1742020	0.4456179	-0.0320245	-1.6954573	-1.1346025	-0.2357889	-1.3820655	-1.6974140	0.2771728	0.2576060	NA	0.8357449	1.5972660	-1.6337776	-1.5573049	NA	NA	1.712818	-1.6824224	-1.6956298	-1.6501374	NA	-0.8155133	0.0117191	-0.0775263	-0.0857878	0.8486261	NA	-0.3986360	-0.5012519	-0.4486395	NA	1.4242647	NA	NA	NA	NA	NA	1.0328567	0.9595907	0.9241533	1.4410923	1.4233735	-1.4179378	-1.3482589	-1.3295076	0.4729025	2.4507599	-1.2412405	-0.1841005	0.8770072	-0.6765265	NA	NA	-1.1730601	1.2652645	1.1465061	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.698207	-1.663345	-1.683423	-0.6142170	-0.2133192	0.0923555	0.3381338	0.0792024	1.2277617	1.5559916	-1.1251545	-1.2511960	-1.2808721	0.6316001	1.2820809	NA	-1.141406	-0.5272321	NA	0.6337197	-0.1566623	-0.1624236	0.3338072	0.7766645	0.7308462	-0.8455650	-0.9104609	-0.9033952	0.2606497	1.6210721	-0.8716040	-0.7732275	NA	0.6254585	1.4644852	-0.8192092	-0.6499581	-0.4826636	0.5667040	0.7375314
IgG1_FHA	-0.7092298	-0.5517942	0.8381332	-0.3219101	-0.7097523	-0.9867374	-1.0527420	-1.0187819	-0.7765329	-0.5736428	-1.0779943	-1.0637138	-0.9668838	-0.6015075	-0.6565404	-0.8430600	NA	-0.9597434	-0.8738853	-0.6610684	-0.8610926	-0.7024379	0.1904525	NA	-0.9460800	-0.7719256	-0.6108326	0.1801773	0.4213814	-0.7233363	NA	0.9310988	0.6658615	1.5531787	3.7720819	-0.4778304	-0.4457859	-0.3386809	0.0902617	0.4518067	-0.1180081	0.3753716	NA	NA	4.3339043	-1.1135219	NA	-1.1420833	-0.9475526	-0.7989988	-1.0341074	-1.1041176	NA	-0.6076030	-0.4909195	-0.4842997	-0.6197919	-0.3451501	0.1205391	0.4794716	NA	-0.4330727	-0.4412580	-0.3315406	-0.0180624	-0.9818336	-0.9757382	-1.0323384	-0.5307734	-0.2458565	-0.9493890	-0.4389421	0.8801044	-0.7102748	-0.9816076	-0.1362615	0.4966162	1.4055287	-0.3577861	-0.3738083	0.0854850	0.1070801	NA	2.2028556	4.5286894	-0.1316857	0.0813053	NA	NA	6.599039	-0.9186410	-0.8907763	-0.9691458	NA	-0.6612406	0.1321584	0.0921029	0.0409014	1.2173153	NA	0.0868782	-0.0628947	-0.0487882	NA	2.4306498	NA	NA	NA	NA	NA	0.3450688	0.2158462	0.0730394	2.3722277	3.3339090	-1.2221924	-1.2221924	-1.2221924	-0.8972220	1.7394778	-0.7663525	-0.1470591	-0.1101383	-0.5880183	NA	NA	-0.5983264	0.8022245	1.2663463	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.222192	-1.222192	-1.222192	-1.2221924	-1.0456645	0.1709143	0.4845666	0.2154979	2.4295244	4.5084071	-1.2212961	-1.1657407	-1.2221924	-0.8416392	-0.4753922	NA	-1.222192	-0.2370001	NA	1.5313650	0.0799611	-0.0436887	-0.4163793	0.5435604	0.5787398	0.6375889	0.5027932	0.5139390	2.5588274	4.9370818	-0.8480829	-0.8623636	NA	-0.6458895	-0.0008211	-0.6608924	-0.7378687	-0.5295798	0.4629270	0.4563090
IgG1_TT	-0.3503404	0.0296774	0.2893068	-0.1659075	-0.3627669	-0.7439322	-0.9041494	-0.9295069	0.3108350	0.9017318	0.1222599	-0.1410602	-0.0629696	0.9557394	1.0088443	0.2603058	NA	0.1133382	0.5696145	0.8578142	-0.1030846	0.0083824	0.3373930	NA	-0.1783340	-0.1871760	0.0393159	0.3059816	-0.0722041	-0.2432015	NA	-0.4716715	-0.5127481	0.0634123	0.5234325	-0.0517056	0.0046120	-0.0300654	0.4757310	0.7687367	-0.4440836	-0.3039135	NA	NA	0.6784984	0.0494797	NA	-0.0934252	0.1922520	0.4073795	-0.0977533	-0.3889267	NA	0.6193472	0.7129444	-0.2595365	-0.2720162	0.0962390	0.5555156	0.6418906	NA	0.0320406	-0.0481211	0.4747220	0.5762318	0.3438188	0.2964227	0.3633348	0.6720594	0.7697988	-0.7857996	-0.3556509	0.2380606	-0.4833148	-0.8348684	-0.9694891	-0.3379670	0.3038043	-0.4716052	-0.9499996	-0.0308408	-0.0396296	NA	0.5283526	0.9192835	0.1292436	0.1860126	NA	NA	1.118373	-0.0208305	0.0186794	0.1787373	NA	0.5057299	0.2955147	0.2515705	0.2842033	0.5612510	NA	-0.3877846	-0.4099293	-0.3852355	NA	0.6756918	NA	NA	NA	NA	NA	0.0562166	0.0797685	0.0484368	0.5769621	0.6659657	-0.8266695	-0.7200882	-0.7200617	0.4995162	1.1251172	-0.5536255	-0.1090589	0.3979059	-0.2919517	NA	NA	-0.4672373	0.4909323	0.4739919	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.003914	-1.003914	-1.003914	-0.3092506	-0.2393647	-1.0039142	-0.6676813	-0.8466178	0.0052360	0.3549840	-0.6914590	-0.7825071	-0.7855607	0.4207145	0.5766035	NA	-1.003914	-0.5675443	NA	0.0584018	-0.3104640	-0.3234481	0.1172419	0.4279846	0.4436505	-0.2648975	-0.3070627	-0.2980880	0.6979162	0.9137605	0.1675640	0.1925232	NA	0.3313923	0.6766530	-0.9711937	-0.9649539	-0.6693727	-0.0494275	0.2162824
IgG1_DT	-0.5865553	-0.0645725	0.3655865	-0.4693693	-0.7631364	-0.5013164	-0.6886927	-0.7261506	0.4692075	0.9637474	0.1926954	-0.0942218	-0.0020511	0.5906856	0.5823183	0.7619776	NA	0.5344554	0.8436133	1.0593430	-0.1317279	-0.0447162	0.5136406	NA	-0.2397228	-0.5027953	0.0315435	0.6120110	-0.1291699	-0.5685634	NA	-1.1929172	-1.1929172	-0.1966797	0.5668720	-0.7480384	-0.7248874	-0.7229798	0.4102086	1.0098819	-0.9392047	-0.7939252	NA	NA	1.0163668	0.2014962	NA	0.0084839	0.9835600	1.1468748	0.5685750	0.1701946	NA	0.6735786	0.7393900	-0.2155867	-0.1741402	0.1631544	0.7059907	0.7481741	NA	0.3091936	0.3281827	1.0387124	1.1521701	-0.1018895	-0.1976153	-0.0600095	1.1028763	1.2946748	-0.4681987	0.0247803	0.5106926	-0.2086813	-0.5467129	-1.1929172	-1.0539554	-0.6682777	-1.1929172	-1.1929172	-0.0434970	-0.0465317	NA	0.6592289	1.3629087	-0.0907528	0.0254794	NA	NA	1.432535	-1.1929172	-1.1768328	-0.8736579	NA	0.3381314	0.6956464	0.5920736	0.6376387	0.9705545	NA	0.9884163	0.9886333	0.9639214	NA	1.1161374	NA	NA	NA	NA	NA	-0.4748393	-0.4420203	-0.4649113	0.4329079	0.4342953	-1.1084466	-1.0442827	-1.0789659	0.1874062	1.3782555	0.3739538	0.3885208	0.2728956	0.2981709	NA	NA	0.1757317	0.8849174	0.9733163	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.192917	-1.192917	-1.192917	-1.0932231	-0.5517743	-0.9697692	-0.7625802	-0.8534937	0.3428615	0.5015374	-0.4247038	-0.5350833	-0.5500404	0.5212817	0.6051284	NA	-0.745460	-0.4966510	NA	-0.3324692	-0.1303959	-0.1563650	0.1455094	0.3492733	0.2400211	-1.1929172	-1.1929172	-1.1929172	-0.7926551	0.1636921	-0.7521137	-0.6945395	NA	-0.1738139	0.5804167	0.1563479	0.0930076	0.3606755	0.7052101	0.7769612
IgG1_OVA	-4.9051704	-5.5497599	-5.5301075	-5.5772729	-5.6087160	-5.9054632	-5.9054632	-5.9054632	-5.9054632	-5.8759847	-4.9719877	-5.7757587	-5.6264029	-5.3905778	-5.5084906	-4.0974693	NA	-5.1193786	-4.2880945	-4.0109997	3.4351835	1.2498693	1.8787365	NA	0.9354353	-3.6494012	-4.1937642	-4.2330685	-3.7280095	-4.3902855	NA	-5.9054632	-5.9054632	-5.9054632	-5.8917065	-5.9054632	-4.6245589	-5.5244188	-5.8881893	-5.6936331	9.3937025	9.9714737	NA	NA	9.0497904	34.4894409	NA	24.8147087	28.2616882	26.6934509	-5.8799152	-5.9054632	NA	-5.8976021	-5.8700891	11.9677153	13.2490320	13.2018671	14.9332170	14.7701063	NA	-0.1635303	-2.2309322	-1.3603437	-1.6433342	-2.0697849	-2.7242000	-1.9970720	-2.3783231	-1.4802217	13.9136858	11.1348791	8.6174440	7.6033955	11.8698683	-5.5399337	-5.5261774	-5.4966993	-5.4908037	-5.6205072	1.9333701	1.7958050	NA	-0.7452123	-0.5427957	-5.9038901	-5.8449340	NA	NA	-5.675926	-5.7014942	-5.7290072	-5.6857724	NA	-5.5855465	-5.9054632	-5.9054632	-5.9054632	-5.9054632	NA	-5.7820473	-5.8527946	-5.8331428	NA	-5.7564993	NA	NA	NA	NA	NA	1.4856944	1.2243214	0.5895581	-0.4716554	0.4893327	-3.8812959	-2.9851599	-2.3523622	-0.0216222	2.4545431	0.4991589	0.7978706	-0.4559338	-0.9629581	NA	NA	-4.2428946	-2.5901525	-3.6317141	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-5.027220	-1.030188	-3.248705	-4.0465803	-2.7829499	2.2324743	3.5766788	1.1358871	4.2428846	3.3801575	5.9364834	4.4881229	3.9732380	2.0060620	4.1147327	NA	-2.806739	-2.9010689	NA	-3.6144404	-5.5285559	-5.3890257	-5.4145737	-5.4912167	-5.4794254	-4.1919956	-4.6695418	-4.6656113	-3.9858446	-2.6377101	9.3657761	10.6588850	NA	11.4567604	12.2664270	14.3868895	11.8694544	11.3309870	12.6555395	11.1580486
IgG2_PT	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-9.3557396	1.5892558	-7.2256622	-7.2256622	3.9489760	13.7267857	38.8697205	20.7109337	28.3934994	45.1554527	50.0443573	-3.0351727	NA	-5.8288321	-1.6383429	1.1553168	-9.3557396	-9.3557396	-9.3557396	NA	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-9.3557396	NA	-9.3557396	-9.3557396	-9.3557396	-3.3263073	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-8.6573248	28.4515705	26.7055340	NA	NA	37.8801689	-7.9240770	NA	-7.2256622	2.5521464	2.5521464	29.7903271	17.2188606	NA	14.4251995	28.3934994	-9.3557396	-9.3557396	-9.3557396	-8.4530277	-4.9609537	NA	-9.3557396	-9.3557396	-9.3557396	-8.6573248	-9.3557396	-4.4668355	-7.2604952	-5.8636651	-5.1652503	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-9.3557396	15.2930794	18.7851543	11.1025887	14.9438725	15.9914932	-9.3557396	-9.3557396	NA	-5.0999451	0.1381669	2.5826192	-3.7031152	NA	NA	9.566769	-9.3557396	-9.3557396	-9.3557396	NA	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-9.3557396	NA	3.2810340	3.9794488	1.1857891	NA	9.5667686	NA	NA	NA	NA	NA	21.1182137	16.9277248	19.0229702	22.5150452	32.9912643	-7.9240770	-9.3209066	-7.9240770	-6.5272474	4.6473908	-9.3557396	-9.3557396	-9.3557396	-9.3557396	NA	NA	-9.3557396	-9.3557396	-9.3557396	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-9.355740	-9.355740	-9.355740	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-9.3557396	-8.9136267	-9.3557396	-9.3557396	-9.3557396	-7.2604952	-6.5620799	NA	-9.355740	-9.3557396	NA	9.7057590	3.4200253	8.3089294	2.7216105	15.9914932	18.0867386	15.8525028	14.4556732	17.2493324	15.1540871	17.9477463	-9.3557396	-9.3557396	NA	-4.8160429	-5.5144577	0.6263657	2.7216105	2.0231957	6.2136846	22.2772274

# abtiter <- head(results$abtiter$group1)
knitr::kable(results$abtiter$group1[1:10,], "html", align = "lccrr", booktabs=TRUE, border_left = T, 
             border_right = T, caption = "Ab-titer After Imputation")  %>% 
  kable_styling("striped", full_width = T) %>% 
  scroll_box(width = "100%", height = "400px")

Ab-titer After Imputation
	468	469	470	471	472	475	476	477	478	479	483	484	485	486	487	490	491	492	493	494	498	499	500	501	502	506	507	508	509	510	513	514	515	516	517	521	522	523	524	525	529	530	531	532	533	537	538	539	540	541	546	547	548	549	550	554	555	556	557	558	562	563	564	565	566	569	570	571	572	573	577	578	579	580	581	585	586	587	588	589	593	594	595	596	597	601	602	603	604	605	608	609	610	611	612	616	617	618	619	620	623	624	625	626	627	630	631	632	633	634	636	637	638	639	640	643	644	645	646	647	650	651	652	653	654	657	658	659	660	661	664	665	666	667	668	669	670	671	672	673	674	675	676	677	678	681	682	683	684	685	688	689	690	691	692	695	696	697	698	699	702	703	704	705	706	709	710	711	712	713	716	717	718	719	720	723	724	725	726	727
IgG_PT	0.7943678	0.7803540	0.9134848	1.8360593	2.5811257	0.9083583	2.3120730	2.1999626	7.702711	14.207444	2.3447719	3.8652678	4.3977919	14.006579	17.647830	3.0407898	3.660170	4.1945920	3.8816173	4.9583435	0.5234344	0.5234344	0.6939354	3.660170	16.9102240	1.3268917	1.1213560	1.1400411	17.0153284	18.9819307	3.660170	0.3618093	0.3431242	3.192595	19.626098	0.8756237	0.8639455	0.9013157	8.520145	14.366231	0.9036758	0.9176896	3.660170	3.660170	10.486772	0.1469426	3.660170	0.2707310	0.2146759	1.6207261	6.5862784	8.7046967	3.660170	6.497525	8.307639	3.1673694	2.408289	3.099636	8.744857	15.961959	3.660170	2.779163	2.963678	8.914867	12.350581	0.1982906	0.2450032	0.2917159	2.0854809	3.344386	-0.1211998	-0.1679125	-0.1492274	0.2291448	1.0139170	10.9099874	10.0714960	10.2443323	10.2957163	11.3911276	0.3879678	0.5935035	3.660170	1.8337237	4.472987	0.7873607	0.8807859	3.660170	3.660170	7.509308	3.9568126	3.3915899	3.9101000	3.660170	9.2633677	0.1450622	0.1777611	0.1263771	0.988225	3.660170	2.3966110	2.4620085	2.5390844	3.660170	4.568748	1.710877	3.264269	1.1050591	1.8281397	1.846576	1.2166505	1.1162181	1.1676021	2.835243	3.818544	-0.0329010	0.2987585	0.2707310	1.1676135	4.178243	0.0142667	0.0469656	0.0422943	0.7476549	3.660170	3.660170	-0.0399194	3.2323003	4.1478682	3.660170	2.931367	3.008275	3.069359	3.5175458	3.818175	2.0601151	3.4063709	3.471312	3.973157	2.8172638	-0.2081094	-0.1987667	-0.2104449	0.3687916	0.9924054	0.0208070	0.0021222	0.0254784	0.4599059	1.814572	0.6911087	0.5252790	0.7378216	2.2676601	4.2949882	3.660170	-0.0511310	-0.0231032	3.660170	3.2701371	1.8313880	1.7566478	1.803360	5.218054	5.769263	5.3371711	4.9471207	4.5921044	6.3718557	9.025133	3.8675678	4.3580503	3.660170	5.925259	9.8187571	0.4767218	0.5608046	0.4767218	1.051287	3.347213
IgG_PRN	-4.4400501	-4.4346561	-4.4342413	-0.4194262	3.9614661	-2.4343700	-0.9530516	-0.4677608	3.870674	7.361890	-4.5495129	-4.3388834	-4.2700109	1.021994	4.666999	-1.7947383	1.956022	0.4245433	-0.7814226	1.9983840	-2.8699436	-2.8775501	-2.9137840	1.956022	7.6554279	1.1722552	0.9703388	1.1513721	7.4575224	7.6841946	1.956022	-3.7532196	-3.9059010	1.105357	6.076924	5.1333120	4.7901924	5.0024812	6.798153	6.347576	-0.1793008	-0.0563531	1.956022	1.956022	7.392976	2.6929209	1.956022	4.4186137	2.6978996	7.0496111	-1.9163029	-0.3136959	1.956022	8.525536	8.419322	6.9309168	7.153163	6.988587	7.487293	7.396707	1.956022	1.228859	1.304094	5.122386	6.520450	2.3541968	2.2969410	2.5053573	7.0307722	7.744118	3.9524767	3.2366419	3.4833670	8.8272343	8.8730106	-4.5495129	-4.5495129	-4.5495129	2.1579111	4.7145035	-0.9881477	-0.9212112	1.956022	-0.1233652	1.573700	-4.1538086	-4.0354242	1.956022	1.956022	6.657150	-4.4510775	-4.4253540	-4.4668436	1.956022	0.2076573	2.1523423	1.6584772	1.6454771	5.969252	1.956022	-0.6140494	-0.5848684	-0.7259331	1.956022	6.713576	1.659926	1.601073	0.9147029	0.3505958	1.889504	5.9832954	5.6502721	5.8123581	6.679492	6.458767	-3.5485058	-1.6477265	-1.5138533	3.5018308	8.469110	-1.6589987	-1.6890097	-1.0522819	7.6427050	1.956022	1.956022	-4.0811262	5.8748691	4.8966815	1.956022	1.340276	1.921556	1.675730	1.0649438	2.269758	1.7612128	0.8476521	1.011529	1.925124	1.1714520	-4.4748621	-4.3124990	-4.2625732	5.7444546	6.8974524	2.5200160	2.6173785	2.7685392	6.5241823	8.055565	-2.3049526	-2.7892752	-2.4100599	5.6542838	7.3817749	1.956022	-1.2256718	-1.4486096	1.956022	7.0578756	2.3302679	2.2684484	1.945244	5.168437	5.552769	0.0612638	-0.0845032	-0.1528227	1.9299577	6.221509	-1.3248291	-0.9635925	1.956022	4.984087	5.4317610	-1.6899409	-1.2018845	-1.4853971	2.264438	6.105411
IgG_FHA	8.7799115	8.8140712	8.8166986	13.1602311	19.3050284	3.4414027	9.5586104	9.6584611	24.374709	38.076674	-0.2701759	0.8452682	0.7046881	20.811592	35.747248	2.7201083	5.092749	5.7379832	6.5420489	25.8002152	6.7933950	7.3557158	8.7470651	5.092749	20.9341826	10.8570819	6.6449323	8.7326131	14.4215112	19.2879500	5.092749	-2.9339137	1.1363411	3.750869	8.696138	-2.2719083	-2.1589189	4.0160971	7.135137	9.995352	-1.1352763	-0.2379284	5.092749	5.092749	10.292445	7.1069975	5.092749	10.9696670	8.9989171	24.9633050	24.5533891	40.1512170	5.092749	42.629107	56.497927	-3.3782430	-3.342769	-2.714757	-3.214014	-2.357395	5.092749	2.873062	4.307768	2.519641	4.563966	-3.0037141	-2.1865096	-1.1012831	5.2537279	5.682038	4.8896508	2.6587622	1.1425986	8.0297122	30.0902848	22.3071389	17.1542850	12.6425805	17.9806857	20.9354968	-3.5797448	-3.4785800	5.092749	-3.5482125	-3.566606	-3.5508399	-3.5390162	5.092749	5.092749	-3.450989	-3.4557595	-3.5267062	-3.4505038	5.092749	-3.3191209	-3.5153670	-3.4142013	-3.5179944	-3.473324	5.092749	-3.5692344	-3.5179944	-3.4917178	5.092749	-3.461500	2.874670	5.113358	1.7547857	3.8373271	1.962888	-2.4687161	-3.4082084	0.6383982	3.203001	4.171296	0.9884763	7.6351585	8.0187974	21.2530389	65.626441	6.8538318	6.6028895	8.7286716	19.7267699	5.092749	5.092749	-3.5092721	0.5398607	-1.0025792	5.092749	6.531875	2.423749	6.474574	4.4484710	6.746854	3.4134853	6.0544800	6.588367	6.391343	5.2453808	-3.5981383	-3.5981383	-3.5981383	-3.5981383	-3.3834119	-2.8419461	-3.5211978	-3.5435328	5.5915494	8.637015	-2.2613978	-3.5831141	-3.1955333	-3.2585979	7.1824350	5.092749	-3.5981383	-3.5981383	5.092749	-3.5004296	-3.5293336	-3.5674348	-3.506998	-3.510940	-3.464956	-3.5666060	-3.5377021	-3.5679202	-3.4825206	-3.511426	-3.2940707	-2.6897073	5.092749	2.354098	6.2798319	-3.4623284	-3.5556102	-3.4321103	-3.470212	-3.225838
IgG1_PT	1.4537363	2.9943285	6.9074330	2.1932211	0.5910044	0.0339622	-0.5186253	-0.5186253	23.142847	58.114291	1.6978025	-0.2125320	0.1572104	69.268184	87.077426	4.8406105	10.822070	1.2664366	7.3671823	11.1570396	0.9607468	4.2268023	54.3884878	10.822070	0.5293808	1.2380533	31.3104162	51.2764921	6.2911963	1.0223703	10.822070	1.3723927	0.8794031	6.980148	97.043178	4.8855281	6.0255661	5.7174478	33.632979	77.601481	-0.5186253	-0.3186407	10.822070	10.822070	38.284984	-0.5186253	10.822070	-0.5186253	-0.5186253	-0.0276613	-0.4590273	-0.5186253	10.822070	2.437286	3.854631	6.4266181	7.196914	22.325531	58.930003	64.075580	10.822070	10.061918	8.798633	34.927077	58.035960	-0.5186253	-0.5186253	-0.4449215	6.4569321	11.818193	-0.5182219	0.0363913	1.9159136	-0.2101030	-0.5182219	10.0502415	10.7281017	13.3471088	7.0614924	9.0950747	0.8185387	0.5104208	10.822070	19.0591526	51.041846	-0.2290640	0.5104208	10.822070	10.822070	41.952354	11.2332659	11.0483952	13.3592834	10.822070	50.8572989	-0.5186253	-0.5186253	-0.5186253	1.958577	10.822070	0.1406784	0.0790548	0.3255491	10.822070	7.936075	8.915908	9.616322	7.9103791	8.4380516	10.206602	-0.2914476	-0.4763184	-0.4146948	6.302288	11.047312	-0.5186253	-0.5186253	-0.5186253	0.8966942	12.112206	-0.1484795	1.3921127	6.9382448	0.1904507	10.822070	10.822070	-0.5186253	5.6244273	8.4591169	10.822070	9.273033	11.044400	9.631159	9.9652773	10.490618	8.1661771	9.7512956	9.456814	10.344961	10.0202093	-0.5186253	-0.5186253	-0.5186253	-0.5186253	1.2068386	-0.4418879	0.0166712	-0.3186407	3.4984102	7.719633	0.0480680	-0.3216743	-0.3337536	2.7595110	8.8910685	10.822070	-0.5186253	1.0684338	10.822070	7.9672103	2.1129599	1.8356524	10.062416	19.490841	15.485301	10.9588757	9.0485411	9.9112720	25.7453232	48.546088	9.0451269	10.8630266	10.822070	18.103809	40.2575254	-0.5186253	-0.5186253	-0.4721584	3.283810	6.056876
IgG1_PRN	0.1029508	0.5266765	1.6743120	0.3986239	0.0524038	0.9115950	0.5980946	0.5256437	2.207230	3.854031	0.0867541	0.0518603	0.0593609	1.836171	2.469422	1.4144566	1.745252	0.9948617	1.6289283	2.1073316	0.2712237	1.0600837	2.3799053	1.745252	0.2048059	1.1709068	1.9097090	2.5195347	1.6099596	1.1066631	1.745252	0.0853100	0.0741135	1.275177	2.601955	2.7518245	2.7445413	2.6213804	3.149787	3.399697	1.1595163	1.3507799	1.745252	1.745252	3.212814	2.3311518	1.745252	1.9314497	2.5289918	3.0037538	1.0234507	0.4281369	1.745252	4.145846	4.371568	3.0563024	3.095001	3.113263	3.219629	3.473071	1.745252	1.968617	1.821596	2.522243	3.044889	2.0835165	1.9920969	2.0759071	3.0348879	3.505845	1.8020927	2.2940296	2.9194542	2.1908702	1.7132277	0.0497949	0.6106497	1.5094633	0.3631867	0.0478382	2.0224251	2.0028583	1.745252	2.5809971	3.342518	0.1114746	0.1879474	1.745252	1.745252	3.458070	0.0628299	0.0496224	0.0951148	1.745252	0.9297390	1.7569714	1.6677259	1.6594645	2.593878	1.745252	1.3466163	1.2440003	1.2966127	1.745252	3.169517	1.568399	1.792909	1.4101585	1.4417715	1.582079	2.7781090	2.7048429	2.6694056	3.186345	3.168626	0.3273145	0.3969934	0.4157447	2.2181548	4.196012	0.5040118	1.5611517	2.6222595	1.0687257	1.745252	1.745252	0.5721922	3.0105168	2.8917583	1.745252	1.584483	1.429016	1.603443	1.4811543	2.041468	1.6390718	1.6917725	1.697892	1.858151	1.4616760	0.0470456	0.0819075	0.0618292	1.1310352	1.5319331	1.8376077	2.0833861	1.8244547	2.9730140	3.301244	0.6200978	0.4940562	0.4643801	2.3768524	3.0273331	1.745252	0.6038464	1.2180202	1.745252	2.3789719	1.5885899	1.5828286	2.079060	2.521917	2.476098	0.8996872	0.8347914	0.8418571	2.0059019	3.366324	0.8736483	0.9720248	1.745252	2.370711	3.2097374	0.9260431	1.0952941	1.2625886	2.311956	2.482784
IgG1_FHA	1.1314962	1.2889318	2.6788592	1.5188159	1.1309737	0.8539886	0.7879840	0.8219441	1.064193	1.267083	0.7627317	0.7770122	0.8738422	1.239219	1.184186	0.9976661	1.840726	0.8809826	0.9668407	1.1796576	0.9796334	1.1382881	2.0311785	1.840726	0.8946460	1.0688004	1.2298934	2.0209033	2.2621074	1.1173897	1.840726	2.7718248	2.5065875	3.393905	5.612808	1.3628956	1.3949401	1.5020452	1.930988	2.292533	1.7227179	2.2160976	1.840726	1.840726	6.174630	0.7272041	1.840726	0.6986427	0.8931734	1.0417272	0.8066186	0.7366084	1.840726	1.233123	1.349806	1.3564264	1.220934	1.495576	1.961265	2.320198	1.840726	1.407653	1.399468	1.509185	1.822664	0.8588924	0.8649878	0.8083876	1.3099526	1.594869	0.8913370	1.4017839	2.7208304	1.1304512	0.8591185	1.7044646	2.3373423	3.2462547	1.4829400	1.4669178	1.9262110	1.9478061	1.840726	4.0435816	6.369415	1.7090403	1.9220313	1.840726	1.840726	8.439765	0.9220850	0.9499497	0.8715802	1.840726	1.1794854	1.9728844	1.9328289	1.8816274	3.058041	1.840726	1.9276042	1.7778313	1.7919378	1.840726	4.271376	2.299725	1.827573	2.0628329	2.0515828	2.288069	2.1857948	2.0565722	1.9137654	4.212954	5.174635	0.6185336	0.6185336	0.6185336	0.9435040	3.580204	1.0743735	1.6936669	1.7305877	1.2527077	1.840726	1.840726	1.2423996	2.6429505	3.1070724	1.840726	1.427675	1.503699	1.637949	1.3626002	1.610369	1.4113166	1.2392097	1.473271	1.377759	1.7223454	0.6185336	0.6185336	0.6185336	0.6185336	0.7950615	2.0116403	2.3252926	2.0562239	4.2702504	6.349133	0.6194299	0.6749853	0.6185336	0.9990868	1.3653338	1.840726	0.6185336	1.6037259	1.840726	3.3720911	1.9206871	1.7970374	1.424347	2.384286	2.419466	2.4783149	2.3435192	2.3546650	4.3995534	6.777808	0.9926431	0.9783624	1.840726	1.194837	1.8399049	1.1798337	1.1028574	1.3111463	2.303653	2.297035
IgG1_TT	0.8701398	1.2501576	1.5097870	1.0545727	0.8577133	0.4765480	0.3163308	0.2909733	1.531315	2.122212	1.3427401	1.0794200	1.1575106	2.176220	2.229325	1.4807860	1.220480	1.3338184	1.7900947	2.0782944	1.1173956	1.2288626	1.5578732	1.220480	1.0421462	1.0333042	1.2597961	1.5264618	1.1482761	0.9772787	1.220480	0.7488087	0.7077321	1.283893	1.743913	1.1687746	1.2250922	1.1904148	1.696211	1.989217	0.7763966	0.9165667	1.220480	1.220480	1.898979	1.2699599	1.220480	1.1270550	1.4127322	1.6278597	1.1227269	0.8315535	1.220480	1.839827	1.933425	0.9609437	0.948464	1.316719	1.775996	1.862371	1.220480	1.252521	1.172359	1.695202	1.796712	1.5642990	1.5169029	1.5838150	1.8925396	1.990279	0.4346806	0.8648293	1.4585408	0.7371654	0.3856118	0.2509911	0.8825132	1.5242845	0.7488750	0.2704806	1.1896394	1.1808506	1.220480	1.7488328	2.139764	1.3497238	1.4064928	1.220480	1.220480	2.338853	1.1996497	1.2391596	1.3992175	1.220480	1.7262101	1.5159949	1.4720507	1.5046835	1.781731	1.220480	0.8326956	0.8105509	0.8352447	1.220480	1.896172	1.252277	1.313783	1.1827668	1.1598632	1.209319	1.2766968	1.3002487	1.2689170	1.797442	1.886446	0.3938107	0.5003920	0.5004185	1.7199965	2.345597	0.6668547	1.1114213	1.6183861	0.9285285	1.220480	1.220480	0.7532429	1.7114125	1.6944721	1.220480	1.074661	1.063247	1.008265	0.9275647	1.190545	0.9057848	1.0341805	1.007740	1.106671	0.8782137	0.2165660	0.2165660	0.2165660	0.9112296	0.9811155	0.2165660	0.5527989	0.3738624	1.2257162	1.575464	0.5290212	0.4379731	0.4349195	1.6411947	1.7970837	1.220480	0.2165660	0.6529359	1.220480	1.2788820	0.9100162	0.8970321	1.337722	1.648465	1.664131	0.9555827	0.9134175	0.9223922	1.9183964	2.134241	1.3880442	1.4130034	1.220480	1.551872	1.8971332	0.2492865	0.2555263	0.5511075	1.171053	1.436763
IgG1_DT	0.7777871	1.2997700	1.7299290	0.8949732	0.6012061	0.8630260	0.6756498	0.6381919	1.833550	2.328090	1.5570378	1.2701206	1.3622913	1.955028	1.946661	2.1263200	1.364343	1.8987979	2.2079557	2.4236854	1.2326145	1.3196262	1.8779831	1.364343	1.1246196	0.8615472	1.3958859	1.9763534	1.2351725	0.7957790	1.364343	0.1714252	0.1714252	1.167663	1.931215	0.6163040	0.6394551	0.6413627	1.774551	2.374224	0.4251378	0.5704172	1.364343	1.364343	2.380709	1.5658387	1.364343	1.3728263	2.3479024	2.5112172	1.9329175	1.5345371	1.364343	2.037921	2.103733	1.1487558	1.190202	1.527497	2.070333	2.112516	1.364343	1.673536	1.692525	2.403055	2.516513	1.2624530	1.1667272	1.3043330	2.4672188	2.659017	0.8961437	1.3891227	1.8750350	1.1556611	0.8176295	0.1714252	0.3103870	0.6960648	0.1714252	0.1714252	1.3208455	1.3178108	1.364343	2.0235714	2.727251	1.2735896	1.3898219	1.364343	1.364343	2.796878	0.1714252	0.1875097	0.4906845	1.364343	1.7024739	2.0599889	1.9564160	2.0019811	2.334897	1.364343	2.3527588	2.3529757	2.3282639	1.364343	2.480480	1.358961	1.625156	1.5544384	1.6372918	1.265139	0.8895031	0.9223222	0.8994312	1.797250	1.798638	0.2558959	0.3200598	0.2853765	1.5517486	2.742598	1.7382963	1.7528633	1.6372380	1.6625134	1.364343	1.364343	1.5400741	2.2492598	2.3376588	1.364343	1.296283	1.101430	1.196817	1.2740205	1.386303	1.2805180	1.4820862	1.473436	1.432489	1.1023693	0.1714252	0.1714252	0.1714252	0.2711194	0.8125681	0.3945732	0.6017622	0.5108488	1.7072040	1.865880	0.9396386	0.8292592	0.8143020	1.8856242	1.9694709	1.364343	0.6188825	0.8676915	1.364343	1.0318732	1.2339466	1.2079774	1.509852	1.713616	1.604364	0.1714252	0.1714252	0.1714252	0.5716874	1.528035	0.6122288	0.6698030	1.364343	1.190529	1.9447591	1.5206903	1.4573500	1.7250179	2.069552	2.141304
IgG1_OVA	-0.6993842	-1.3439736	-1.3243213	-1.3714867	-1.4029298	-1.6996770	-1.6996770	-1.6996770	-1.699677	-1.670198	-0.7662015	-1.5699725	-1.4206166	-1.184792	-1.302704	0.1083169	4.205786	-0.9135923	-0.0823083	0.1947865	7.6409698	5.4556556	6.0845227	4.205786	5.1412215	0.5563850	0.0120220	-0.0272822	0.4777768	-0.1844993	4.205786	-1.6996770	-1.6996770	-1.699677	-1.685920	-1.6996770	-0.4187727	-1.3186326	-1.682403	-1.487847	13.5994887	14.1772599	4.205786	4.205786	13.255577	38.6952271	4.205786	29.0204949	32.4674745	30.8992372	-1.6741290	-1.6996770	4.205786	-1.691816	-1.664303	16.1735015	17.454818	17.407653	19.139003	18.975893	4.205786	4.042256	1.974854	2.845442	2.562452	2.1360013	1.4815862	2.2087142	1.8274632	2.725564	18.1194720	15.3406653	12.8232303	11.8091817	16.0756545	-1.3341475	-1.3203912	-1.2909131	-1.2850175	-1.4147210	6.1391563	6.0015912	4.205786	3.4605739	3.662991	-1.6981039	-1.6391478	4.205786	4.205786	-1.470139	-1.4957080	-1.5232210	-1.4799862	4.205786	-1.3797603	-1.6996770	-1.6996770	-1.6996770	-1.699677	4.205786	-1.5762610	-1.6470084	-1.6273565	4.205786	-1.550713	4.365495	3.562846	3.7576916	3.4349387	5.132477	5.6914806	5.4301076	4.7953444	3.734131	4.695119	0.3244903	1.2206264	1.8534241	4.1841640	6.660329	4.7049451	5.0036569	3.7498524	3.2428281	4.205786	4.205786	-0.0371084	1.6156337	0.5740721	4.205786	3.945973	3.422038	4.288735	2.2908697	4.504811	4.1067821	3.2884600	3.173830	3.294553	4.4678428	-0.8214340	3.1755979	0.9570816	0.1592059	1.4228363	6.4382606	7.7824650	5.3416734	8.4486709	7.585944	10.1422696	8.6939092	8.1790242	6.2118483	8.3205190	4.205786	1.3990474	1.3047173	4.205786	0.5913458	-1.3227696	-1.1832395	-1.208787	-1.285430	-1.273639	0.0137906	-0.4637556	-0.4598250	0.2199416	1.568076	13.5715623	14.8646712	4.205786	15.662547	16.4722133	18.5926757	16.0752406	15.5367732	16.861326	15.363835
IgG2_PT	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	7.7989230	-1.0159950	-1.0159950	10.158643	19.936453	45.0793877	26.9206009	34.6031666	51.365120	56.254024	3.1744945	6.209667	0.3808351	4.5713243	7.3649840	-3.1460724	-3.1460724	-3.1460724	6.209667	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	6.209667	-3.1460724	-3.1460724	-3.146072	2.883360	-3.1460724	-3.1460724	-3.1460724	-3.146072	-2.447658	34.6612377	32.9152012	6.209667	6.209667	44.089836	-1.7144098	6.209667	-1.0159950	8.7618136	8.7618136	35.9999943	23.4285278	6.209667	20.634867	34.603167	-3.1460724	-3.146072	-3.146072	-2.243361	1.248714	6.209667	-3.146072	-3.146072	-3.146072	-2.447658	-3.1460724	1.7428317	-1.0508280	0.3460021	1.044417	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	21.5027466	24.9948215	17.3122559	21.1535397	22.2011604	-3.1460724	-3.1460724	6.209667	1.1097221	6.347834	8.7922864	2.5065520	6.209667	6.209667	15.776436	-3.1460724	-3.1460724	-3.1460724	6.209667	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.146072	6.209667	9.4907012	10.1891160	7.3954563	6.209667	15.776436	3.760733	3.431909	-0.0283578	2.5401755	5.652262	27.3278809	23.1373920	25.2326374	28.724712	39.200932	-1.7144098	-3.1112394	-1.7144098	-0.3175802	10.857058	-3.1460724	-3.1460724	-3.1460724	-3.1460724	6.209667	6.209667	-3.1460724	-3.1460724	-3.1460724	6.209667	6.561872	5.153059	7.341485	3.3878465	7.825629	0.1905924	5.0194195	5.234543	6.199418	7.1474952	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-3.1460724	-2.703959	-3.1460724	-3.1460724	-3.1460724	-1.0508280	-0.3524127	6.209667	-3.1460724	-3.1460724	6.209667	15.9154263	9.6296926	14.5185966	8.931278	22.201160	24.296406	22.0621700	20.6653404	23.4589996	21.3637543	24.157414	-3.1460724	-3.1460724	6.209667	1.393624	0.6952095	6.8360329	8.9312778	8.2328629	12.423352	28.486895

# knitr::kable(head(results$rnaseq), "html", align = "lccrr", booktabs=TRUE, border_left = T, 
#              border_right = T, caption = "RNA seq")  %>% 
#   kable_styling("striped", full_width = T) %>% 
#   scroll_box(width = "100%", height = "400px")
# 
# knitr::kable(head(results$cytof), "html", align = "lccrr", booktabs=TRUE, border_left = T, 
#              border_right = T, caption = "Cell Frequency")  %>% 
#   kable_styling("striped", full_width = T) %>% 
#   scroll_box(width = "100%", height = "400px")
# 
# olink <- head(results$olink)
# knitr::kable(, "html", align = "lccrr", booktabs=TRUE, border_left = T, 
#              border_right = T, caption = "Olink")  %>% 
#   kable_styling("striped", full_width = T) %>% 
#   scroll_box(width = "100%", height = "400px")

Imputation using MOFA for 2021 Dataset removed 20 samples, Batch corrected

2023-09-19

Load libraries

Load data and Generate Mofa object

Plot multiomic missing values

Setting data Options

Setting model Options

Setting training Options

Training Model

Impute Data

Add meta data to Mofa and Imputed objects

Mofa object

Imputed data

Results for Actual Data

Results for Imputed Data