Imputation using MOFA for 2021 Dataset removed 20 samples

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"]]$normalized_data)
normalizedData[["cytof"]] <- as.data.frame(df_source[["pbmc_cell_frequency"]]$normalized_data)
normalizedData[["rnaseq"]] <- as.data.frame(df_source[["pbmc_gene_expression"]]$raw_data)
normalizedData[["olink"]] <- as.data.frame(df_source[["plasma_cytokine_concentrations"]]$normalized_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 = 2,
  color_by = "infancy_vac",
  dodge = TRUE,
  add_violin = TRUE
)

plot_factor(plot_object,
  factors = 1,
  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 = 1,
  color_by = "infancy_vac",
  dodge = TRUE,
  add_violin = TRUE
)

plot_factor(plot_object,
  factors = 2,
  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	-2.7206037	-2.7339075	-2.6075215	-1.7316902	-1.0243731	-2.6123884	-1.2797942	-1.3862243	3.8377225	10.0128889	-1.2487521	0.1947069	0.7002504	9.8222008	13.2789631	-0.5879982	NA	0.5073454	0.2102280	1.2324011	-2.9778099	-2.9778099	-2.8159475	NA	12.5787315	-2.2150605	-2.4101825	-2.3924441	12.6785088	14.5454712	NA	-3.1312461	-3.1489844	-0.4438848	15.1570015	-2.6434646	-2.6545510	-2.6190743	4.6137419	10.1636305	-2.6168337	-2.6035299	NA	NA	6.4807272	-3.3352265	NA	-3.2177100	-3.2709250	-1.9361135	2.7778556	4.7889414	NA	2.6935985	4.4120016	-0.4678321	-1.1884530	-0.5321336	4.8270683	11.6785088	NA	-0.8363693	-0.6612029	4.9884644	8.2501049	-3.2864799	-3.2421341	-3.1977882	-1.4949057	-0.2997839	-3.5897832	-3.6341293	-3.6163909	-3.2571890	-2.5121779	6.8825006	6.0864916	6.2505713	6.2993517	7.3392630	-3.1064129	-2.9112911	NA	-1.7339075	0.7716358	-2.7272556	-2.6385636	NA	NA	3.654119	0.2816136	-0.2549717	0.2372677	NA	5.3193073	-3.3370118	-3.3059695	-3.3547502	-2.5365682	NA	-1.1995394	-1.1374552	-1.0642843	NA	0.8625448	NA	NA	NA	NA	NA	-2.3197162	-2.4150600	-2.3662796	-0.7831309	0.1503503	-3.5059583	-3.1911023	-3.2177100	-2.3662686	0.4918244	-3.4611802	-3.4301381	-3.4345727	-2.7649496	NA	NA	-3.5126209	-0.4061906	0.4629891	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-3.672289	-3.663420	-3.674507	-3.1246176	-2.5325999	-3.4549713	-3.4727097	-3.4505367	-3.0381198	-1.7520888	-2.8186307	-2.9760587	-2.7742848	-1.3219569	0.6026552	NA	-3.5232644	-3.4966569	NA	-0.3702712	-1.7361248	-1.8070782	-1.7627323	1.4789526	2.0022342	1.5920351	1.2217467	0.8847177	2.5742967	5.0931435	0.1968901	0.6625221	NA	2.1503270	5.8465576	-3.0221558	-2.9423332	-3.0221558	-2.4767013	-0.2971003
IgG_PRN	-1.8261276	-1.8245878	-1.8244693	-0.6782091	0.5725689	-1.2534909	-0.8305632	-0.6920090	0.5466471	1.5434155	-1.8573802	-1.7972440	-1.7775803	-0.2666726	0.7740037	-1.0708711	NA	-0.4372494	-0.7815619	0.0120941	-1.3778503	-1.3800220	-1.3903672	NA	1.6272233	-0.2237719	-0.2814206	-0.2297343	1.5707195	1.6354361	NA	-1.6300323	-1.6736242	-0.2428719	1.1765482	0.9071398	0.8091767	0.8697867	1.3824646	1.2538214	-0.6096514	-0.5745490	NA	NA	1.5522909	0.2103896	NA	0.7030880	0.2118111	1.4542577	-1.1055787	-0.6480223	NA	1.8756456	1.8453207	1.4203696	1.4838228	1.4368351	1.5792193	1.5533564	NA	-0.2076111	-0.1861311	0.9040205	1.3031781	0.1136813	0.0973344	0.1568389	1.4488790	1.6525445	0.5700023	0.3656261	0.4360681	1.9617827	1.9748523	-1.8573802	-1.8573802	-1.8573802	0.0576403	0.7875667	-0.8405834	-0.8214725	NA	-0.5936813	-0.1091564	-1.7444035	-1.7106040	NA	NA	1.342207	-1.8292761	-1.8219318	-1.8337774	NA	-0.4991720	0.0560503	-0.0849519	-0.0886636	1.1458070	NA	-0.7337755	-0.7254441	-0.7657192	NA	1.3583174	NA	NA	NA	NA	NA	1.1498165	1.0547359	1.1010127	1.3485858	1.2855673	-1.5715851	-1.0288982	-0.9906763	0.4413395	1.8595355	-1.0321164	-1.0406847	-0.8588941	1.6235905	NA	NA	-1.7236521	1.1188600	0.8395801	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.836067	-1.789711	-1.775457	1.0816257	1.4108152	0.1610241	0.1888218	0.2319791	1.3042438	1.7414653	-1.2165413	-1.3548191	-1.2465501	1.0558813	1.5490930	NA	-0.9083984	-0.9720488	NA	1.4566174	0.1068494	0.0891995	-0.0030779	0.9171681	1.0268979	-0.5409684	-0.5825861	-0.6020918	-0.0074421	1.2178283	-0.9367085	-0.8335726	NA	0.8645351	0.9923491	-1.0409508	-0.9016068	-0.9825519	0.0880544	1.1846817
IgG_FHA	3.1606541	3.1899357	3.1921883	6.9154835	12.1828318	-1.4155393	3.8281584	3.9137506	16.5285835	28.2739677	-4.5971208	-3.6409569	-3.7614629	13.4742632	26.2771721	-2.0338359	NA	0.5530996	1.2423487	17.7505341	1.4578037	1.9398270	3.1324978	NA	13.5793486	4.9412136	1.3305407	3.1201096	7.9966583	12.1681910	NA	-6.8804889	-3.3914480	-1.1502628	3.0888424	-6.3130145	-6.2161593	-0.9229083	1.7507467	4.2025356	-5.3386889	-4.5694780	NA	NA	4.4572048	1.7266245	NA	5.0377226	3.3483868	17.0331306	16.6817474	30.0522728	NA	32.1763306	44.0647469	-7.2613692	-7.2309613	-6.6926265	-7.1205916	-6.3862934	NA	-1.9027228	-0.6728868	-2.2056770	-0.4532733	-6.9403210	-6.2398100	-5.3095493	0.1379943	0.5051432	-0.1740942	-2.0864220	-3.3860841	2.5175800	21.4280014	14.7562513	10.3392038	6.4717512	11.0475969	13.5804739	-7.4340973	-7.3473783	NA	-7.4070678	-7.4228354	-7.4093204	-7.3991842	NA	NA	-7.323728	-7.3278165	-7.3886328	-7.3233118	NA	-7.2106895	-7.3789124	-7.2921934	-7.3811650	-7.3428731	NA	-7.4250875	-7.3811650	-7.3586402	NA	-7.3327374	NA	NA	NA	NA	NA	-6.4817190	-7.2870555	-3.8182867	-1.6198983	-0.7898712	-3.5181985	2.1793671	2.5082245	13.8526735	51.8897514	1.5096102	1.2945013	3.1167307	12.5443487	NA	NA	-7.3736882	-3.9027536	-5.2249403	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-7.449864	-7.449864	-7.449864	-7.4498644	-7.2658000	-6.8016529	-7.3839107	-7.4030566	0.4275761	3.0381622	-6.3040047	-7.4369860	-7.1047497	-7.1588087	1.7912903	NA	-7.4498644	-7.4498644	NA	-7.3661084	-7.3908854	-7.4235458	-7.3717394	-7.3751183	-7.3357000	-7.4228354	-7.3980584	-7.4239612	-7.3507566	-7.3755336	-7.1892166	-6.6711540	NA	-2.3475814	1.0175753	-7.3334479	-7.4134097	-7.3075447	-7.3402052	-7.1307273
IgG1_PT	-14.6289625	-12.2232771	-6.1128368	-13.4742336	-15.9761467	-16.8459854	-17.7088699	-17.7088699	19.2392921	73.8483505	-14.2478456	-17.2308960	-16.6535301	91.2655106	119.0752258	-9.3402472	NA	-14.9214373	-5.3949232	0.5230618	-15.3987818	-10.2987289	68.0303802	NA	-16.0723743	-14.9657583	31.9932175	63.1708984	-7.0751104	-15.3025541	NA	-14.7559834	-15.5258026	-5.9992895	134.6370697	-9.2701073	-7.4899006	-7.9710379	35.6199799	104.2782364	-17.7088699	-17.3965874	NA	NA	42.8842354	-17.7088699	NA	-17.7088699	-17.7088699	-16.9422131	-17.6158047	-17.7088699	NA	-13.0931168	-10.8798866	-6.8636456	-5.6608028	17.9630280	75.1221085	83.1570969	NA	-1.1870060	-3.1596670	37.6407547	73.7260361	-17.7088699	-17.7088699	-17.5937786	-6.8163090	1.5554752	-17.7082405	-16.8421936	-13.9072571	-17.2271023	-17.7082405	-1.2052383	-0.1467381	3.9429283	-5.8722677	-2.6967640	-15.6208439	-16.1019821	NA	12.8624687	62.8044968	-17.2567101	-16.1019821	NA	NA	48.610954	0.6420937	0.3534107	3.9619389	NA	62.5163193	-17.7088699	-17.7088699	-17.7088699	-13.8406372	NA	-16.6793461	-16.7755737	-16.3906631	NA	-4.5065784	NA	NA	NA	NA	NA	-17.3541241	-17.6428051	-17.5465794	-7.0577908	0.3517208	-17.7088699	-17.7088699	-17.7088699	-15.4988022	2.0145874	-17.1308746	-14.7251902	-6.0647230	-16.6016235	NA	NA	-17.7088699	-8.1162920	-3.6898317	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-17.708870	-17.708870	-17.708870	-17.7088699	-15.0145016	-17.5890408	-16.8729858	-17.3965874	-11.4361382	-4.8445606	-16.8239594	-17.4013233	-17.4201870	-12.5899534	-3.0153255	NA	-17.7088699	-15.2306252	NA	-4.4579601	-13.5995636	-14.0325871	-1.1862278	13.5365658	7.2817841	0.2136230	-2.7694273	-1.4222431	23.3031425	58.9072876	-2.7747574	0.0639515	NA	11.3706722	45.9644241	-17.7088699	-17.7088699	-17.6363106	-11.7712431	-7.4410095
IgG1_PRN	-1.8606243	-1.3805699	-0.0803713	-1.5256454	-1.9178909	-0.9444816	-1.2996578	-1.3817399	0.5233912	2.3891132	-1.8789743	-1.9185067	-1.9100091	0.1030049	0.8204389	-0.3747710	NA	-0.8501456	-0.1317881	0.4102128	-1.6699820	-0.7762531	0.7190216	NA	-1.7452290	-0.6506976	0.1863186	0.8772128	-0.1532785	-0.7234817	NA	-1.8806103	-1.8932952	-0.5325664	0.9705901	1.1403825	1.1321311	0.9925976	1.5912495	1.8743806	-0.6636022	-0.4469128	NA	NA	1.6626542	0.6637869	NA	0.2109497	0.8879273	1.4258025	-0.8177561	-1.4922091	NA	2.7197201	2.9754498	1.4853368	1.5291796	1.5498695	1.6703756	1.9575093	NA	0.2530577	0.0864918	0.8802812	1.4724057	0.3832316	0.2796590	0.3746107	1.4610755	1.9946404	0.0643963	0.6217299	1.3302965	0.5048566	-0.0362822	-1.9208467	-1.2854335	-0.2671344	-1.5657936	-1.9230634	0.3140187	0.2918510	NA	0.9468458	1.8096013	-1.8509674	-1.7643286	NA	NA	1.940514	-1.9060788	-1.9210421	-1.8695021	NA	-0.9239255	0.0132767	-0.0878328	-0.0971925	0.9614396	NA	-0.4516298	-0.5678872	-0.5082806	NA	1.6136017	NA	NA	NA	NA	NA	1.1701612	1.0871553	1.0470071	1.6326661	1.6125920	-1.6064345	-1.5274928	-1.5062486	0.5357683	2.7765558	-1.4062476	-0.2085747	0.9935935	-0.7664624	NA	NA	-1.3290035	1.4334645	1.2989187	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.923962	-1.884465	-1.907213	-0.6958696	-0.2416775	0.1046326	0.3830838	0.0897310	1.3909764	1.7628400	-1.2747295	-1.4175266	-1.4511477	0.7155628	1.4525163	NA	-1.2931414	-0.5973210	NA	0.7179639	-0.1774889	-0.1840161	0.3781819	0.8799114	0.8280022	-0.9579722	-1.0314952	-1.0234902	0.2952993	1.8365719	-0.9874728	-0.8760184	NA	0.7086046	1.6591690	-0.9281127	-0.7363620	-0.5468279	0.6420395	0.8355761
IgG1_FHA	-1.0175102	-0.7916421	1.2024431	-0.4618347	-1.0182596	-1.4156415	-1.5103362	-1.4616146	-1.1140677	-0.8229877	-1.5465651	-1.5260770	-1.3871582	-0.8629644	-0.9419183	-1.2095120	NA	-1.3769141	-1.2537360	-0.9484144	-1.2353828	-1.0077659	0.2732360	NA	-1.3573117	-1.1074576	-0.8763425	0.2584946	0.6045425	-1.0377483	NA	1.3358181	0.9552903	2.2282965	5.4116869	-0.6855285	-0.6395553	-0.4858952	0.1294954	0.6481924	-0.1693028	0.5385337	NA	NA	6.2177162	-1.5975353	NA	-1.6385114	-1.3594244	-1.1462989	-1.4836018	-1.5840431	NA	-0.8717092	-0.7043070	-0.6948097	-0.8891962	-0.4951763	0.1729333	0.6878827	NA	-0.6213161	-0.6330591	-0.4756511	-0.0259137	-1.4086062	-1.3998613	-1.4810638	-0.7614841	-0.3527229	-1.3620589	-0.6297367	1.2626581	-1.0190092	-1.4082818	-0.1954902	0.7124794	2.0164678	-0.5133046	-0.5362912	0.1226423	0.1536243	NA	3.1603682	6.4971685	-0.1889256	0.1166458	NA	NA	9.467434	-1.3179457	-1.2779691	-1.3904035	NA	-0.9486614	0.1896033	0.1321368	0.0586798	1.7464440	NA	0.1246412	-0.0902333	-0.0699952	NA	3.4871771	NA	NA	NA	NA	NA	0.4950590	0.3096673	0.1047871	3.4033601	4.7830544	-1.7534413	-1.7534413	-1.7534413	-1.2872165	2.4955738	-1.0994623	-0.2109812	-0.1580122	-0.8436117	NA	NA	-0.8584003	1.1509259	1.8167870	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.753441	-1.753441	-1.753441	-1.7534413	-1.5001825	0.2452049	0.6951921	0.3091676	3.4855626	6.4680700	-1.7521555	-1.6724521	-1.7534413	-1.2074736	-0.6820306	NA	-1.7534413	-0.3400170	NA	2.1970012	0.1147175	-0.0626791	-0.5973667	0.7798290	0.8302994	0.9147284	0.7213414	0.7373321	3.6710699	7.0830765	-1.2167182	-1.2372062	NA	-0.9266376	-0.0011783	-0.9481617	-1.0585972	-0.7597718	0.6641464	0.6546521
IgG1_TT	-0.3932271	0.0333105	0.3247223	-0.1862168	-0.4071748	-0.8350003	-1.0148304	-1.0432920	0.3488859	1.0121174	0.1372265	-0.1583279	-0.0706779	1.0727363	1.1323419	0.2921711	NA	0.1272126	0.6393440	0.9628234	-0.1157035	0.0094087	0.3786951	NA	-0.2001646	-0.2100888	0.0441289	0.3434384	-0.0810428	-0.2729728	NA	-0.5294109	-0.5755158	0.0711750	0.5875084	-0.0580350	0.0051767	-0.0337458	0.5339677	0.8628416	-0.4984458	-0.3411168	NA	NA	0.7615569	0.0555370	NA	-0.1048616	0.2157866	0.4572488	-0.1097195	-0.4365370	NA	0.6951646	0.8002193	-0.2913074	-0.3053148	0.1080202	0.6235191	0.7204676	NA	0.0359629	-0.0540117	0.5328351	0.6467713	0.3859074	0.3327094	0.4078125	0.7543294	0.8640337	-0.8819929	-0.3991876	0.2672029	-0.5424795	-0.9370685	-1.0881686	-0.3793390	0.3409946	-0.5293365	-1.0662934	-0.0346160	-0.0444807	NA	0.5930308	1.0318174	0.1450651	0.2087834	NA	NA	1.255279	-0.0233804	0.0209662	0.2006174	NA	0.5676389	0.3316902	0.2823665	0.3189942	0.6299565	NA	-0.4352550	-0.4601105	-0.4323939	NA	0.7584066	NA	NA	NA	NA	NA	0.0630984	0.0895336	0.0543662	0.6475909	0.7474899	-0.9278659	-0.8082374	-0.8082076	0.5606645	1.2628481	-0.6213973	-0.1224091	0.4466157	-0.3276907	NA	NA	-0.5244339	0.5510298	0.5320157	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.126808	-1.126808	-1.126808	-0.3471072	-0.2686663	-1.1268079	-0.7494151	-0.9502562	0.0058771	0.3984395	-0.7761036	-0.8782973	-0.8817247	0.4722162	0.6471885	NA	-1.1268079	-0.6370199	NA	0.0655512	-0.3484693	-0.3630428	0.1315942	0.4803762	0.4979600	-0.2973247	-0.3446515	-0.3345782	0.7833514	1.0256183	0.1880765	0.2160910	NA	0.3719597	0.7594855	-1.0900819	-1.0830784	-0.7513136	-0.0554780	0.2427586
IgG1_DT	-0.5234634	-0.0576270	0.3262624	-0.4188823	-0.6810507	-0.4473931	-0.6146144	-0.6480432	0.4187375	0.8600825	0.1719680	-0.0840871	-0.0018307	0.5271490	0.5196816	0.6800159	NA	0.4769671	0.7528707	0.9453956	-0.1175590	-0.0399066	0.4583912	NA	-0.2139375	-0.4487129	0.0281503	0.5461805	-0.1152762	-0.5074067	NA	-1.0646026	-1.0646026	-0.1755244	0.5058968	-0.6675767	-0.6469159	-0.6452135	0.3660847	0.9012548	-0.8381804	-0.7085277	NA	NA	0.9070421	0.1798222	NA	0.0075711	0.8777643	1.0235122	0.5074166	0.1518875	NA	0.6011256	0.6598581	-0.1923976	-0.1554092	0.1456046	0.6300513	0.6676973	NA	0.2759353	0.2928818	0.9269842	1.0282379	-0.0909301	-0.1763593	-0.0535549	0.9842464	1.1554142	-0.4178377	0.0221145	0.4557602	-0.1862350	-0.4879066	-1.0646026	-0.9405881	-0.5963954	-1.0646026	-1.0646026	-0.0388185	-0.0415268	NA	0.5883195	1.2163087	-0.0809914	0.0227385	NA	NA	1.278446	-1.0646026	-1.0502484	-0.7796841	NA	0.3017604	0.6208198	0.5283877	0.5690515	0.8661577	NA	0.8820983	0.8822917	0.8602380	NA	0.9960810	NA	NA	NA	NA	NA	-0.4237640	-0.3944751	-0.4149038	0.3863424	0.3875805	-0.9892181	-0.9319558	-0.9629084	0.1672478	1.2300047	0.3337297	0.3467298	0.2435416	0.2660983	NA	NA	0.1568290	0.7897319	0.8686224	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-1.064603	-1.064603	-1.064603	-0.9756321	-0.4924235	-0.8654574	-0.6805543	-0.7616888	0.3059818	0.4475899	-0.3790213	-0.4775279	-0.4908762	0.4652103	0.5400382	NA	-0.6652756	-0.4432296	NA	-0.2967077	-0.1163703	-0.1395460	0.1298575	0.3117039	0.2142034	-1.0646026	-1.0646026	-1.0646026	-0.7073942	0.1460845	-0.6712136	-0.6198324	NA	-0.1551181	0.5179845	0.1395302	0.0830030	0.3218796	0.6293548	0.6933879
IgG1_OVA	-2.3348942	-2.6417229	-2.6323683	-2.6548193	-2.6697865	-2.8110399	-2.8110399	-2.8110399	-2.8110399	-2.7970083	-2.3666995	-2.7493000	-2.6782057	-2.5659511	-2.6220784	-1.9504226	NA	-2.4368587	-2.0411615	-1.9092627	1.6351714	0.5949469	0.8942921	NA	0.4452744	-1.7371392	-1.9962599	-2.0149689	-1.7745574	-2.0898051	NA	-2.8110399	-2.8110399	-2.8110399	-2.8044918	-2.8110399	-2.2013209	-2.6296604	-2.8028176	-2.7102077	4.4714670	4.7464905	NA	NA	4.3077626	16.4172115	NA	11.8119717	13.4527588	12.7062654	-2.7988791	-2.8110399	NA	-2.8072982	-2.7942019	5.6967144	6.3066301	6.2841797	7.1083145	7.0306721	NA	-0.0778410	-1.0619383	-0.6475322	-0.7822375	-0.9852310	-1.2967372	-0.9506193	-1.1320974	-0.7045951	6.6230106	5.3002787	4.1019626	3.6192689	5.6501389	-2.6370456	-2.6304975	-2.6164656	-2.6136594	-2.6753993	0.9202979	0.8548162	NA	-0.3547256	-0.2583737	-2.8102915	-2.7822278	NA	NA	-2.701779	-2.7139494	-2.7270458	-2.7064660	NA	-2.6587577	-2.8110399	-2.8110399	-2.8110399	-2.8110399	NA	-2.7522933	-2.7859697	-2.7766151	NA	-2.7401323	NA	NA	NA	NA	NA	0.7072012	0.5827861	0.2806344	-0.2245107	0.2329264	-1.8475227	-1.4209559	-1.1197398	-0.0102916	1.1683798	0.2376037	0.3797925	-0.2170269	-0.4583740	NA	NA	-2.0196462	-1.2329297	-1.7287201	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-2.392991	-0.490376	-1.546405	-1.9261992	-1.3247025	1.0626738	1.7025242	0.5406907	2.0196428	1.6089787	2.8258071	2.1363778	1.8912892	0.9548998	1.9586415	NA	-1.3360262	-1.3809280	NA	-1.7204977	-2.6316297	-2.5652125	-2.5773733	-2.6138561	-2.6082432	-1.9954178	-2.2227330	-2.2208622	-1.8972886	-1.2555674	4.4581738	5.0737023	NA	5.4534969	5.8389034	6.8482580	5.6499424	5.3936281	6.0241232	5.3113079
IgG2_PT	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.8452635	1.3326750	-6.0590849	-6.0590849	3.3114204	11.5106134	32.5942497	17.3671799	23.8093987	37.8651581	41.9647560	-2.5451450	NA	-4.8877716	-1.3738317	0.9687948	-7.8452635	-7.8452635	-7.8452635	NA	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.8452635	NA	-7.8452635	-7.8452635	-7.8452635	-2.7892766	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.2596068	23.8580971	22.3939552	NA	NA	31.7644615	-6.6447411	NA	-6.0590849	2.1401072	2.1401072	24.9807129	14.4388952	NA	12.0962706	23.8093987	-7.8452635	-7.8452635	-7.8452635	-7.0882936	-4.1600108	NA	-7.8452635	-7.8452635	-7.8452635	-7.2596068	-7.8452635	-3.7456675	-6.0882940	-4.9169807	-4.3313241	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.8452635	12.8240309	15.7523127	9.3100901	12.5312014	13.4096861	-7.8452635	-7.8452635	NA	-4.2765622	0.1158619	2.1656599	-3.1052489	NA	NA	8.022225	-7.8452635	-7.8452635	-7.8452635	NA	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.8452635	NA	2.7513170	3.3369732	0.9943476	NA	8.0222254	NA	NA	NA	NA	NA	17.7087059	14.1947641	15.9517336	18.8800163	27.6648636	-6.6447411	-7.8160543	-6.6447411	-5.4734278	3.8970776	-7.8452635	-7.8452635	-7.8452635	-7.8452635	NA	NA	-7.8452635	-7.8452635	-7.8452635	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	-7.845263	-7.845263	-7.845263	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.8452635	-7.4745293	-7.8452635	-7.8452635	-7.8452635	-6.0882940	-5.5026369	NA	-7.8452635	-7.8452635	NA	8.1387777	2.8678684	6.9674644	2.2822113	13.4096861	15.1666574	13.2931356	12.1218214	14.4644480	12.7074785	15.0501051	-7.8452635	-7.8452635	NA	-4.0384955	-4.6241522	0.5252419	2.2822113	1.6965551	5.2104950	18.6805954

# 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	0.9999998	0.9866960	1.1130819	1.9889133	2.6962304	1.1082151	2.4408092	2.3343792	7.5583260	13.7334924	2.4718513	3.9153104	4.4208539	13.5428042	16.9995666	3.1326053	3.720604	4.2279489	3.9308314	4.9530046	0.7427936	0.7427936	0.9046559	3.720604	16.2993350	1.5055430	1.3104210	1.3281593	16.399112	18.2660747	3.720604	0.5893574	0.5716190	3.2767186	18.8776050	1.0771389	1.0660524	1.1015291	8.3343453	13.8842340	1.1037698	1.1170735	3.720604	3.720604	10.201331	0.3853769	3.720604	0.5028934	0.4496784	1.7844900	6.4984591	8.5095448	3.720604	6.414202	8.1326051	3.2527714	2.5321505	3.188470	8.5476718	15.399112	3.720604	2.884234	3.059401	8.709068	11.970708	0.4341235	0.4784694	0.5228152	2.225698	3.420819	0.1308203	0.0864742	0.1042125	0.4634144	1.2084255	10.6031041	9.8070951	9.9711747	10.0199552	11.0598664	0.6141906	0.8093123	3.720604	1.9866960	4.4922392	0.9933479	1.0820398	3.720604	3.720604	7.3747227	4.0022171	3.4656317	3.9578712	3.720604	9.0399108	0.3835917	0.4146340	0.3658533	1.1840353	3.720604	2.5210640	2.5831482	2.6563191	3.720604	4.5831482	3.066388	3.726390	2.760294	2.870139	3.066015	1.4008873	1.3055434	1.3543239	2.937473	3.870954	0.2146451	0.5295012	0.5028934	1.354335	4.212428	0.2594233	0.2904654	0.2860308	0.9556539	3.720604	3.720604	0.2079825	3.314413	4.183593	3.720604	2.4481166	3.611983	2.6048893	3.5845765	3.226403	2.657566	3.013288	3.191063	3.388986	2.7732334	0.0483141	0.0571833	0.0460968	0.5959859	1.1880035	0.2656322	0.2478938	0.2700667	0.6824837	1.968515	0.9019728	0.7445447	0.9463186	2.3986466	4.323259	3.720604	0.1973391	0.2239466	3.720604	3.350332	1.9844787	1.9135252	1.9578712	5.1995561	5.7228377	5.3126385	4.9423501	4.6053212	6.2949002	8.8137469	3.9174936	4.3831255	3.720604	5.870930	9.567161	0.6984477	0.7782702	0.6984477	1.2439022	3.4235032
IgG_PRN	0.1105806	0.1121204	0.1122389	1.2584991	2.5092771	0.6832173	1.1061450	1.2446992	2.4833553	3.4801238	0.0793281	0.1394643	0.1591280	1.6700356	2.7107120	0.8658371	1.936708	1.4994588	1.1551464	1.9488024	0.5588579	0.5566862	0.5463411	1.936708	3.5639315	1.7129363	1.6552876	1.7069739	3.507428	3.5721443	1.936708	0.3066759	0.2630841	1.6938363	3.1132565	2.8438480	2.7458849	2.8064950	3.3191729	3.1905296	1.3270568	1.3621593	1.936708	1.936708	3.488999	2.1470978	1.936708	2.6397963	2.1485193	3.3909659	0.8311296	1.2886859	1.936708	3.812354	3.7820289	3.3570778	3.4205310	3.373543	3.5159276	3.490065	1.936708	1.729097	1.750577	2.840729	3.239886	2.0503895	2.0340426	2.0935471	3.385587	3.589253	2.5067105	2.3023343	2.3727763	3.8984909	3.9115605	0.0793281	0.0793281	0.0793281	1.9943485	2.7242749	1.0961248	1.1152357	1.936708	1.3430269	1.8275518	0.1923047	0.2261043	1.936708	1.936708	3.2789154	0.1074321	0.1147764	0.1029308	1.936708	1.4375362	1.9927585	1.8517563	1.8480446	3.0825152	1.936708	1.2029327	1.2112641	1.1709890	1.936708	3.2950256	1.803961	1.825191	1.444538	1.334283	1.717867	3.0865247	2.9914441	3.0377209	3.285294	3.222275	0.3651232	0.9078100	0.9460319	2.378048	3.796244	0.9045918	0.8960235	1.0778141	3.5602987	1.936708	1.936708	0.2130561	3.055568	2.776288	1.936708	1.6322304	1.608658	1.7793506	1.6503232	2.048088	1.899003	1.580604	1.749615	1.885592	1.6198284	0.1006415	0.1469973	0.1612515	3.0183339	3.3475235	2.0977323	2.1255300	2.1686873	3.2409520	3.678174	0.7201669	0.5818892	0.6901581	2.9925895	3.485801	1.936708	1.0283098	0.9646595	1.936708	3.393326	2.0435576	2.0259078	1.9336303	2.8538764	2.9636061	1.3957398	1.3541222	1.3346164	1.9292661	3.1545365	0.9999998	1.1031356	1.936708	2.801243	2.929057	0.8957574	1.0351014	0.9541563	2.0247626	3.1213899
IgG_FHA	10.6423745	10.6716561	10.6739087	14.3972039	19.6645522	6.0661812	11.3098788	11.3954711	24.0103040	35.7556882	2.8845997	3.8407636	3.7202575	20.9559836	33.7588925	5.4478846	7.481720	8.0348201	8.7240691	25.2322545	8.9395242	9.4215474	10.6142182	7.481720	21.0610690	12.4229341	8.8122611	10.6018300	15.478379	19.6499114	7.481720	0.6012316	4.0902724	6.3314576	10.5705628	1.1687059	1.2655611	6.5588121	9.2324672	11.6842561	2.1430316	2.9122424	7.481720	7.481720	11.938925	9.2083449	7.481720	12.5194430	10.8301072	24.5148511	24.1634679	37.5339932	7.481720	39.658051	51.5464673	0.2203512	0.2507591	0.789094	0.3611288	1.095427	7.481720	5.578998	6.808834	5.276043	7.028447	0.5413995	1.2419105	2.1721711	7.619715	7.986864	7.3076262	5.3952985	4.0956364	9.9993005	28.9097219	22.2379718	17.8209243	13.9534717	18.5293174	21.0621943	0.0476232	0.1343422	7.481720	0.0746527	0.0588851	0.0724001	0.0825362	7.481720	7.481720	0.1579928	0.1539040	0.0930877	0.1584086	7.481720	0.2710309	0.1028080	0.1895270	0.1005554	0.1388474	7.481720	0.0566330	0.1005554	0.1230803	7.481720	0.1489830	7.097158	7.719633	7.113578	7.461094	6.314072	1.0000014	0.1946650	3.6634338	5.861822	6.691849	3.9635220	9.6610875	9.9899449	21.334394	59.371472	8.9913306	8.7762218	10.5984511	20.0260692	7.481720	7.481720	0.1080322	3.578967	2.256780	7.481720	7.6473607	7.255055	7.5490898	8.0501546	7.302556	7.769903	7.507216	7.781509	7.811458	7.0144419	0.0318561	0.0318561	0.0318561	0.0318561	0.2159204	0.6800675	0.0978098	0.0786638	7.9092965	10.519883	1.1777158	0.0447345	0.3769708	0.3229117	9.273011	7.481720	0.0318561	0.0318561	7.481720	0.115612	0.0908351	0.0581746	0.1099811	0.1066022	0.1460204	0.0588851	0.0836620	0.0577593	0.1309638	0.1061869	0.2925038	0.8105664	7.481720	5.134139	8.499296	0.1482725	0.0683107	0.1741757	0.1415153	0.3509932
IgG1_PT	3.1949987	5.6006842	11.7111244	4.3497276	1.8478146	0.9779758	0.1150913	0.1150913	37.0632534	91.6723118	3.5761156	0.5930653	1.1704311	109.0894718	136.8991871	8.4837141	17.823961	2.9025240	12.4290380	18.3470230	2.4251795	7.5252323	85.8543415	17.823961	1.7515869	2.8582029	49.8171787	80.9948597	10.748851	2.5214071	17.823961	3.0679779	2.2981586	11.8246717	152.4610310	8.5538540	10.3340607	9.8529234	53.4439411	122.1021976	0.1150913	0.4273739	17.823961	17.823961	60.708197	0.1150913	17.823961	0.1150913	0.1150913	0.8817482	0.2081566	0.1150913	17.823961	4.730844	6.9440746	10.9603157	12.1631584	35.786989	92.9460697	100.981058	17.823961	16.636955	14.664294	55.464716	91.549997	0.1150913	0.1150913	0.2301826	11.007652	19.379437	0.1157207	0.9817677	3.9167042	0.5968590	0.1157207	16.6187229	17.6772232	21.7668896	11.9516935	15.1271973	2.2031174	1.7219791	17.823961	30.6864300	80.6284580	0.5672512	1.7219791	17.823961	17.823961	66.4349155	18.4660549	18.1773720	21.7859001	17.823961	80.3402805	0.1150913	0.1150913	0.1150913	3.9833241	17.823961	1.1446152	1.0483875	1.4332981	17.823961	13.3173828	17.436308	17.878274	17.585362	17.518275	17.980403	0.4698372	0.1811562	0.2773819	10.766170	18.175682	0.1150913	0.1150913	0.1150913	2.325159	19.838549	0.6930866	3.0987711	11.7592382	1.2223377	17.823961	17.823961	0.1150913	9.707669	14.134130	17.823961	16.7680711	18.091194	16.9300920	17.6268101	17.529355	16.998802	17.487881	17.447013	17.655288	17.3620007	0.1150913	0.1150913	0.1150913	0.1150913	2.8094597	0.2349205	0.9509754	0.4273739	6.3878231	12.979401	1.0000019	0.4226379	0.4037743	5.2340078	14.808636	17.823961	0.1150913	2.5933361	17.823961	13.366001	4.2243977	3.7913742	16.6377335	31.3605270	25.1057453	18.0375843	15.0545340	16.4017181	41.1271038	76.7312489	15.0492039	17.8879128	17.823961	29.194633	63.788385	0.1150913	0.1150913	0.1876507	6.0527182	10.3829517
IgG1_PRN	0.0674883	0.5475427	1.8477414	0.4024673	0.0102217	0.9836311	0.6284548	0.5463728	2.4515039	4.3172258	0.0491383	0.0096059	0.0181035	2.0311176	2.7485515	1.5533416	1.928113	1.0779670	1.7963245	2.3383254	0.2581307	1.1518595	2.6471342	1.928113	0.1828836	1.2774150	2.1144313	2.8053254	1.774834	1.2046310	1.928113	0.0475023	0.0348175	1.3955462	2.8987027	3.0684952	3.0602437	2.9207102	3.5193621	3.8024932	1.2645104	1.4811999	1.928113	1.928113	3.590767	2.5918995	1.928113	2.1390623	2.8160399	3.3539151	1.1103566	0.4359035	1.928113	4.647833	4.9035624	3.4134494	3.4572922	3.477982	3.5984882	3.885622	1.928113	2.181170	2.014604	2.808394	3.400518	2.3113443	2.2077717	2.3027233	3.389188	3.922753	1.9925089	2.5498425	3.2584091	2.4329692	1.8918304	0.0072659	0.6426791	1.6609782	0.3623190	0.0050492	2.2421314	2.2199637	1.928113	2.8749584	3.7377139	0.0771452	0.1637840	1.928113	1.928113	3.8686267	0.0220338	0.0070705	0.0586106	1.928113	1.0041871	1.9413893	1.8402798	1.8309201	2.8895522	1.928113	1.4764829	1.3602254	1.4198320	1.928113	3.5417143	1.619142	1.873199	1.475811	1.435509	1.773188	3.0982739	3.0152680	2.9751197	3.560779	3.540705	0.3216782	0.4006199	0.4218640	2.463881	4.704668	0.5218650	1.7195380	2.9217061	1.1616502	1.928113	1.928113	0.5991092	3.361577	3.227031	1.928113	1.4065096	1.467446	1.5128440	1.5497880	2.109586	1.789485	1.797402	1.847341	1.849795	1.5973774	0.0041511	0.0436475	0.0209000	1.2322431	1.6864351	2.0327452	2.3111964	2.0178436	3.3190891	3.690953	0.6533831	0.5105860	0.4769650	2.6436754	3.380629	1.928113	0.6349713	1.3307916	1.928113	2.646077	1.7506237	1.7440965	2.3062946	2.8080240	2.7561148	0.9701404	0.8966174	0.9046224	2.2234119	3.7646846	0.9406399	1.0520942	1.928113	2.636717	3.587282	0.9999999	1.1917506	1.3812847	2.5701522	2.7636887
IgG1_FHA	1.0819523	1.3078204	3.3019056	1.6376278	1.0812029	0.6838210	0.5891263	0.6378479	0.9853948	1.2764748	0.5528975	0.5733855	0.7123044	1.2364981	1.1575443	0.8899505	2.099463	0.7225484	0.8457265	1.1510481	0.8640797	1.0916966	2.3726985	2.099463	0.7421508	0.9920049	1.2231200	2.3579571	2.704005	1.0617142	2.099463	3.4352806	3.0547528	4.3277590	7.5111494	1.4139340	1.4599072	1.6135674	2.2289579	2.7476549	1.9301597	2.6379962	2.099463	2.099463	8.317179	0.5019273	2.099463	0.4609511	0.7400382	0.9531636	0.6158607	0.5154194	2.099463	1.227753	1.3951555	1.4046528	1.2102664	1.604286	2.2723958	2.787345	2.099463	1.478146	1.466403	1.623811	2.073549	0.6908563	0.6996012	0.6183987	1.337978	1.746740	0.7374036	1.4697258	3.3621206	1.0804533	0.6911807	1.9039723	2.8119419	4.1159303	1.5861579	1.5631713	2.2221048	2.2530868	2.099463	5.2598307	8.5966311	1.9105369	2.2161083	2.099463	2.099463	11.5668964	0.7815168	0.8214934	0.7090590	2.099463	1.1508011	2.2890658	2.2315993	2.1581423	3.8459065	2.099463	2.2241037	2.0092292	2.0294673	2.099463	5.5866396	2.614410	2.028148	2.123721	2.041209	2.610730	2.5945215	2.4091299	2.2042496	5.502823	6.882517	0.3460212	0.3460212	0.3460212	0.812246	4.595036	1.0000002	1.8884813	1.9414504	1.2558508	2.099463	2.099463	1.2410622	3.250388	3.916250	2.099463	1.5925456	1.755451	1.8860819	1.4200241	1.935972	1.499008	1.285893	1.587233	1.508129	2.0053672	0.3460212	0.3460212	0.3460212	0.3460212	0.5992800	2.3446674	2.7946546	2.4086301	5.5850251	8.567533	0.3473070	0.4270104	0.3460212	0.8919889	1.417432	2.099463	0.3460212	1.7594455	2.099463	4.296464	2.2141800	2.0367835	1.5020958	2.8792915	2.9297619	3.0141909	2.8208039	2.8367946	5.7705324	9.1825390	0.8827443	0.8622563	2.099463	1.172825	2.098284	1.1513008	1.0408653	1.3396907	2.7636089	2.7541146
IgG1_TT	0.9040976	1.3306352	1.6220469	1.1111078	0.8901499	0.4623243	0.2824943	0.2540326	1.6462106	2.3094420	1.4345511	1.1389967	1.2266468	2.3700609	2.4296665	1.5894958	1.297325	1.4245373	1.9366686	2.2601480	1.1816212	1.3067334	1.6760198	1.297325	1.0971601	1.0872358	1.3414536	1.6407630	1.216282	1.0243518	1.297325	0.7679138	0.7218089	1.3684996	1.8848331	1.2392896	1.3025013	1.2635789	1.8312924	2.1601663	0.7988788	0.9562078	1.297325	1.297325	2.058882	1.3528616	1.297325	1.1924630	1.5131112	1.7545735	1.1876051	0.8607877	1.297325	1.992489	2.0975440	1.0060172	0.9920099	1.405345	1.9208437	2.017792	1.297325	1.333288	1.243313	1.830160	1.944096	1.6832321	1.6300341	1.7051371	2.051654	2.161358	0.4153317	0.8981370	1.5645275	0.7548452	0.3602562	0.2091560	0.9179857	1.6383193	0.7679882	0.2310313	1.2627087	1.2528440	1.297325	1.8903555	2.3291421	1.4423897	1.5061080	1.297325	1.297325	2.5526035	1.2739443	1.3182908	1.4979421	1.297325	1.8649635	1.6290148	1.5796912	1.6163188	1.9272811	1.297325	0.8620697	0.8372142	0.8649307	1.297325	2.0557313	1.257185	1.387983	1.253411	1.202244	1.304506	1.3604231	1.3868582	1.3516909	1.944916	2.044815	0.3694588	0.4890872	0.4891170	1.857989	2.560173	0.6759274	1.1749156	1.7439404	0.9696339	1.297325	1.297325	0.7728908	1.848354	1.829340	1.297325	0.9178357	1.101571	0.9018899	0.9921314	1.174128	0.975423	1.052726	1.047708	1.069656	0.9008104	0.1705167	0.1705167	0.1705167	0.9502174	1.0286584	0.1705167	0.5479096	0.3470685	1.3032018	1.695764	0.5212211	0.4190273	0.4155999	1.7695409	1.944513	1.297325	0.1705167	0.6603048	1.297325	1.362876	0.9488554	0.9342819	1.4289188	1.7777009	1.7952846	1.0000000	0.9526731	0.9627465	2.0806761	2.3229430	1.4854012	1.5134157	1.297325	1.669284	2.056810	0.2072427	0.2142463	0.5460110	1.2418467	1.5400833
IgG1_DT	0.8555583	1.3213947	1.7052840	0.9601393	0.6979709	0.9316285	0.7644072	0.7309784	1.7977592	2.2391042	1.5509896	1.2949345	1.3771909	1.9061706	1.8987032	2.0590376	1.379022	1.8559887	2.1318923	2.3244172	1.2614627	1.3391150	1.8374128	1.379022	1.1650841	0.9303088	1.4071720	1.9252021	1.263745	0.8716149	1.379022	0.3144190	0.3144190	1.2034973	1.8849185	0.7114449	0.7321058	0.7338082	1.7451063	2.2802764	0.5408413	0.6704939	1.379022	1.379022	2.286064	1.5588439	1.379022	1.3865927	2.2567860	2.4025339	1.8864383	1.5309092	1.379022	1.980147	2.0388798	1.1866241	1.2236124	1.524626	2.0090729	2.046719	1.379022	1.654957	1.671903	2.306006	2.407260	1.2880915	1.2026623	1.3254668	2.363268	2.534436	0.9611840	1.4011362	1.8347819	1.1927867	0.8911151	0.3144190	0.4384335	0.7826263	0.3144190	0.3144190	1.3402032	1.3374949	1.379022	1.9673412	2.5953304	1.2980303	1.4017601	1.379022	1.379022	2.6574677	0.3144190	0.3287733	0.5993376	1.379022	1.6807821	1.9998415	1.9074093	1.9480731	2.2451793	1.379022	2.2611200	2.2613133	2.2392596	1.379022	2.3751026	1.294625	1.576297	1.507392	1.549852	1.287450	0.9552577	0.9845465	0.9641178	1.765364	1.766602	0.3898036	0.4470658	0.4161133	1.546269	2.609026	1.7127514	1.7257514	1.6225632	1.6451199	1.379022	1.379022	1.5358506	2.168753	2.247644	1.379022	1.1411844	1.154820	1.0985139	1.2933320	1.345069	1.306312	1.441670	1.451770	1.376302	1.1187486	0.3144190	0.3144190	0.3144190	0.4033896	0.8865981	0.5135643	0.6984673	0.6173328	1.6850034	1.826612	1.0000004	0.9014938	0.8881454	1.8442320	1.919060	1.379022	0.7137460	0.9357921	1.379022	1.082314	1.2626513	1.2394756	1.5088792	1.6907256	1.5932250	0.3144190	0.3144190	0.3144190	0.6716275	1.5251062	0.7078080	0.7591892	1.379022	1.223904	1.897006	1.5185518	1.4620247	1.7009013	2.0083765	2.0724095
IgG1_OVA	0.5126290	0.2058003	0.2151549	0.1927040	0.1777368	0.0364833	0.0364833	0.0364833	0.0364833	0.0505149	0.4808238	0.0982232	0.1693175	0.2815721	0.2254448	0.8971006	2.847523	0.4106646	0.8063617	0.9382606	4.4826946	3.4424701	3.7418153	2.847523	3.2927976	1.1103840	0.8512633	0.8325543	1.072966	0.7577181	2.847523	0.0364833	0.0364833	0.0364833	0.0430315	0.0364833	0.6462023	0.2178628	0.0447056	0.1373155	7.3189902	7.5940137	2.847523	2.847523	7.155286	19.2647347	2.847523	14.6594949	16.3002820	15.5537887	0.0486441	0.0364833	2.847523	0.040225	0.0533214	8.5442376	9.1541533	9.131703	9.9558377	9.878195	2.847523	2.769682	1.785585	2.199991	2.065286	1.8622922	1.5507860	1.8969039	1.715426	2.142928	9.4705338	8.1478019	6.9494858	6.4667921	8.4976621	0.2104776	0.2170258	0.2310576	0.2338638	0.1721239	3.7678211	3.7023394	2.847523	2.4927976	2.5891495	0.0372317	0.0652955	2.847523	2.847523	0.1457443	0.1335738	0.1204774	0.1410573	2.847523	0.1887655	0.0364833	0.0364833	0.0364833	0.0364833	2.847523	0.0952299	0.0615535	0.0709081	2.847523	0.1073909	2.214133	2.274892	2.079239	1.884082	2.975326	3.5547245	3.4303093	3.1281576	2.623013	3.080450	1.0000005	1.4265673	1.7277834	2.837232	4.015903	3.0851269	3.2273157	2.6304963	2.3891492	2.847523	2.847523	0.8278770	1.614594	1.118803	2.847523	2.5089412	2.706547	2.7940180	2.2188472	3.010494	3.284474	2.756807	2.562444	2.572397	3.2859900	0.4545326	2.3571472	1.3011183	0.9213240	1.5228207	3.9101970	4.5500474	3.3882139	4.8671660	4.456502	5.6733303	4.9839010	4.7388124	3.8024230	4.806165	2.847523	1.5114970	1.4665952	2.847523	1.127026	0.2158935	0.2823107	0.2701499	0.2336671	0.2392800	0.8521054	0.6247902	0.6266611	0.9502347	1.5919558	7.3056970	7.9212255	2.847523	8.301020	8.686427	9.6957812	8.4974656	8.2411513	8.8716464	8.1588311
IgG2_PT	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	10.1779385	2.7861786	2.7861786	12.1566839	20.3558769	41.4395132	26.2124434	32.6546621	46.7104216	50.8100195	6.3001184	8.845263	3.9574919	7.4714317	9.8140583	1.0000000	1.0000000	1.0000000	8.845263	1.0000000	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	8.845263	1.0000000	1.0000000	1.0000000	6.0559869	1.0000000	1.0000000	1.0000000	1.0000000	1.5856566	32.7033606	31.2392187	8.845263	8.845263	40.609725	2.2005224	8.845263	2.7861786	10.9853706	10.9853706	33.8259764	23.2841587	8.845263	20.941534	32.6546621	1.0000000	1.0000000	1.000000	1.7569699	4.685253	8.845263	1.000000	1.000000	1.000000	1.585657	1.0000000	5.0995960	2.7569695	3.928283	4.513939	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	21.6692944	24.5975761	18.1553535	21.3764648	22.2549496	1.0000000	1.0000000	8.845263	4.5687013	8.9611254	11.0109234	5.7400146	8.845263	8.845263	16.8674889	1.0000000	1.0000000	1.0000000	8.845263	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	8.845263	11.5965805	12.1822367	9.8396111	8.845263	16.8674889	8.172595	8.960496	8.625087	8.462883	9.635943	26.5539694	23.0400276	24.7969971	27.725280	36.510127	2.2005224	1.0292091	2.2005224	3.371836	12.742341	1.0000000	1.0000000	1.0000000	1.0000000	8.845263	8.845263	1.0000000	1.000000	1.000000	8.845263	6.7933706	9.963354	7.2074127	8.4792544	8.300881	7.009596	8.259775	8.239843	9.028661	7.9618904	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.370734	1.0000000	1.0000000	1.0000000	2.7569695	3.342627	8.845263	1.0000000	1.0000000	8.845263	16.984041	11.7131319	15.8127279	11.1274748	22.2549496	24.0119209	22.1383991	20.9670849	23.3097115	21.5527420	23.8953686	1.0000000	1.0000000	8.845263	4.806768	4.221111	9.3705053	11.1274748	10.5418186	14.0557585	27.5258589

# 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

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