Imputation using MOFA for 2020 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$dataset=="2020_dataset", ]$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): 190 
##  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): 190 
##  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): 190 
##  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 = 1,
  color_by = "infancy_vac",
  dodge = TRUE,
  add_violin = TRUE
)

plot_factor(plot_object,
  factors = 4,
  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 = 4,
  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 <- 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/imputedData2020_removed20Samples.RDS"))

saveRDS(imputationTestingList, file = here("./results/2020Data_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
	1	102	103	104	105	106	114	115	116	117	118	131	132	133	134	135	138	139	140	141	142	146	147	148	149	150	153	154	155	156	157	160	161	162	163	164	167	168	169	170	171	174	175	176	177	178	181	182	183	184	185	19	191	192	193	194	195	2	20	201	202	203	204	205	208	209	21	210	211	212	22	223	224	225	226	227	23	241	242	243	244	245	248	249	250	251	252	255	256	257	258	259	27	274	275	276	277	278	28	281	282	283	284	285	29	293	294	295	296	297	3	30	31	324	325	326	327	328	332	333	334	335	336	342	343	344	345	346	349	350	351	352	353	355	356	357	358	359	360	361	362	363	364	369	37	370	371	372	373	38	385	386	387	388	389	39	397	398	399	4	40	400	401	405	406	407	408	409	41	45	46	47	48	49	5	70	71	72	73	74	77	78	79	80	81	87	88	89	90	91
IgG_PT	0.1826267	-1.9731314	-1.5442266	-1.2767997	-1.3585975	-0.3898735	-3.2521157	NA	-2.4566026	-2.672514	1.9225113	6.4159546	4.4645405	4.2674780	5.3076077	NA	-1.3337350	-2.0307870	-2.3919148	6.8381987	6.4143820	-0.0759842	0.0939107	0.6324399	5.6495485	5.7209883	1.3028891	1.5587971	-2.2007377	NA	3.7090943	2.2879384	-3.0276911	-3.3883157	-2.1580284	-1.6266918	-0.0730023	-3.2379289	-3.4471817	3.9219763	1.3502510	-3.2330735	-3.2091913	-3.2654362	0.8018954	3.6518672	-3.3486199	NA	-3.3910859	-3.3980913	-2.2663865	-2.4579990	-2.8729546	NA	-3.2352281	-3.0539486	-2.2953997	NA	-2.8416803	-2.8993349	-3.3473489	NA	-2.5221605	NA	-2.8481388	-3.3473489	-3.0380459	-3.0507536	-3.5251517	-1.1584740	2.8696930	NA	-2.7711215	-2.5518456	-1.6389232	0.5382321	3.4871819	-3.4328485	NA	NA	NA	-1.7556313	-1.908591	-2.0574529	-1.9634216	4.3981800	11.3092699	-2.9979339	NA	NA	-2.9265153	NA	NA	-2.2680488	-1.7747551	-1.2873168	0.4490650	0.6608465	NA	-2.4969833	-3.0312843	NA	-2.0957792	-1.2979672	-1.9269168	-2.8473306	-3.0129611	-3.0728142	1.3643219	3.0492804	-1.2988312	-2.1331000	4.3421755	-2.3860905	-2.4691920	-2.4716454	4.4164152	5.3093653	-2.5500493	NA	-2.7532444	-2.7812879	-2.1095815	-2.6508622	-2.7524703	NA	9.0256844	NA	-3.4512930	-3.4198844	-3.5251517	-3.1311324	-1.4560885	6.2882366	7.0994911	5.8827896	9.4891539	7.4135695	1.7592070	NA	2.1088974	NA	11.7548485	-3.5251517	0.2436421	NA	-3.5251517	-3.3595483	-3.5251517	-0.3978848	-3.1325905	-3.1732082	-3.3647718	-2.6516061	-0.6721869	-1.3164134	-0.6312068	NA	-3.5251517	-0.3039956	2.4425843	-0.0188406	2.4407451	13.5355511	5.3111954	2.5856864	3.1798079	8.3967972	1.7728379	-3.3410370	NA	NA	-3.3794308	5.5745211	7.3197470	-3.3335550	-3.3608491	-3.2044892	-2.6464043	-2.6055017	-3.5251517	NA	-3.5251517	-3.3015738	-3.3460808	NA	-1.5847659	-1.6338494	25.5372715	19.0374737
IgG_PRN	-0.3305829	-1.7474023	-1.5311012	-2.5248027	10.7205343	12.3461647	-1.8900740	NA	-0.7715371	2.655693	15.4080992	-2.5313892	-2.5540173	-2.5524926	-2.5174358	NA	-0.9671137	-1.6748275	-2.0305436	-2.2486837	-2.3702090	-0.3480611	-2.5118890	-2.5023661	-2.4171906	-2.3832536	-2.4265680	-2.4194942	-0.5597374	NA	-2.4903882	-0.7273815	11.4845057	3.5862484	9.2296848	11.1450043	-1.4647987	-0.9635826	-2.1307211	-2.4026234	-2.4267595	-2.5805774	-2.5462883	-2.6350815	-2.3577762	32.5382652	-1.8408030	NA	-1.9529635	-2.0003712	1.4729404	4.7197022	-2.1917994	NA	-2.5331185	-2.3599601	3.3360109	NA	2.3585086	-1.7571013	-2.5552747	NA	2.3770361	NA	-2.1339099	-2.6582470	0.7428370	-2.3274672	-2.4062493	-2.4477928	7.5438490	NA	2.1444077	3.6346788	4.9817991	-2.4198318	5.1439304	0.1445906	NA	NA	NA	2.2608166	-1.454200	-1.5423177	-1.5902617	-2.3251753	-2.1606266	-2.7503231	NA	NA	-2.7303176	NA	NA	-1.5779126	-0.8924890	0.1424010	-2.4776206	4.7714286	NA	1.1121812	-0.9447175	NA	1.4289031	1.6523008	1.1431899	-2.4536736	-2.5519898	-2.6082125	-2.4516883	-2.3620784	-1.5625397	0.0229516	4.8383932	1.9650736	1.4138436	1.4105663	-2.3868980	-2.3536160	-1.4541392	NA	-1.7063754	-1.7546922	7.1301622	-2.2758250	-2.3426623	NA	-2.2979527	NA	-2.7930970	-2.7787185	-2.8642628	-1.5304977	9.3076496	-2.6067693	-2.5932155	-2.6119738	-2.3762593	-2.3956523	-2.4743598	NA	-2.4668124	NA	-2.2793529	-1.8889146	2.3353410	NA	-2.8843067	2.1696081	-2.3458519	1.6521001	-2.6839566	-2.7022259	-2.8022878	-0.5417309	-2.3071921	0.4725285	-0.9812847	NA	-2.5299556	-0.6977954	2.3573875	-2.4803252	-2.3912477	-2.5472548	-2.6987531	-2.7531462	-2.5479298	-2.4584813	2.1294651	-2.8427570	NA	NA	-2.8476307	-0.6766944	4.7270236	-2.7775326	-2.7829719	-0.6824377	1.0784082	1.1181502	-2.8661947	NA	-2.8591902	7.8802538	6.9109612	NA	3.5439358	3.1754241	6.7741394	4.8399515
IgG_FHA	30.1677532	-2.7729707	1.1921635	-1.5418420	71.4015503	18.2466354	-2.6675782	NA	-3.2872515	-2.761404	-3.7987661	-1.4338012	-1.5564439	-1.5571055	-1.4618883	NA	6.5743699	1.5940280	-3.0818119	-0.7317407	-0.7695525	-2.8228064	-2.0390007	-1.9906309	-1.5727189	-1.6392605	-1.9753207	-1.9797705	8.2170830	NA	-1.3480422	10.3602848	-2.9936001	-3.5390306	13.4282627	-3.7987661	-0.4720998	-3.3634152	-3.6126654	-1.4444399	-1.5525193	-1.6578245	-1.7870660	-1.9913670	-0.7695525	63.3408890	-3.3588903	NA	-3.3800106	-3.3895404	-1.7770417	-2.7867460	-3.6125677	NA	-3.7256851	-3.6741979	-2.5405507	NA	-3.1327353	-2.9070320	-3.5016453	NA	-1.4992354	NA	-1.6786175	-3.1018562	-3.2774520	-2.1272900	-1.6161544	-1.6456656	-3.7987661	NA	0.2370930	2.2089977	8.2531185	-1.3111191	7.6810060	-3.5566585	NA	NA	NA	2.2734818	-3.137939	-3.2163365	-3.1871600	-1.6027889	-0.5412097	-3.2926230	NA	NA	-3.2090318	NA	NA	-1.4978998	-0.4243846	3.3813753	-1.5692406	-3.7987661	NA	-0.8738325	-2.4607062	NA	-1.4651060	-0.4720998	-3.0633776	-3.1190543	-3.2412093	-3.2944360	-1.5458384	-0.9270601	0.5557566	-3.1772380	-0.9520571	-3.0570095	-3.1164839	-3.0778823	-1.7211964	-1.2657809	-2.8593740	NA	-3.0419281	-3.0793569	0.4488187	-3.4402566	-3.4781048	NA	-1.4904728	NA	-3.6168289	-3.6036069	-3.7398882	0.3108220	-3.7987661	-1.8108168	-1.7717383	-1.8182991	-1.4048924	-1.3915081	-1.9229500	NA	-1.9055500	NA	-0.8886950	-3.4865808	-3.7987661	NA	-3.7987661	-1.7548742	-1.3427293	6.6482511	-3.3543358	-3.3705566	-3.5771987	-2.4874558	-1.2037830	0.7837319	-3.7987661	NA	-1.5354500	6.4543595	-3.7987661	-1.4021780	-0.8543594	-1.1400864	-0.8828659	-1.6524258	-1.7299788	-0.8668954	-3.7987661	-3.5039134	NA	NA	-3.4984937	6.2016091	-3.7987661	-3.5072565	-3.5478020	-3.2384975	1.9511461	2.1080737	-3.7785099	NA	-3.7764380	-1.4044049	-1.6462352	NA	-0.8464789	-1.0270543	15.6248398	32.1060944
IgG1_PT	2.1348767	-1.8155916	-2.2489669	-1.5068433	-0.3711414	11.3436871	-5.1510515	NA	-5.0796218	-4.958407	-1.6807382	14.9185753	12.4004974	9.9223251	1.9431562	NA	-2.0961764	-2.5411127	-2.8073945	1.7579393	14.4917965	0.3694081	3.3185883	0.4575591	1.3434196	11.9477444	-4.3867874	-4.3919740	-4.3706183	NA	5.9643831	-5.0518389	-5.0347776	-5.0271945	-2.8158619	-2.6480925	-5.1039705	-5.1042862	-5.0815382	7.6888380	9.3608437	-5.1099734	-5.0884891	-5.1213479	1.0071311	8.0183735	-5.1267190	NA	-5.1380930	-5.1390409	-3.6791835	-3.7757390	-4.5185156	NA	-4.6970272	-4.7829657	-4.5674610	NA	-3.3621652	-4.3169394	-4.3630681	NA	0.3900867	NA	-4.9719033	-4.9823298	-4.1385050	-5.0088696	-2.4163382	2.3788271	5.3465939	NA	-4.7897949	-4.7048502	-3.8420267	-0.4071202	11.9183846	-5.1717110	NA	NA	NA	-2.7736650	-3.550071	-2.9765279	-3.3772495	10.0499392	15.9477177	-4.4072094	NA	NA	-3.9885061	NA	NA	-2.2538297	-2.2374430	-2.7021487	0.2260470	4.0839548	NA	-4.0560603	-4.5115509	NA	-3.2388520	-1.4506133	-0.7926564	-4.5284634	-4.4807858	-4.6425257	3.2688017	6.0751543	3.0846395	-0.7123804	9.2269382	-3.9248610	-4.2315893	-3.0946662	8.0647974	14.3524113	-5.0351672	NA	-5.1307664	-4.8764162	-4.4594173	-3.8741622	-4.5441942	NA	17.2989693	NA	-5.1707869	-5.1951985	-5.1878977	-5.1441698	-5.1289601	10.3285036	3.0671654	14.3825150	15.6506214	13.4898968	7.1559391	NA	7.9700499	NA	16.6123695	-5.1945996	2.2564764	NA	-5.1945996	-5.1718802	-5.0877395	0.6987157	-5.0977292	-5.1643100	-5.1277332	-4.7911024	-1.4479129	1.0154161	-4.0344419	NA	-3.6343873	1.3578596	8.4770184	-1.9943323	-2.7146425	3.3911409	4.3998489	4.5844460	8.5694466	11.6638432	7.7575827	-5.1157045	NA	NA	-5.1590600	-4.3353000	14.1871128	-5.1951985	-5.1951985	-5.1951985	-5.1360273	-4.6998658	-5.1951985	NA	-5.1951985	-5.1951985	-5.1716733	NA	-3.1694601	-2.4892449	22.9058685	21.2514191
IgG1_PRN	0.5330515	-0.1993157	-0.3962686	-0.4511517	0.7135785	1.3868463	-1.5595392	NA	-1.5202327	-1.386737	-0.3921682	-0.1400127	-0.3287729	-0.5154612	-0.6989410	NA	-0.1678466	-0.2620096	-0.4819883	0.1983252	1.2341013	-0.1672769	0.2036593	-0.3094846	-0.1178493	0.8491502	-0.7524341	-0.6402987	-0.5890375	NA	0.1845559	1.0148330	1.4013894	1.0989377	1.4993036	1.1930537	-0.2020819	-0.2238406	-0.1294262	0.8152604	0.8316472	-1.3959357	-1.3609915	-1.4062996	0.6697321	0.6828904	-0.1937799	NA	-0.2888726	-0.2958180	0.3204190	1.5198588	-1.1282654	NA	-1.2122345	-1.2418611	0.2624913	NA	1.6077621	-0.4477493	-0.4734418	NA	-0.4839150	NA	-1.1854024	-1.2176335	1.4833300	-1.2428107	0.3916509	0.7887402	1.6678741	NA	0.9822462	1.0052211	1.1126740	1.3315177	1.9037926	0.3722317	NA	NA	NA	0.8292079	-1.467018	-1.4233909	-1.4596071	1.2346506	1.2107978	-1.5734509	NA	NA	-1.5639607	NA	NA	0.5806220	0.5504797	0.2981476	0.7486768	1.1405210	NA	0.6683860	0.4868803	NA	0.4165201	0.6152568	0.9980505	-1.3100252	-1.2716544	-1.3128674	0.5907907	0.8788643	0.6107559	1.0095935	1.3035491	0.7451999	0.5627561	0.9834130	1.2909937	1.6220632	-0.4186394	NA	-0.7697773	-0.1253444	1.0876431	-0.8616315	-1.1367958	NA	1.2497027	NA	-1.6097591	-1.6166849	-1.6210227	-0.3037775	0.4388831	-1.3936888	-1.4577167	-1.2647336	1.0813246	0.9720395	0.1710225	NA	0.2077831	NA	1.2359488	-1.1374356	1.1183329	NA	-1.1174660	0.8635759	1.2172108	0.9358635	-1.5162941	-1.5810224	-1.5466971	-0.1736298	1.0039778	1.0027845	-0.4425238	NA	-0.2541584	0.4080057	1.3089554	0.8241258	1.0855370	-1.6020430	-1.5856622	-1.5767926	-0.7146888	0.0799493	1.3359869	-1.6162525	NA	NA	-1.6234123	-1.4026898	1.2974999	-1.5878769	-1.6027222	-1.6056446	-0.5690702	0.4060843	-1.6090636	NA	-1.6280003	0.6161623	0.9984081	NA	1.2167926	1.3686037	1.7934091	1.6999514
IgG1_FHA	1.3506850	-0.2977802	0.6075326	0.5711867	0.5056711	1.6506091	-0.4257567	NA	-0.4471017	-0.477857	1.0316552	0.7943434	0.6657492	0.5406431	-0.1094899	NA	0.6482202	0.5448984	0.3556803	0.4478794	1.5264968	-0.7523011	-0.4173621	-0.7596757	-0.3282365	0.4848517	-1.4186293	-1.3894570	-1.3966438	NA	0.5816485	-0.7534875	-0.6832778	-0.6664927	1.3315595	1.2289065	-1.0289313	-1.0324562	-0.9646443	0.9321696	1.1396097	0.1346369	0.1734824	0.0669689	1.1944970	1.1617900	-0.8344398	NA	-0.8750117	-0.8687773	0.0982956	-0.3750523	-1.3673432	NA	-1.3976043	-1.4300236	-0.7100758	NA	-0.2587634	-0.5780591	-0.5997359	NA	0.0623581	NA	-0.1936320	-0.1748087	-0.5320424	-0.2215912	0.1475357	0.8878089	1.0538224	NA	0.5522584	0.5614938	0.6770099	1.1042305	1.3703839	-1.2615570	NA	NA	NA	0.5019115	-1.062923	-0.9538963	-1.0924051	0.4927672	1.1672686	-0.7750806	NA	NA	-0.5454957	NA	NA	0.5396284	0.4956983	0.3646978	0.7282447	1.1015285	NA	0.3940600	0.1161021	NA	0.0492946	0.3315313	-0.3099908	-1.1147962	-1.1143761	-1.1494951	0.4595512	1.1809603	1.4329895	-0.2438264	0.3454627	-0.7465454	-0.8560758	-0.4839463	0.2069417	1.4544340	-0.6198442	NA	-0.9761960	-0.3855206	0.3449920	-1.1579571	-1.3571022	NA	0.9355954	NA	-1.4594471	-1.5094374	-1.5245588	0.3223425	0.4196430	-0.3247627	-0.7172022	0.0217575	0.8066441	0.7195188	-0.0472527	NA	-0.0118845	NA	1.3525733	-1.2840472	1.2022661	NA	-1.3100265	0.2245244	1.2158409	1.0225698	-1.2072489	-1.3601164	-1.2818596	-0.5747859	0.8698355	1.1558508	0.4565169	NA	0.5471708	1.2370526	1.4364480	1.1094517	1.1644405	-0.1967018	-0.1641138	-0.1065036	0.1316295	1.0013691	1.4422122	-1.0083758	NA	NA	-1.1527846	-0.1089413	1.8572685	-1.2424072	-1.3474181	-1.3387465	-0.2644275	0.7718261	-1.6149327	NA	-1.6352330	-1.3599993	0.2779243	NA	0.2383531	0.5570120	1.5052449	2.2293525
IgG1_TT	0.2212083	-0.2363772	0.1153790	0.1501327	0.4998592	0.6206998	-1.1879717	NA	-1.1677321	-0.353964	0.3943050	0.0606662	-0.0562036	-0.1337434	-0.2190498	NA	0.2745302	0.2265518	0.1253496	0.3168206	0.6737412	-0.0914048	0.1379777	-0.1405669	0.2883382	0.5866187	-0.6133177	-0.5134034	-0.5020854	NA	-0.0712787	0.1375604	0.2048780	0.1989123	0.8343232	0.6145651	-0.6087146	-0.6245129	-0.5502352	0.4305834	0.4578713	-0.7273121	-0.6750379	-0.7433865	0.4970171	0.4238074	-0.2140155	NA	-0.2626072	-0.2500864	0.2452828	0.1697469	-0.3541930	NA	-0.4127644	-0.4300357	0.3324347	NA	0.2355964	-0.1387620	-0.1138309	NA	0.4105803	NA	-0.0911831	-0.1088042	0.1177952	-0.1418370	0.1945635	0.4386851	0.5165128	NA	-0.1402802	-0.1128591	0.4287161	0.6128532	0.6137218	0.1054313	NA	NA	NA	0.5929229	0.122508	0.2135081	0.0626293	0.5006725	0.5483192	-0.3584005	NA	NA	-0.2284058	NA	NA	-0.1213124	-0.1046139	-0.1983784	0.1176281	0.3874810	NA	-0.1602125	-0.2787660	NA	-0.1509396	-0.0274665	0.4994658	-0.5925738	-0.5902869	-0.6769808	-0.2972627	-0.1687434	0.2774595	0.5103652	0.6098183	0.1288854	0.0023512	0.2975727	0.6266220	0.7670068	-0.0420759	NA	-0.2286596	0.0866948	0.4114217	0.0414597	-0.1721776	NA	0.5705432	NA	-0.2062715	-0.3255722	-0.3188989	0.5235603	0.5279391	-0.6958706	-0.7546335	-0.5368431	0.0187387	-0.0208020	-0.4555789	NA	-0.4233707	NA	0.4874020	-0.2317325	0.3297883	NA	-0.2462165	0.4039148	0.5115052	0.2617891	-0.3652931	-0.4857155	-0.4492400	0.1035992	0.3051312	0.3045197	-0.7551563	NA	-0.6797618	0.1754229	0.5531147	0.4867998	0.5026983	-0.8511436	-0.8033890	-0.7974573	-0.1418537	0.2332835	0.5644479	-0.3327233	NA	NA	-0.4470547	0.5910554	0.6543214	-0.6583313	-0.7018538	-0.7464511	-0.0979317	0.2265518	0.1173426	NA	-0.0632721	0.5020870	0.6121622	NA	-0.8401834	-0.7486132	0.6866778	0.5932148
IgG1_DT	0.9618455	-0.1219424	0.5636808	0.6215144	0.0147097	0.7996684	-1.4117879	NA	-1.3977960	-1.397610	-0.6326299	0.7396089	0.6152757	0.5757691	-0.0385431	NA	0.3327219	0.2109730	0.1748501	-0.0708445	0.5998486	-0.6810889	-0.4216137	-0.6852385	-0.5977430	0.0697491	-0.5768223	-0.4659923	-0.4479035	NA	0.3214171	0.5817925	0.7224320	0.6452783	1.1322016	0.9235500	-0.7190422	-0.7161835	-0.6789913	0.3987793	0.4813228	-1.1285141	-1.0842049	-1.1498942	-0.1015142	-0.0358844	-0.4233804	NA	-0.4927323	-0.4596792	0.3573823	0.0966332	-0.6669016	NA	-0.7500108	-0.7956302	-0.2216030	NA	0.2313665	-0.7525420	-0.7391718	NA	0.2374314	NA	-1.1408122	-1.1505494	-0.0117220	-1.1743715	-0.5320106	0.2380100	0.8278466	NA	0.3106569	0.3585596	0.7394174	0.8224591	1.0991656	-0.6213020	NA	NA	NA	0.7874616	0.322349	0.5176784	0.2085603	0.9971415	1.0368048	-0.4312356	NA	NA	-0.2576829	NA	NA	-0.0469112	-0.0516385	-0.2004471	0.0228010	0.1154203	NA	-0.6537624	-0.7790657	NA	-0.0010276	0.2447321	0.6004382	0.1108874	0.1295092	0.0670279	0.1415768	0.2878871	1.0061072	0.6824151	0.7992967	0.7472397	0.5745481	0.7819825	0.8765031	0.9543737	-0.1663792	NA	-0.6085284	0.0647079	0.5370405	0.3824414	-0.2727789	NA	0.4968278	NA	-1.1349676	-1.2194507	-1.2107943	-0.2468946	-0.0993884	-0.9833739	-1.1214896	-0.7694778	-0.2119415	-0.3787125	0.3285273	NA	0.3390247	NA	0.5581017	0.5947877	0.8229285	NA	0.4581857	0.8195270	0.9222018	0.7221740	-0.0815792	-0.2805638	-0.1971866	0.6002721	0.7275206	0.7970933	-0.6678988	NA	-0.5658232	0.9322418	1.3015691	0.7198123	0.6997091	-1.3295312	-1.2917790	-1.2859290	-0.1506715	0.6238285	1.2868150	-1.0579413	NA	NA	-1.1620492	0.9869462	1.2003673	-0.5547750	-0.6456937	-0.6690062	-0.2621555	-0.0935755	-0.5870612	NA	-0.8028134	1.0844437	1.1131366	NA	-1.3926417	-1.3735541	0.9365917	0.9168876
IgG1_OVA	-4.6534700	-4.9440260	30.9856186	41.6696129	17.8935757	50.3149986	-1.9460940	NA	0.3489890	-4.440108	0.8776903	-5.2172551	-5.2293153	-4.9975972	-5.2293153	NA	-5.2293153	-5.2293153	-5.1465273	-5.2293153	-5.1167412	-4.5038233	-3.7242460	-4.1784821	-5.0564332	-3.9997663	11.9134026	13.3803635	14.3260689	NA	8.3541508	-5.1749468	-5.0960135	-5.1433735	-4.8907881	-5.0012941	-5.0960135	-5.0802274	-5.0881205	-4.9539347	-4.8276420	-3.2647707	-2.3017895	-3.6910083	-1.6861129	-1.7097929	-3.7778344	NA	-4.1330328	-4.0462065	-4.6888008	28.4532394	-4.3461514	NA	-4.6855626	-4.7960687	-4.6888008	NA	34.2642021	-3.4384232	-3.4226365	NA	-4.3481998	NA	-5.0802274	-5.1118002	19.3646812	-5.1591601	-5.2293153	-4.7408156	13.3281937	NA	-5.0805230	-5.0331631	-5.0963097	-5.0647364	20.0938702	-4.8279376	NA	NA	NA	-4.9599018	-5.112096	-5.0805230	-5.1907434	-5.0418134	-5.1013851	5.8437886	NA	NA	12.3557529	NA	NA	-3.2966397	-3.3124261	-3.5227275	-3.2397604	-0.4994488	NA	-4.2911940	-4.6542854	NA	-4.8184185	-4.7737393	0.7891407	-5.2293153	-5.2293153	-5.2293153	-5.2293153	-5.2293153	-4.5331912	1.1650114	-0.6542034	-5.2293153	-5.2293153	-5.2293153	-5.1244340	-5.1708651	17.7351494	NA	9.7894382	29.9155769	7.0482554	-5.0470486	-5.2293153	NA	-4.7684622	NA	-5.0315719	-5.2078114	-5.1166954	-5.0470486	-5.0006175	7.1024251	3.7667499	16.2803097	10.3293877	6.2511888	0.0097260	NA	0.3771720	NA	-1.9772053	19.0965157	-1.1879401	NA	20.4165993	17.3630600	22.4226589	-2.5711451	-2.3106287	-3.5966907	-3.1475897	-2.4490957	-2.7885072	-2.5861800	0.7174001	NA	1.1460876	-4.8414054	-2.5185232	2.1526918	-0.9651575	-3.0523257	-2.3038242	-1.4872770	-2.3070166	-4.3119121	-2.3531401	5.1342082	NA	NA	0.7214837	-1.8269207	-4.8789926	-4.8489227	-4.9391317	-4.9541669	-4.9992714	-4.6859760	-5.2293153	NA	-5.2293153	-5.2293153	-5.1565375	NA	-5.1413584	-5.0958200	-4.9743848	-5.0199232
IgG2_PT	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.8417406	NA	-4.8417406	-4.841741	-4.8417406	0.9762440	0.0645370	-0.3977962	-0.1611032	NA	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.6582747	-4.0976048	-3.9481947	-4.7371721	-3.3959105	-3.7115016	-4.8417406	-4.8417406	-4.8417406	NA	-2.2847350	-4.5482841	-4.5078635	-4.7099662	-4.3866019	-4.3866019	-4.6291251	-4.6291251	-4.6291251	-4.1440787	-4.2249198	-4.3866019	-3.1739864	-3.7398736	-3.4165094	-0.5870733	-4.8417406	NA	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.8417406	NA	-4.8417406	-4.8417406	-4.8417406	NA	-4.8417406	-4.8417406	-4.8417406	NA	-4.5482841	NA	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.3057609	-4.8417406	NA	-4.8417406	-4.8417406	-4.3351817	2.0138879	-4.8417406	-4.8417406	NA	NA	NA	-4.8417406	-2.182955	-2.6134002	-2.2934744	1.1660848	20.4597816	-4.5504045	NA	NA	-4.5504045	NA	NA	28.5938911	26.8721085	25.3829994	23.8749886	27.3345490	NA	-2.2367604	-2.3981774	NA	-2.3821812	-0.8741679	-1.5204196	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.5813551	-1.4689755	0.8460178	-4.5985036	-4.4327383	-4.8417406	-1.7804990	0.9546227	-2.9408538	NA	-1.5732932	-2.2777941	-3.1066189	-4.8417406	-4.8417406	NA	-4.8417406	NA	-4.5156212	-4.3498559	-4.8417406	-4.5156212	-4.5985036	-3.1895013	-2.2363529	-2.7336476	-3.6867962	-2.6093240	-4.8417406	NA	-4.8417406	NA	-4.8417406	-4.8417406	0.2286863	NA	-4.8417406	-4.8417406	-4.8417406	-1.4689755	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-1.6108489	-1.8290854	81.6821518	NA	69.6829071	-4.8417406	0.1000757	114.0673370	138.6068878	1.1284084	3.3341522	0.7313747	1.2097454	2.1499434	0.3315749	-4.8417406	NA	NA	-4.8417406	-4.8417406	-4.7614102	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.8417406	-4.8417406	NA	-4.8417406	-4.8417406	-4.8417406	NA	-4.8417406	-4.8417406	-4.2561626	-4.4147205

# 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
	1	102	103	104	105	106	114	115	116	117	118	131	132	133	134	135	138	139	140	141	142	146	147	148	149	150	153	154	155	156	157	160	161	162	163	164	167	168	169	170	171	174	175	176	177	178	181	182	183	184	185	19	191	192	193	194	195	2	20	201	202	203	204	205	208	209	21	210	211	212	22	223	224	225	226	227	23	241	242	243	244	245	248	249	250	251	252	255	256	257	258	259	27	274	275	276	277	278	28	281	282	283	284	285	29	293	294	295	296	297	3	30	31	324	325	326	327	328	332	333	334	335	336	342	343	344	345	346	349	350	351	352	353	355	356	357	358	359	360	361	362	363	364	369	37	370	371	372	373	38	385	386	387	388	389	39	397	398	399	4	40	400	401	405	406	407	408	409	41	45	46	47	48	49	5	70	71	72	73	74	77	78	79	80	81	87	88	89	90	91
IgG_PT	3.7369909	1.5812328	2.010138	2.2775645	2.195767	3.164491	0.3022485	3.554364	1.0977616	0.8818502	5.4768755	9.9703188	8.0189047	7.8218422	8.8619719	3.554364	2.2206292	1.5235772	1.1624494	10.3925629	9.9687462	3.4783800	3.6482749	4.1868041	9.2039127	9.2753525	4.8572533	5.1131613	1.3536265	3.554364	7.2634585	5.8423026	0.5266731	0.1660485	1.3963358	1.9276724	3.4813619	0.3164353	0.1071825	7.4763405	4.9046152	0.3212907	0.3451729	0.2889280	4.3562596	7.206231	0.2057443	2.855624	0.1632783	0.1562729	1.2879777	1.096365	0.6814096	3.053528	0.3191361	0.5004156	1.2589645	3.226343	0.7126839	0.6550293	0.2070153	3.554364	1.0322037	3.554364	0.7062254	0.2070153	0.5163183	0.5036106	0.0292125	2.3958902	6.4240572	3.554364	0.7832427	1.0025187	1.915441	4.0925963	7.041546	0.1215158	3.554364	3.554364	3.554364	1.7987329	1.6457729	1.4969113	1.5909426	7.9525442	14.8636341	0.5564303	3.059515	3.132579	0.6278489	3.554364	3.554364	1.286315	1.779609	2.267047	4.0034292	4.2152107	3.146933	1.0573809	0.5230799	3.554364	1.4585850	2.2563970	1.6274474	0.7070336	0.5414031	0.4815500	4.9186862	6.6036446	2.2555330	1.4212642	7.896540	1.1682737	1.0851722	1.0827188	7.9707794	8.8637295	1.0043149	3.281347	0.8011198	0.7730763	1.444783	0.9035020	0.8018939	3.554364	12.5800486	3.554364	0.1030712	0.1344798	0.0292125	0.4232318	2.0982757	9.8426008	10.6538553	9.4371538	13.0435181	10.9679337	5.3135712	3.554364	5.6632617	3.554364	15.3092127	0.0292125	3.7980063	3.554364	0.0292125	0.1948159	0.0292125	3.156479	0.4217737	0.3811560	0.1895924	0.9027581	2.882177	2.237951	2.9231575	3.554364	0.0292125	3.2503686	5.9969485	3.5355237	5.9951093	17.0899153	8.8655596	6.1400506	6.7341721	11.9511614	5.3272021	0.2133272	2.797947	3.554364	0.1749334	9.1288853	10.8741112	0.2208092	0.1935151	0.3498750	0.9079599	0.9488626	0.0292125	3.010039	0.0292125	0.2527905	0.2082834	3.554364	1.9695983	1.9205148	29.0916357	22.5918379
IgG_PRN	2.6023502	1.1855308	1.401832	0.4081304	13.653467	15.279098	1.0428591	2.932933	2.1613960	5.5886261	18.3410323	0.4015439	0.3789158	0.3804405	0.4154973	2.932933	1.9658194	1.2581056	0.9023895	0.6842494	0.5627241	2.5848720	0.4210441	0.4305670	0.5157425	0.5496795	0.5063651	0.5134389	2.3731956	2.932933	0.4425449	2.2055516	14.4174387	6.5191815	12.1626179	14.0779374	1.4681344	1.9693505	0.8022120	0.5303097	0.5061736	0.3523557	0.3866448	0.2978516	0.5751569	35.471198	1.0921301	3.163481	0.9799696	0.9325619	4.4058735	7.652635	0.7411337	3.030399	0.3998146	0.5729730	6.2689440	2.946610	5.2914417	1.1758318	0.3776584	2.932933	5.3099692	2.932933	0.7990232	0.2746861	3.6757700	0.6054659	0.5266838	0.4851403	10.4767821	2.932933	5.0773408	6.5676119	7.914732	0.5131013	8.076863	3.0775237	2.932933	2.932933	2.932933	5.1937497	1.4787329	1.3906153	1.3426714	0.6077578	0.7723064	0.1826100	2.795148	2.883554	0.2026155	2.932933	2.932933	1.355020	2.040444	3.075334	0.4553125	7.7043617	3.590483	4.0451143	1.9882156	2.932933	4.3618362	4.5852339	4.0761230	0.4792595	0.3809433	0.3247206	0.4812448	0.5708547	1.3703934	2.9558847	7.771326	4.8980067	4.3467767	4.3434994	0.5460351	0.5793171	1.4787939	2.598960	1.2265577	1.1782409	10.063095	0.6571081	0.5902708	2.932933	0.6349804	2.932933	0.1398361	0.1542146	0.0686703	1.4024354	12.2405827	0.3261638	0.3397176	0.3209593	0.5566738	0.5372808	0.4585733	2.932933	0.4661207	2.932933	0.6535802	1.0440185	5.2682741	2.932933	0.0486264	5.1025412	0.5870812	4.585033	0.2489765	0.2307072	0.1306453	2.3912022	0.625741	3.405461	1.9516484	2.932933	0.4029775	2.2351377	5.2903206	0.4526079	0.5416853	0.3856783	0.2341800	0.1797869	0.3850033	0.4744518	5.0623982	0.0901761	2.663086	2.932933	0.0853024	2.2562387	7.6599567	0.1554005	0.1499612	2.2504954	4.0113413	4.0510833	0.0667384	3.389242	0.0737429	10.8131869	9.8438942	2.932933	6.4768689	6.1083572	9.7070725	7.7728846
IgG_FHA	34.0509555	1.1102316	5.075366	2.3413603	75.284753	22.129838	1.2156241	3.883202	0.5959508	1.1217988	0.0844362	2.4494011	2.3267584	2.3260968	2.4213140	3.883202	10.4575722	5.4772303	0.8013904	3.1514616	3.1136498	1.0603960	1.8442016	1.8925714	2.3104835	2.2439418	1.9078816	1.9034318	12.1002853	3.883202	2.5351601	14.2434871	0.8896022	0.3441718	17.3114650	0.0844362	3.4111025	0.5197871	0.2705369	2.4387624	2.3306830	2.2253778	2.0961363	1.8918353	3.1136498	67.224091	0.5243120	3.674054	0.5031917	0.4936619	2.1061606	1.096456	0.2706347	3.671143	0.1575172	0.2090044	1.3426516	3.785521	0.7504671	0.9761703	0.3815570	3.883202	2.3839669	3.883202	2.2045848	0.7813461	0.6057503	1.7559123	2.2670479	2.2375367	0.0844362	3.883202	4.1202953	6.0922000	12.136321	2.5720832	11.564208	0.3265438	3.883202	3.883202	3.883202	6.1566842	0.7452638	0.6668658	0.6960423	2.2804134	3.3419926	0.5905793	3.647105	3.736402	0.6741705	3.883202	3.883202	2.385302	3.458818	7.264578	2.3139617	0.0844362	3.875440	3.0093699	1.4224961	3.883202	2.4180963	3.4111025	0.8198247	0.7641480	0.6419930	0.5887663	2.3373640	2.9561422	4.4389589	0.7059643	2.931145	0.8261929	0.7667184	0.8053200	2.1620059	2.6174214	1.0238283	3.230149	0.8412743	0.8038454	4.332021	0.4429457	0.4050975	3.883202	2.3927295	3.883202	0.2663734	0.2795954	0.1433141	4.1940243	0.0844362	2.0723855	2.1114640	2.0649033	2.4783099	2.4916942	1.9602523	3.883202	1.9776523	3.883202	2.9945073	0.3966215	0.0844362	3.883202	0.0844362	2.1283281	2.5404730	10.531453	0.5288665	0.5126457	0.3060036	1.3957465	2.679419	4.666934	0.0844362	3.883202	2.3477523	10.3375618	0.0844362	2.4810243	3.0288429	2.7431159	3.0003364	2.2307765	2.1532235	3.0163069	0.0844362	0.3792889	3.504662	3.883202	0.3847086	10.0848114	0.0844362	0.3759458	0.3354003	0.6447048	5.8343484	5.9912760	0.1046925	3.914662	0.1067643	2.4787974	2.2369671	3.883202	3.0367234	2.8561480	19.5080421	35.9892967
IgG1_PT	7.3347120	3.3842437	2.950868	3.6929920	4.828694	16.543522	0.0487838	5.199835	0.1202135	0.2414279	3.5190971	20.1184106	17.6003327	15.1221604	7.1429915	5.199835	3.1036589	2.6587226	2.3924408	6.9577746	19.6916318	5.5692434	8.5184236	5.6573944	6.5432549	17.1475797	0.8130479	0.8078613	0.8292170	5.199835	11.1642184	0.1479964	0.1650577	0.1726408	2.3839734	2.5517428	0.0958648	0.0955491	0.1182971	12.8886733	14.5606790	0.0898619	0.1113462	0.0784874	6.2069664	13.218209	0.0731163	4.642797	0.0617423	0.0607944	1.5206518	1.424096	0.6813197	4.824586	0.5028081	0.4168696	0.6323743	5.074737	1.8376701	0.8828959	0.8367672	5.199835	5.5899220	5.199835	0.2279320	0.2175055	1.0613303	0.1909657	2.7834971	7.5786624	10.5464292	5.199835	0.4100404	0.4949851	1.357809	4.7927151	17.118220	0.0281243	5.199835	5.199835	5.199835	2.4261703	1.6497641	2.2233074	1.8225858	15.2497745	21.1475530	0.7926259	4.865930	4.847702	1.2113292	5.199835	5.199835	2.946006	2.962392	2.497687	5.4258823	9.2837901	4.783921	1.1437750	0.6882844	5.199835	1.9609833	3.7492220	4.4071789	0.6713719	0.7190495	0.5573096	8.4686370	11.2749896	8.2844748	4.4874549	14.426773	1.2749743	0.9682460	2.1051691	13.2646327	19.5522466	0.1646681	4.791113	0.0690689	0.3234191	0.740418	1.3256731	0.6556411	5.199835	22.4988046	5.199835	0.0290484	0.0046368	0.0119376	0.0556655	0.0708752	15.5283389	8.2670007	19.5823503	20.8504567	18.6897321	12.3557744	5.199835	13.1698852	5.199835	21.8122048	0.0052357	7.4563117	5.199835	0.0052357	0.0279551	0.1120958	5.898551	0.1021061	0.0355253	0.0721021	0.4087329	3.751922	6.215251	1.1653934	5.199835	1.5654480	6.5576949	13.6768537	3.2055030	2.4851928	8.5909762	9.5996842	9.7842813	13.7692819	16.8636785	12.9574180	0.0841308	4.813440	5.199835	0.0407753	0.8645353	19.3869481	0.0046368	0.0046368	0.0046368	0.0638080	0.4999695	0.0046368	4.738888	0.0046368	0.0046368	0.0281620	5.199835	2.0303752	2.7105904	28.1057038	26.4512544
IgG1_PRN	2.1747831	1.4424160	1.245463	1.1905799	2.355310	3.028578	0.0821924	1.641732	0.1214989	0.2549950	1.2495635	1.5017189	1.3129587	1.1262704	0.9427906	1.641732	1.4738851	1.3797220	1.1597433	1.8400568	2.8758329	1.4744548	1.8453909	1.3322470	1.5238823	2.4908818	0.8892975	1.0014329	1.0526941	1.641732	1.8262875	2.6565646	3.0431210	2.7406694	3.1410352	2.8347853	1.4396497	1.4178910	1.5123054	2.4569920	2.4733788	0.2457960	0.2807401	0.2354320	2.3114637	2.324622	1.4479517	1.628691	1.3528590	1.3459136	1.9621506	3.161590	0.5134662	1.810468	0.4294971	0.3998705	1.9042230	1.637565	3.2494937	1.1939824	1.1682898	1.641732	1.1578166	1.641732	0.4563292	0.4240981	3.1250616	0.3989209	2.0333825	2.4304718	3.3096057	1.641732	2.6239778	2.6469527	2.754406	2.9732493	3.545524	2.0139633	1.641732	1.641732	1.641732	2.4709395	0.1747137	0.2183408	0.1821245	2.8763822	2.8525294	0.0682807	1.735234	1.557671	0.0777709	1.641732	1.641732	2.222354	2.192211	1.939879	2.3904084	2.7822527	1.791334	2.3101176	2.1286119	1.641732	2.0582517	2.2569884	2.6397821	0.3317064	0.3700773	0.3288642	2.2325224	2.5205959	2.2524875	2.6513251	2.945281	2.3869315	2.2044877	2.6251446	2.9327253	3.2637948	1.2230922	1.658701	0.8719543	1.5163872	2.729375	0.7801002	0.5049359	1.641732	2.8914343	1.641732	0.0319725	0.0250467	0.0207089	1.3379542	2.0806147	0.2480428	0.1840149	0.3769981	2.7230562	2.6137711	1.8127542	1.641732	1.8495147	1.641732	2.8776804	0.5042961	2.7600645	1.641732	0.5242656	2.5053076	2.8589424	2.577595	0.1254375	0.0607092	0.0950345	1.4681019	2.645709	2.644516	1.1992078	1.641732	1.3875732	2.0497373	2.9506871	2.4658574	2.7272686	0.0396886	0.0560694	0.0649390	0.9270428	1.7216809	2.9777185	0.0254791	2.061464	1.641732	0.0183194	0.2390418	2.9392315	0.0538547	0.0390095	0.0360870	1.0726614	2.0478159	0.0326680	1.422699	0.0137314	2.2578939	2.6401397	1.641732	2.8585242	3.0103353	3.4351407	3.3416830
IgG1_FHA	3.0132531	1.3647879	2.270101	2.2337548	2.168239	3.313177	1.2368114	1.662568	1.2154664	1.1847111	2.6942233	2.4569114	2.3283173	2.2032112	1.5530782	1.662568	2.3107883	2.2074665	2.0182484	2.1104475	3.1890649	0.9102670	1.2452060	0.9028924	1.3343316	2.1474198	0.2439388	0.2731111	0.2659243	1.662568	2.2442166	0.9090806	0.9792903	0.9960754	2.9941276	2.8914746	0.6336368	0.6301119	0.6979238	2.5947376	2.8021778	1.7972050	1.8360505	1.7295370	2.8570651	2.824358	0.8281283	1.279866	0.7875564	0.7937908	1.7608637	1.287516	0.2952249	1.565630	0.2649637	0.2325445	0.9524923	1.382448	1.4038047	1.0845090	1.0628322	1.662568	1.7249262	1.662568	1.4689361	1.4877594	1.1305257	1.4409769	1.8101038	2.5503770	2.7163905	1.662568	2.2148265	2.2240618	2.339578	2.7667986	3.032952	0.4010111	1.662568	1.662568	1.662568	2.1644796	0.5996451	0.7086718	0.5701630	2.1553353	2.8298367	0.8874875	1.548569	1.538518	1.1170723	1.662568	1.662568	2.202196	2.158266	2.027266	2.3908128	2.7640966	1.728477	2.0566281	1.7786702	1.662568	1.7118627	1.9940994	1.3525773	0.5477719	0.5481920	0.5130730	2.1221193	2.8435284	3.0955576	1.4187417	2.008031	0.9160227	0.8064923	1.1786218	1.8695098	3.1170021	1.0427239	1.654607	0.6863721	1.2770475	2.007560	0.5046110	0.3054659	1.662568	2.5981635	1.662568	0.2031209	0.1531307	0.1380093	1.9849106	2.0822111	1.3378054	0.9453659	1.6843256	2.4692122	2.3820869	1.6153154	1.662568	1.6506836	1.662568	3.0151414	0.3785208	2.8648342	1.662568	0.3525416	1.8870925	2.8784090	2.685138	0.4553192	0.3024517	0.3807085	1.0877821	2.532404	2.818419	2.1190850	1.662568	2.2097389	2.8996207	3.0990161	2.7720197	2.8270086	1.4658663	1.4984543	1.5560645	1.7941976	2.6639372	3.1047803	0.6541923	1.346454	1.662568	0.5097835	1.5536268	3.5198365	0.4201609	0.3151500	0.3238215	1.3981405	2.4343942	0.0476354	1.180319	0.0273350	0.3025688	1.9404924	1.662568	1.9009212	2.2195801	3.1678129	3.8919206
IgG1_TT	1.4288515	0.9712660	1.323022	1.3577758	1.707502	1.828343	0.0196714	1.207643	0.0399110	0.8536791	1.6019481	1.2683094	1.1514395	1.0738997	0.9885933	1.207643	1.4821733	1.4341949	1.3329928	1.5244638	1.8813844	1.1162384	1.3456209	1.0670762	1.4959813	1.7942618	0.5943255	0.6942398	0.7055577	1.207643	1.1363645	1.3452035	1.4125211	1.4065554	2.0419663	1.8222083	0.5989286	0.5831303	0.6574080	1.6382265	1.6655145	0.4803311	0.5326053	0.4642566	1.7046603	1.631450	0.9936276	1.078963	0.9450359	0.9575567	1.4529259	1.377390	0.8534502	1.308156	0.7948787	0.7776074	1.5400778	1.120224	1.4432396	1.0688812	1.0938122	1.207643	1.6182234	1.207643	1.1164601	1.0988389	1.3254384	1.0658062	1.4022067	1.6463282	1.7241559	1.207643	1.0673629	1.0947840	1.636359	1.8204963	1.821365	1.3130745	1.207643	1.207643	1.207643	1.8005661	1.3301512	1.4211513	1.2702725	1.7083156	1.7559624	0.8492426	1.305105	1.234761	0.9792374	1.207643	1.207643	1.086331	1.103029	1.009265	1.3252712	1.5951241	1.226005	1.0474306	0.9288772	1.207643	1.0567036	1.1801766	1.7071090	0.6150694	0.6173562	0.5306624	0.9103804	1.0388998	1.4851027	1.7180084	1.817462	1.3365285	1.2099943	1.5052159	1.8342651	1.9746499	1.1655673	1.421191	0.9789836	1.2943380	1.619065	1.2491028	1.0354656	1.207643	1.7781863	1.207643	1.0013716	0.8820710	0.8887442	1.7312034	1.7355822	0.5117725	0.4530096	0.6708001	1.2263819	1.1868411	0.7520642	1.207643	0.7842725	1.207643	1.6950451	0.9759107	1.5374315	1.207643	0.9614267	1.6115580	1.7191484	1.469432	0.8423500	0.7219276	0.7584032	1.3112423	1.512774	1.512163	0.4524869	1.207643	0.5278814	1.3830661	1.7607578	1.6944430	1.7103415	0.3564996	0.4042541	0.4101858	1.0657895	1.4409267	1.7720910	0.8749199	1.312631	1.207643	0.7605885	1.7986985	1.8619646	0.5493119	0.5057894	0.4611920	1.1097114	1.4341949	1.3249857	1.418585	1.1443710	1.7097301	1.8198054	1.207643	0.3674598	0.4590299	1.8943210	1.8008579
IgG1_DT	2.3891534	1.3053654	1.990989	2.0488223	1.442018	2.226976	0.0155200	1.427308	0.0295118	0.0296979	0.7946780	2.1669167	2.0425836	2.0030769	1.3887647	1.427308	1.7600298	1.6382809	1.6021580	1.3564633	2.0271565	0.7462190	1.0056942	0.7420694	0.8295648	1.4970570	0.8504856	0.9613156	0.9794043	1.427308	1.7487249	2.0091003	2.1497399	2.0725862	2.5595094	2.3508579	0.7082657	0.7111244	0.7483166	1.8260871	1.9086306	0.2987938	0.3431029	0.2774136	1.3257936	1.391423	1.0039275	1.282705	0.9345755	0.9676286	1.7846901	1.523941	0.7604063	1.421938	0.6772970	0.6316777	1.2057048	1.440349	1.6586744	0.6747659	0.6881360	1.427308	1.6647393	1.427308	0.2864957	0.2767584	1.4155859	0.2529364	0.8952973	1.6653179	2.2551545	1.427308	1.7379647	1.7858675	2.166725	2.2497669	2.526473	0.8060058	1.427308	1.427308	1.427308	2.2147695	1.7496568	1.9449862	1.6358682	2.4244493	2.4641126	0.9960722	1.479074	1.427774	1.1696249	1.427308	1.427308	1.380397	1.375669	1.226861	1.4501089	1.5427282	1.365239	0.7735454	0.6482422	1.427308	1.4262803	1.6720400	2.0277461	1.5381953	1.5568171	1.4943358	1.5688846	1.7151949	2.4334151	2.1097230	2.226605	2.1745476	2.0018560	2.2092904	2.3038110	2.3816816	1.2609286	1.624240	0.8187794	1.4920157	1.964348	1.8097492	1.1545290	1.427308	1.9241357	1.427308	0.2923403	0.2078571	0.2165135	1.1804132	1.3279195	0.4439340	0.3058182	0.6578301	1.2153664	1.0485953	1.7558352	1.427308	1.7663325	1.427308	1.9854095	2.0220956	2.2502364	1.427308	1.8854935	2.2468349	2.3495096	2.149482	1.3457286	1.1467440	1.2301213	2.0275799	2.154828	2.224401	0.7594091	1.427308	0.8614846	2.3595496	2.7288769	2.1471201	2.1270169	0.0977767	0.1355288	0.1413789	1.2766364	2.0511364	2.7141229	0.3693665	1.471891	1.427308	0.2652587	2.4142541	2.6276752	0.8725328	0.7816142	0.7583017	1.1651523	1.3337324	0.8402466	1.634760	0.6244944	2.5117515	2.5404445	1.427308	0.0346662	0.0537537	2.3638996	2.3441955
IgG1_OVA	0.6652026	0.3746467	36.304291	46.9882855	23.212248	55.633671	3.3725786	5.318673	5.6676617	0.8785644	6.1963630	0.1014175	0.0893574	0.3210754	0.0893574	5.318673	0.0893574	0.0893574	0.1721454	0.0893574	0.2019315	0.8148494	1.5944266	1.1401906	0.2622395	1.3189063	17.2320752	18.6990361	19.6447415	5.318673	13.6728234	0.1437259	0.2226591	0.1752992	0.4278846	0.3173785	0.2226591	0.2384453	0.2305522	0.3647380	0.4910307	2.0539019	3.0168831	1.6276643	3.6325598	3.608880	1.5408382	5.600546	1.1856399	1.2724662	0.6298718	33.771912	0.9725213	5.988903	0.6331100	0.5226040	0.6298718	5.832268	39.5828748	1.8802495	1.8960361	5.318673	0.9704728	5.318673	0.2384453	0.2068725	24.6833539	0.1595125	0.0893574	0.5778570	18.6468663	5.318673	0.2381496	0.2855096	0.222363	0.2539363	25.412543	0.4907351	5.318673	5.318673	5.318673	0.3587708	0.2065768	0.2381496	0.1279292	0.2768593	0.2172875	11.1624613	6.024703	5.720834	17.6744256	5.318673	5.318673	2.022033	2.006247	1.795945	2.0789123	4.8192239	3.841911	1.0274787	0.6643872	5.318673	0.5002542	0.5449333	6.1078134	0.0893574	0.0893574	0.0893574	0.0893574	0.0893574	0.7854815	6.4836841	4.664469	0.0893574	0.0893574	0.0893574	0.1942387	0.1478076	23.0538220	4.999596	15.1081109	35.2342496	12.366928	0.2716241	0.0893574	5.318673	0.5502105	5.318673	0.2871008	0.1108613	0.2019773	0.2716241	0.3180552	12.4210978	9.0854225	21.5989823	15.6480603	11.5698614	5.3283987	5.318673	5.6958447	5.318673	3.3414674	24.4151883	4.1307325	5.318673	25.7352719	22.6817327	27.7413316	2.747528	3.0080440	1.7219820	2.1710830	2.8695769	2.530165	2.732493	6.0360727	5.318673	6.4647603	0.4772673	2.8001494	7.4713645	4.3535151	2.2663469	3.0148485	3.8313956	3.0116560	1.0067606	2.9655325	10.4528809	7.286963	5.318673	6.0401564	3.4917519	0.4396801	0.4697499	0.3795409	0.3645058	0.3194013	0.6326966	0.0893574	4.862638	0.0893574	0.0893574	0.1621351	5.318673	0.1773143	0.2228527	0.3442879	0.2987494
IgG2_PT	1.0000000	1.0000000	1.000000	1.0000000	1.000000	1.000000	1.0000000	5.841741	1.0000000	1.0000000	1.0000000	6.8179846	5.9062777	5.4439445	5.6806374	5.841741	1.0000000	1.0000000	1.0000000	1.0000000	1.1834660	1.7441359	1.8935459	1.1045685	2.4458301	2.1302390	1.0000000	1.0000000	1.0000000	5.841741	3.5570056	1.2934566	1.3338771	1.1317744	1.4551387	1.4551387	1.2126155	1.2126155	1.2126155	1.6976619	1.6168208	1.4551387	2.6677542	2.1018670	2.4252312	5.254667	1.0000000	5.916438	1.0000000	1.0000000	1.0000000	1.000000	1.0000000	5.793798	1.0000000	1.0000000	1.0000000	5.685723	1.0000000	1.0000000	1.0000000	5.841741	1.2934566	5.841741	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.5359797	1.0000000	5.841741	1.0000000	1.0000000	1.506559	7.8556285	1.000000	1.0000000	5.841741	5.841741	5.841741	1.0000000	3.6587858	3.2283404	3.5482662	7.0078254	26.3015223	1.2913361	6.053704	6.117347	1.2913361	5.841741	5.841741	34.435632	32.713849	31.224740	29.7167292	33.1762896	5.833825	3.6049802	3.4435632	5.841741	3.4595594	4.9675727	4.3213210	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.2603855	4.3727651	6.687758	1.2432370	1.4090023	1.0000000	4.0612416	6.7963634	2.9008868	6.224041	4.2684474	3.5639465	2.735122	1.0000000	1.0000000	5.841741	1.0000000	5.841741	1.3261194	1.4918847	1.0000000	1.3261194	1.2432370	2.6522393	3.6053877	3.1080930	2.1549444	3.2324166	1.0000000	5.841741	1.0000000	5.841741	1.0000000	1.0000000	6.0704269	5.841741	1.0000000	1.0000000	1.0000000	4.372765	1.0000000	1.0000000	1.0000000	1.0000000	4.230892	4.012655	87.5238924	5.841741	75.5246477	1.0000000	5.9418163	119.9090776	144.4486284	6.9701490	9.1758928	6.5731153	7.0514860	7.9916840	6.1733155	1.0000000	5.848206	5.841741	1.0000000	1.0000000	1.0803304	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	1.0000000	5.680357	1.0000000	1.0000000	1.0000000	5.841741	1.0000000	1.0000000	1.5855780	1.4270201

# 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 2020 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