Imputation using MOFA for 2020 Dataset removed 20 samples, Batch corrected

Load libraries

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

Load data and Generate Mofa object

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

imputationTestingList <- list()

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

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

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

normalizedData <- list()

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

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

dataset_cols <- metaDf[metaDf$dataset=="2020_dataset", ]$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 = 15,
  color_by = "infancy_vac",
  dodge = TRUE,
  add_violin = TRUE
)

Results for Imputed Data

plot_object = imputed_data

plot_factor_cor(plot_object)

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

plot_variance_explained(plot_object, max_r2=1)

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

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

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

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

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

plot_factor(plot_object,
  factors = 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	0.1775434	-1.9182090	-1.5012426	-1.2412598	-1.3207808	-0.3790212	-3.1615927	NA	-2.3882227	-2.5981243	1.8689983	6.2373667	4.3402700	4.1486931	5.1598711	NA	-1.2966101	-1.9742597	-2.3253355	6.6478567	6.2358379	-0.0738690	0.0912969	0.6148360	5.4922934	5.5617456	1.2666233	1.5154083	-2.1394799	NA	3.6058519	2.2242539	-2.9434149	-3.2940013	-2.0979593	-1.5814126	-0.0709701	-3.1478009	-3.3512290	3.8128078	1.3126671	-3.143080	-3.1198630	-3.1745422	0.7795751	3.5502174	-3.2554107	NA	-3.2966945	-3.3035052	-2.2033014	-2.3895802	-2.7929854	NA	-3.1451752	-2.9689417	-2.2315068	NA	-2.7625818	-2.8186316	-3.2541749	NA	-2.4519558	NA	-2.7688606	-3.2541749	-2.9534814	-2.9658356	-3.4270289	-1.1262276	2.7898152	NA	-2.6939869	-2.4808145	-1.5933034	0.5232503	3.3901160	-3.3372946	NA	NA	NA	-1.7067630	-1.8554653	-2.0001836	-1.9087694	4.2757568	10.9944754	-2.9144862	NA	NA	-2.8450553	NA	NA	-2.2049170	-1.7253544	-1.2514839	0.4365656	0.6424520	NA	-2.4274793	-2.9469082	NA	-2.0374427	-1.2618380	-1.8732808	-2.7680748	-2.9290950	-2.9872820	1.3263462	2.9644034	-1.2626779	-2.0737247	4.2213116	-2.3196733	-2.4004617	-2.4028468	4.2934847	5.1615791	-2.4790683	NA	-2.6766074	-2.7038703	-2.0508611	-2.5770750	-2.6758547	NA	8.7744541	NA	-3.3552258	-3.3246915	-3.4270289	-3.0439770	-1.4155581	6.1132040	6.9018764	5.7190428	9.2250233	7.2072124	1.7102396	NA	2.0501964	NA	11.4276514	-3.4270289	0.2368605	NA	-3.4270289	-3.2660348	-3.4270289	-0.3868096	-3.0453944	-3.0848818	-3.2711132	-2.5777981	-0.6534762	-1.2797709	-0.6136370	NA	-3.4270289	-0.2955337	2.3745949	-0.0183160	2.3728068	13.1587887	5.1633587	2.5137141	3.0912979	8.1630716	1.7234910	-3.2480388	NA	NA	-3.2853639	5.4193544	7.1160011	-3.2407651	-3.2672994	-3.1152918	-2.5727410	-2.5329771	-3.4270289	NA	-3.4270289	-3.2096739	-3.2529421	NA	-1.5406537	-1.5883708	24.8264408	18.5075645
IgG_PRN	-0.2664616	-1.4084675	-1.2341212	-2.0350792	8.6411228	9.9514380	-1.5234659	NA	-0.6218860	2.1405807	12.4194641	-2.0403881	-2.0586271	-2.0573983	-2.0291414	NA	-0.7795278	-1.3499696	-1.6366892	-1.8125179	-1.9104714	-0.2805498	-2.0246704	-2.0169945	-1.9483401	-1.9209857	-1.9558985	-1.9501969	-0.4511683	NA	-2.0073400	-0.5862951	9.2569113	2.8906407	7.4394464	8.9832611	-1.1806791	-0.7766815	-1.7174357	-1.9365985	-1.9560530	-2.080035	-2.0523975	-2.1239679	-1.9004500	26.2269745	-1.4837518	NA	-1.5741569	-1.6123693	1.1872408	3.8042440	-1.7666671	NA	-2.0417819	-1.9022104	2.6889405	NA	1.9010396	-1.4162852	-2.0596409	NA	1.9159732	NA	-1.7200061	-2.1426401	0.5987520	-1.8760201	-1.9395211	-1.9730065	6.0806046	NA	1.7284665	2.9296775	4.0155034	-1.9504691	4.1461864	0.1165447	NA	NA	NA	1.8222966	-1.1721364	-1.2431623	-1.2818067	-1.8741727	-1.7415407	-2.2168565	NA	NA	-2.2007315	NA	NA	-1.2718529	-0.7193775	0.1147797	-1.9970487	3.8459373	NA	0.8964565	-0.7614756	NA	1.1517451	1.3318114	0.9214506	-1.9777467	-2.0569930	-2.1023104	-1.9761466	-1.9039178	-1.2594619	0.0184994	3.8999133	1.5839171	1.1396067	1.1369650	-1.9239234	-1.8970969	-1.1720872	NA	-1.3753983	-1.4143434	5.7471581	-1.8343945	-1.8882678	NA	-1.8522301	NA	-2.2513340	-2.2397442	-2.3086960	-1.2336348	7.5022888	-2.1011472	-2.0902224	-2.1053424	-1.9153482	-1.9309795	-1.9944205	NA	-1.9883370	NA	-1.8372382	-1.5225313	1.8823657	NA	-2.3248520	1.7487793	-1.8908386	1.3316498	-2.1633630	-2.1780887	-2.2587419	-0.4366543	-1.8596776	0.3808739	-0.7909501	NA	-2.0392327	-0.5624478	1.9001360	-1.9992288	-1.9274292	-2.0531764	-2.1752894	-2.2191322	-2.0537205	-1.9816220	1.7164226	-2.2913616	NA	NA	-2.2952900	-0.5454397	3.8101449	-2.2387884	-2.2431726	-0.5500689	0.8692341	0.9012675	-2.3102531	NA	-2.3046074	6.3517580	5.5704746	NA	2.8565350	2.5595021	5.4601917	3.9011688
IgG_FHA	28.0613098	-2.5793495	1.1089225	-1.4341834	66.4159927	16.9725800	-2.4813159	NA	-3.0577209	-2.5685899	-3.5335193	-1.3336864	-1.4477656	-1.4483809	-1.3598123	NA	6.1153202	1.4827271	-2.8666260	-0.6806469	-0.7158184	-2.6257050	-1.8966284	-1.8516359	-1.4629042	-1.5247996	-1.8373947	-1.8415339	7.6433306	NA	-1.2539155	9.6368856	-2.7845733	-3.2919197	12.4906454	-3.5335193	-0.4391351	-3.1285663	-3.3604131	-1.3435822	-1.4441152	-1.542067	-1.6622846	-1.8523204	-0.7158184	58.9181557	-3.1243575	NA	-3.1440032	-3.1528676	-1.6529603	-2.5921626	-3.3603220	NA	-3.4655411	-3.4176490	-2.3631580	NA	-2.9139936	-2.7040498	-3.2571447	NA	-1.3945518	NA	-1.5614085	-2.8852706	-3.0486054	-1.9787529	-1.5033069	-1.5307574	-3.5335193	NA	0.2205386	2.0547566	7.6768513	-1.2195704	7.1446867	-3.3083167	NA	NA	NA	2.1147380	-2.9188335	-2.9917574	-2.9646182	-1.4908748	-0.5034194	-3.0627172	NA	NA	-2.9849627	NA	NA	-1.3933091	-0.3947515	3.1452742	-1.4596686	-3.5335193	NA	-0.8128171	-2.2888887	NA	-1.3628054	-0.4391351	-2.8494787	-2.9012678	-3.0148933	-3.0644038	-1.4379005	-0.8623281	0.5169520	-2.9553888	-0.8855796	-2.8435552	-2.8988769	-2.8629706	-1.6010144	-1.1773982	-2.6597197	NA	-2.8295269	-2.8643422	0.4174809	-3.2000422	-3.2352481	NA	-1.3864009	NA	-3.3642855	-3.3519869	-3.4787526	0.2891197	-3.5335193	-1.6843772	-1.6480272	-1.6913369	-1.3067961	-1.2943463	-1.7886808	NA	-1.7724955	NA	-0.8266416	-3.2431321	-3.5335193	NA	-3.5335193	-1.6323407	-1.2489736	6.1840420	-3.1201210	-3.1352091	-3.3274226	-2.3137705	-1.1197290	0.7290092	-3.5335193	NA	-1.4282377	6.0036888	-3.5335193	-1.3042712	-0.7947035	-1.0604801	-0.8212199	-1.5370457	-1.6091835	-0.8063645	-3.5335193	-3.2592545	NA	NA	-3.2542133	5.7685871	-3.5335193	-3.2623641	-3.3000784	-3.0123711	1.8149095	1.9608793	-3.5146775	NA	-3.5127501	-1.3063426	-1.5312872	NA	-0.7873735	-0.9553404	14.5338469	29.8643074
IgG1_PT	5.3107424	-4.5164723	-5.5945406	-3.7484283	-0.9232473	28.2186470	-12.8137875	NA	-12.6360989	-12.3345642	-4.1810107	37.1115646	30.8475761	24.6828575	4.8338165	NA	-5.2144575	-6.3212843	-6.9836893	4.3730688	36.0499077	0.9189491	8.2553530	1.1382341	3.3419075	29.7213020	-10.9125996	-10.9255009	-10.8723764	NA	14.8370523	-12.5669851	-12.5245438	-12.5056801	-7.0047526	-6.5874081	-12.6966686	-12.6974545	-12.6408653	19.1268158	23.2861099	-12.711602	-12.6581564	-12.7398968	2.5053530	19.9465714	-12.7532578	NA	-12.7815523	-12.7839108	-9.1523571	-9.3925495	-11.2402859	NA	-11.6843529	-11.8981342	-11.3620434	NA	-8.3637409	-10.7388449	-10.8535948	NA	0.9703884	NA	-12.3681374	-12.3940744	-10.2949696	-12.4600945	-6.0108948	5.9175949	13.3002329	NA	-11.9151230	-11.7038136	-9.5574484	-1.0127492	29.6482697	-12.8651810	NA	NA	NA	-6.8997836	-8.8311777	-7.4044266	-8.4012651	25.0003090	39.6716652	-10.9634008	NA	NA	-9.9218311	NA	NA	-5.6066375	-5.5658731	-6.7218790	0.5623226	10.1592836	NA	-10.0898800	-11.2229614	NA	-8.0569868	-3.6085501	-1.9718113	-11.2650337	-11.1464300	-11.5487747	8.1315012	15.1126070	7.6733809	-1.7721162	22.9530067	-9.7635069	-10.5265274	-7.6983089	20.0620575	35.7031746	-12.5255127	NA	-12.7633266	-12.1306028	-11.0932741	-9.6373882	-11.3041649	NA	43.0330505	NA	-12.8628807	-12.9236088	-12.9054461	-12.7966690	-12.7588329	25.6932678	7.6299086	35.7780571	38.9326096	33.5575752	17.8011742	NA	19.8263626	NA	41.3250580	-12.9221191	5.6132336	NA	-12.9221191	-12.8656006	-12.6562920	1.7381372	-12.6811428	-12.8467693	-12.7557812	-11.9183750	-3.6018324	2.5259638	-10.0361013	NA	-9.0409222	3.3778276	21.0875015	-4.9611096	-6.7529583	8.4358339	10.9451036	11.4043074	21.3174248	29.0150719	19.2978287	-12.7258577	NA	NA	-12.8337097	-10.7845182	35.2919769	-12.9236088	-12.9236088	-12.9236088	-12.7764139	-11.6914158	-12.9236088	NA	-12.9236088	-12.9236088	-12.8650875	NA	-7.8843665	-6.1922579	56.9808159	52.8651924
IgG1_PRN	0.5836387	-0.2182302	-0.4338741	-0.4939656	0.7812977	1.5184584	-1.7075391	NA	-1.6645025	-1.5183375	-0.4293845	-0.1532995	-0.3599730	-0.5643780	-0.7652701	NA	-0.1837747	-0.2868739	-0.5277286	0.2171468	1.3512182	-0.1831510	0.2229871	-0.3388543	-0.1290329	0.9297349	-0.8238397	-0.7010627	-0.6449368	NA	0.2020707	1.1111410	1.5343816	1.2032275	1.6415880	1.3062749	-0.2212590	-0.2450826	-0.1417083	0.8926291	0.9105711	-1.528410	-1.4901493	-1.5397570	0.7332900	0.7476971	-0.2121692	NA	-0.3162862	-0.3238907	0.3508272	1.6640940	-1.2353375	NA	-1.3272753	-1.3597134	0.2874024	NA	1.7603393	-0.4902402	-0.5183711	NA	-0.5298381	NA	-1.2978967	-1.3331866	1.6240985	-1.3607531	0.4288189	0.8635921	1.8261559	NA	1.0754619	1.1006172	1.2182674	1.4578793	2.0844631	0.4075572	NA	NA	NA	0.9079003	-1.6062375	-1.5584702	-1.5981234	1.3518195	1.3257031	-1.7227712	NA	NA	-1.7123802	NA	NA	0.6357236	0.6027207	0.3264422	0.8197267	1.2487571	NA	0.7318165	0.5330858	NA	0.4560485	0.6736450	1.0927658	-1.4343462	-1.3923340	-1.4374583	0.6468575	0.9622691	0.6687171	1.1054044	1.4272566	0.8159201	0.6161621	1.0767393	1.4135094	1.7759974	-0.4583679	NA	-0.8428288	-0.1372392	1.1908610	-0.9433998	-1.2446773	NA	1.3683000	NA	-1.7625248	-1.7701080	-1.7748574	-0.3326055	0.4805336	-1.5259496	-1.5960537	-1.3847563	1.1839428	1.0642865	0.1872531	NA	0.2275022	NA	1.3532410	-1.2453778	1.2244632	NA	-1.2235131	0.9455297	1.3327246	1.0246775	-1.6601901	-1.7310611	-1.6934785	-0.1901067	1.0992558	1.0979493	-0.4845189	NA	-0.2782775	0.4467261	1.4331758	0.9023359	1.1885550	-1.7540766	-1.7361413	-1.7264299	-0.7825124	0.0875368	1.4627726	-1.7696346	NA	NA	-1.7774737	-1.5358046	1.4206333	-1.7385660	-1.7548202	-1.7580199	-0.6230745	0.4446220	-1.7617635	NA	-1.7824972	0.6746366	1.0931575	NA	1.3322668	1.4984848	1.9636042	1.8612773
IgG1_FHA	2.1896586	-0.4827456	0.9848995	0.9259772	0.8197672	2.6758797	-0.6902144	NA	-0.7248180	-0.7746767	1.6724644	1.2877469	1.0792770	0.8764617	-0.1774995	NA	1.0508599	0.8833599	0.5766098	0.7260783	2.4746754	-1.2195910	-0.6766056	-1.2315463	-0.5321199	0.7860157	-2.2998071	-2.2525144	-2.2641652	NA	0.9429374	-1.2215143	-1.1076941	-1.0804830	2.1586535	1.9922378	-1.6680492	-1.6737635	-1.5638305	1.5111837	1.8474746	0.218266	0.2812402	0.1085662	1.9364550	1.8834321	-1.3527498	NA	-1.4185231	-1.4084160	0.1593513	-0.6080153	-2.2166646	NA	-2.2657225	-2.3182788	-1.1511376	NA	-0.4194939	-0.9371191	-0.9722604	NA	0.1010915	NA	-0.3139062	-0.2833909	-0.8625193	-0.3592322	0.2391768	1.4392686	1.7084007	NA	0.8952918	0.9102635	1.0975325	1.7901199	2.2215936	-2.0451696	NA	NA	NA	0.8136718	-1.7231545	-1.5464064	-1.7709496	0.7988477	1.8923137	-1.2565200	NA	NA	-0.8843291	NA	NA	0.8748169	0.8035996	0.5912287	1.1805913	1.7857397	NA	0.6388290	0.1882182	NA	0.0799135	0.5374608	-0.5025408	-1.8072490	-1.8065678	-1.8635008	0.7449999	1.9145100	2.3230860	-0.3952787	0.5600455	-1.2102602	-1.3878250	-0.7845484	0.3354826	2.3578513	-1.0048590	NA	-1.5825576	-0.6249858	0.5592828	-1.8772191	-2.2000623	NA	1.5167377	NA	-2.3659787	-2.4470203	-2.4715343	0.5225642	0.6803029	-0.5264884	-1.1626905	0.0352719	1.3076885	1.1664453	-0.0766038	NA	-0.0192666	NA	2.1927197	-2.0816298	1.9490499	NA	-2.1237459	0.3639867	1.9710567	1.6577356	-1.9571284	-2.2049489	-2.0780830	-0.9318129	1.4101307	1.8738041	0.7400808	NA	0.8870442	2.0054440	2.3286932	1.7985840	1.8877289	-0.3188827	-0.2660527	-0.1726582	0.2133904	1.6233664	2.3380377	-1.6347257	NA	NA	-1.8688335	-0.1766101	3.0109050	-2.0141251	-2.1843631	-2.1703053	-0.4286762	1.2512436	-2.6180437	NA	-2.6509535	-2.2047591	0.4505558	NA	0.3864052	0.9029980	2.4402230	3.6141083
IgG1_TT	0.2402147	-0.2566868	0.1252924	0.1630322	0.5428075	0.6740308	-1.2900429	NA	-1.2680643	-0.3843768	0.4281839	0.0658787	-0.0610327	-0.1452347	-0.2378706	NA	0.2981180	0.2460173	0.1361198	0.3440421	0.7316295	-0.0992582	0.1498328	-0.1526445	0.3131125	0.6370213	-0.6660142	-0.5575152	-0.5452248	NA	-0.0774029	0.1493797	0.2224813	0.2160029	0.9060086	0.6673689	-0.6610156	-0.6781712	-0.5975116	0.4675794	0.4972119	-0.789803	-0.7330374	-0.8072586	0.5397211	0.4602213	-0.2324039	NA	-0.2851705	-0.2715739	0.2663577	0.1843317	-0.3846254	NA	-0.4482293	-0.4669846	0.3609977	NA	0.2558390	-0.1506845	-0.1236113	NA	0.4458575	NA	-0.0990175	-0.1181526	0.1279162	-0.1540236	0.2112806	0.4763771	0.5608919	NA	-0.1523331	-0.1225560	0.4655516	0.6655099	0.6664532	0.1144902	NA	NA	NA	0.6438673	0.1330340	0.2318529	0.0680106	0.5436907	0.5954311	-0.3891944	NA	NA	-0.2480305	NA	NA	-0.1317356	-0.1136024	-0.2154231	0.1277348	0.4207736	NA	-0.1739780	-0.3027176	NA	-0.1639084	-0.0298264	0.5423802	-0.6434880	-0.6410046	-0.7351473	-0.3228036	-0.1832418	0.3012990	0.5542163	0.6622144	0.1399595	0.0025532	0.3231404	0.6804616	0.8329085	-0.0456909	NA	-0.2483060	0.0941437	0.4467713	0.0450220	-0.1869711	NA	0.6195647	NA	-0.2239945	-0.3535454	-0.3462988	0.5685450	0.5733000	-0.7556602	-0.8194721	-0.5829688	0.0203489	-0.0225892	-0.4947224	NA	-0.4597468	NA	0.5292799	-0.2516431	0.3581240	NA	-0.2673715	0.4386194	0.5554541	0.2842822	-0.3966792	-0.5274484	-0.4878389	0.1125005	0.3313483	0.3306842	-0.8200397	NA	-0.7381672	0.1904954	0.6006386	0.5286261	0.5458905	-0.9242743	-0.8724166	-0.8659752	-0.1540418	0.2533274	0.6129456	-0.3613109	NA	NA	-0.4854658	0.6418393	0.7105412	-0.7148954	-0.7621574	-0.8105866	-0.1063460	0.2460173	0.1274248	NA	-0.0687084	0.5452266	0.6647595	NA	-0.9123724	-0.8129344	0.7456777	0.6441841
IgG1_DT	0.9090480	-0.1152487	0.5327393	0.5873984	0.0139023	0.7557732	-1.3342921	NA	-1.3210682	-1.3208923	-0.5979035	0.6990103	0.5815021	0.5441642	-0.0364273	NA	0.3144583	0.1993924	0.1652523	-0.0669557	0.5669218	-0.6437024	-0.3984704	-0.6476243	-0.5649316	0.0659206	-0.5451593	-0.4404129	-0.4233171	NA	0.3037740	0.5498568	0.6827766	0.6098579	1.0700530	0.8728546	-0.6795725	-0.6768707	-0.6417201	0.3768896	0.4549022	-1.066567	-1.0246906	-1.0867742	-0.0959418	-0.0339146	-0.4001400	NA	-0.4656852	-0.4344465	0.3377650	0.0913289	-0.6302940	NA	-0.7088412	-0.7519563	-0.2094387	NA	0.2186664	-0.7112334	-0.6985971	NA	0.2243984	NA	-1.0781906	-1.0873934	-0.0110785	-1.1099079	-0.5028073	0.2249453	0.7824045	NA	0.2936045	0.3388777	0.6988293	0.7773129	1.0388304	-0.5871975	NA	NA	NA	0.7442364	0.3046547	0.4892621	0.1971122	0.9424065	0.9798926	-0.4075642	NA	NA	-0.2435381	NA	NA	-0.0443361	-0.0488038	-0.1894439	0.0215496	0.1090848	NA	-0.6178760	-0.7363011	NA	-0.0009711	0.2312983	0.5674790	0.1048007	0.1224004	0.0633488	0.1338054	0.2720845	0.9508802	0.6449562	0.7554220	0.7062224	0.5430101	0.7390581	0.8283902	0.9019865	-0.1572462	NA	-0.5751250	0.0611560	0.5075613	0.3614485	-0.2578055	NA	0.4695561	NA	-1.0726669	-1.1525127	-1.1443315	-0.2333419	-0.0939326	-0.9293945	-1.0599289	-0.7272395	-0.2003075	-0.3579241	0.3104939	NA	0.3204150	NA	0.5274665	0.5621388	0.7777566	NA	0.4330351	0.7745417	0.8715805	0.6825327	-0.0771011	-0.2651629	-0.1863625	0.5673219	0.6875857	0.7533394	-0.6312364	NA	-0.5347640	0.8810693	1.2301234	0.6803006	0.6613008	-1.2565506	-1.2208707	-1.2153418	-0.1424007	0.5895854	1.2161795	-0.9998688	NA	NA	-1.0982620	0.9327711	1.1344770	-0.5243222	-0.6102502	-0.6322830	-0.2477652	-0.0884389	-0.5548362	NA	-0.7587453	1.0249165	1.0520345	NA	-1.3161968	-1.2981570	0.8851806	0.8665580
IgG1_OVA	-3.8584220	-4.0993366	25.6917114	34.5503387	14.8364506	41.7186546	-1.6136026	NA	0.2893648	-3.6815131	0.7277369	-4.3258843	-4.3358836	-4.1437550	-4.3358836	NA	-4.3358836	-4.3358836	-4.2672400	-4.3358836	-4.2425432	-3.7343426	-3.0879564	-3.4645863	-4.1925387	-3.3164039	9.8779926	11.0943222	11.8784542	NA	6.9268403	-4.2908039	-4.2253571	-4.2646251	-4.0551944	-4.1468205	-4.2253571	-4.2122674	-4.2188125	-4.1075521	-4.0028367	-2.706983	-1.9085271	-3.0603974	-1.3980393	-1.4176733	-3.1323893	NA	-3.4269013	-3.3549097	-3.8877165	23.5919914	-3.6036088	NA	-3.8850317	-3.9766579	-3.8877165	NA	28.4101505	-2.8509665	-2.8378770	NA	-3.6053076	NA	-4.2122674	-4.2384462	16.0562172	-4.2777147	-4.3358836	-3.9308448	11.0510654	NA	-4.2125130	-4.1732445	-4.2256021	-4.1994233	16.6608238	-4.0030818	NA	NA	NA	-4.1124997	-4.2386918	-4.2125130	-4.3039017	-4.1804166	-4.2298107	4.8453755	NA	NA	10.2447662	NA	NA	-2.7334065	-2.7464962	-2.9208674	-2.6862454	-0.4141173	NA	-3.5580411	-3.8590980	NA	-3.9951887	-3.9581432	0.6543159	-4.3358836	-4.3358836	-4.3358836	-4.3358836	-4.3358836	-3.7586930	0.9659691	-0.5424323	-4.3358836	-4.3358836	-4.3358836	-4.2489214	-4.2874198	14.7050915	NA	8.1169071	24.8044891	5.8440585	-4.1847577	-4.3358836	NA	-3.9537678	NA	-4.1719251	-4.3180542	-4.2425051	-4.1847577	-4.1462593	5.8889728	3.1231995	13.4988117	8.5646076	5.1831708	0.0080647	NA	0.3127327	NA	-1.6393983	15.8338680	-0.9849796	NA	16.9284153	14.3965740	18.5917377	-2.1318634	-1.9158564	-2.9821939	-2.6098220	-2.0306661	-2.3120890	-2.1443295	0.5948324	NA	0.9502783	-4.0142484	-2.0882318	1.7849040	-0.8002594	-2.5308340	-1.9102142	-1.2331746	-1.9128611	-3.5752192	-1.9511044	4.2570271	NA	NA	0.5982184	-1.5147901	-4.0454140	-4.0204816	-4.0952783	-4.1077447	-4.1451430	-3.8853745	-4.3358836	NA	-4.3358836	-4.3358836	-4.2755404	NA	-4.2629542	-4.2251964	-4.1245084	-4.1622663
IgG2_PT	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.4679184	NA	-4.4679184	-4.4679184	-4.4679184	0.9008713	0.0595546	-0.3670826	-0.1486640	NA	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.2986174	-3.7812362	-3.6433616	-4.3714237	-3.1337180	-3.4249430	-4.4679184	-4.4679184	-4.4679184	NA	-2.1083341	-4.1971188	-4.1598196	-4.3463182	-4.0479202	-4.0479202	-4.2717185	-4.2717185	-4.2717185	-3.8241217	-3.8987212	-4.047920	-2.9289284	-3.4511244	-3.1527267	-0.5417457	-4.4679184	NA	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.4679184	NA	-4.4679184	-4.4679184	-4.4679184	NA	-4.4679184	-4.4679184	-4.4679184	NA	-4.1971188	NA	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-3.9733207	-4.4679184	NA	-4.4679184	-4.4679184	-4.0004702	1.8584003	-4.4679184	-4.4679184	NA	NA	NA	-4.4679184	-2.0144124	-2.4116240	-2.1163993	1.0760546	18.8801212	-4.1990757	NA	NA	-4.1990757	NA	NA	26.3862133	24.7973652	23.4232292	22.0316467	25.2241001	NA	-2.0640635	-2.2130179	NA	-2.1982570	-0.8066745	-1.4030304	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.2276373	-1.3555579	0.7806993	-4.2434611	-4.0904946	-4.4679184	-1.6430297	0.8809185	-2.7137957	NA	-1.4518213	-2.1019292	-2.8667622	-4.4679184	-4.4679184	NA	-4.4679184	NA	-4.1669779	-4.0140114	-4.4679184	-4.1669779	-4.2434611	-2.9432454	-2.0636878	-2.5225873	-3.4021449	-2.4078622	-4.4679184	NA	-4.4679184	NA	-4.4679184	-4.4679184	0.2110305	NA	-4.4679184	-4.4679184	-4.4679184	-1.3555579	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-1.4864779	-1.6878648	75.3756332	NA	64.3028336	-4.4679184	0.0923495	105.2604218	127.9053116	1.0412865	3.0767288	0.6749072	1.1163435	1.9839506	0.3059754	-4.4679184	NA	NA	-4.4679184	-4.4679184	-4.3937902	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.4679184	-4.4679184	NA	-4.4679184	-4.4679184	-4.4679184	NA	-4.4679184	-4.4679184	-3.9275520	-4.0738678

# 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	3.7817953	1.6860429	2.103009	2.3629920	2.283471	3.225231	0.4426591	3.604252	1.2160292	1.0061276	5.4732502	9.8416185	7.9445219	7.7529449	8.7641230	3.604252	2.3076417	1.6299921	1.2789164	10.2521086	9.8400898	3.5303829	3.6955488	4.2190878	9.0965452	9.1659975	4.8708751	5.1196601	1.4647720	3.604252	7.2101038	5.8285058	0.6608369	0.3102505	1.5062926	2.0228393	3.5332818	0.4564509	0.2530229	7.4170597	4.9169190	0.4611714	0.4843888	0.4297097	4.3838270	7.154469	0.3488412	3.001810	0.3075573	0.3007467	1.4009504	1.2146716	0.8112664	3.934220	0.4590766	0.6353102	1.3727450	4.253003	0.8416700	0.7856202	0.3500769	3.604252	1.152296	3.604252	0.8353913	0.3500769	0.6507704	0.6384163	0.1772230	2.4780242	6.394067	3.604252	0.9102650	1.1234374	2.0109484	4.1275022	6.994368	0.2669573	3.604252	3.604252	3.604252	1.8974888	1.7487866	1.6040683	1.6954825	7.8800087	14.5987272	0.6897657	2.966209	2.377325	0.7591965	3.604252	3.604252	1.399335	1.878897	2.352768	4.0408175	4.246704	3.056696	1.1767726	0.6573436	3.604252	1.5668092	2.3424139	1.730971	0.8361771	0.6751568	0.6169698	4.9305980	6.5686553	2.3415740	1.530527	7.825563	1.2845786	1.2037902	1.2014050	7.8977365	8.7658310	1.1251836	3.499111	0.9276445	0.9003816	1.553391	1.0271769	0.9283972	3.604252	12.3787060	3.604252	0.2490261	0.2795603	0.1772230	0.5602748	2.1886938	9.7174559	10.5061283	9.3232946	12.8292751	10.8114643	5.3144915	3.604252	5.6544483	3.604252	15.0319033	0.1772230	3.841112	3.604252	0.1772230	0.338217	0.177223	3.217442	0.5588574	0.5193701	0.3331387	1.0264537	2.950776	2.324481	2.9906149	3.604252	0.1772230	3.3087182	5.978847	3.5859358	5.9770586	16.7630405	8.7676105	6.1179659	6.6955497	11.7673235	5.327743	0.3562131	3.312713	3.604252	0.3188879	9.0236063	10.7202530	0.3634868	0.3369524	0.4889600	1.0315108	1.0712748	0.1772230	2.404909	0.1772230	0.3945780	0.3513098	3.604252	2.0635982	2.0158811	28.4306927	22.1118164
IgG_PRN	2.4563730	1.3143671	1.488713	0.6877553	11.363957	12.674273	1.1993687	2.722835	2.1009486	4.8634152	15.1422987	0.6824465	0.6642075	0.6654363	0.6936932	2.722835	1.9433068	1.3728650	1.0861454	0.9103167	0.8123631	2.4422848	0.6981642	0.7058401	0.7744945	0.8018489	0.7669361	0.7726377	2.2716663	2.722835	0.7154946	2.1365395	11.9797459	5.6134753	10.1622810	11.7060957	1.5421555	1.9461530	1.0053989	0.7862360	0.7667816	0.6427991	0.6704371	0.5988667	0.8223846	28.949809	1.2390828	3.124200	1.1486777	1.1104653	3.9100754	6.5270786	0.9561675	3.225416	0.6810527	0.8206242	5.4117751	2.631804	4.6238742	1.3065494	0.6631937	2.722835	4.638808	2.722835	1.0028285	0.5801945	3.3215866	0.8468145	0.7833135	0.7498281	8.803439	2.722835	4.4513011	5.6525121	6.7383380	0.7723655	6.869021	2.8393793	2.722835	2.722835	2.722835	4.5451312	1.5506982	1.4796723	1.4410279	0.8486619	0.9812939	0.5059781	2.382829	2.644869	0.5221031	2.722835	2.722835	1.450982	2.003457	2.837614	0.7257859	6.568772	3.613259	3.6192911	1.9613590	2.722835	3.8745797	4.0546460	3.644285	0.7450879	0.6658416	0.6205242	0.7466880	0.8189168	1.4633727	2.741334	6.622748	4.3067517	3.8624413	3.8597996	0.7989112	0.8257377	1.5507474	2.298520	1.3474363	1.3084912	8.469993	0.8884401	0.8345668	2.722835	0.8706045	2.722835	0.4715006	0.4830904	0.4141386	1.4891998	10.2251234	0.6216874	0.6326122	0.6174922	0.8074864	0.7918551	0.7284141	2.722835	0.7344975	2.722835	0.8855964	1.2003033	4.605200	2.722835	0.3979826	4.471614	0.831996	4.054484	0.5594716	0.5447459	0.4640927	2.2861803	0.863157	3.103709	1.9318845	2.722835	0.6836019	2.1603868	4.622971	0.7236058	0.7954054	0.6696582	0.5475452	0.5037024	0.6691141	0.7412126	4.439257	0.4314730	2.436757	2.722835	0.4275446	2.1773949	6.5329795	0.4840462	0.4796619	2.1727657	3.5920687	3.6241021	0.4125814	2.912221	0.4182272	9.0745926	8.2933092	2.722835	5.5793695	5.2823367	8.1830263	6.6240034
IgG_FHA	33.2427626	2.6021032	6.290375	3.7472694	71.597446	22.154033	2.7001369	5.181453	2.1237319	2.6128628	1.6479335	3.8477664	3.7336872	3.7330718	3.8216405	5.181453	11.2967730	6.6641798	2.3148267	4.5008059	4.4656343	2.5557477	3.2848244	3.3298168	3.7185485	3.6566532	3.3440580	3.3399189	12.8247833	5.181453	3.9275372	14.8183384	2.3968794	1.8895330	17.6720982	1.6479335	4.7423177	2.0528865	1.8210397	3.8378706	3.7373376	3.6393855	3.5191681	3.3291323	4.4656343	64.099608	2.0570953	4.980099	2.0374496	2.0285852	3.5284925	2.5892901	1.8211308	5.297659	1.7159116	1.7638037	2.8182948	5.353971	2.2674592	2.4774029	1.9243081	5.181453	3.786901	5.181453	3.6200442	2.2961822	2.1328473	3.2026999	3.6781459	3.6506953	1.647933	5.181453	5.4019914	7.2362094	12.8583040	3.9618824	12.326140	1.8731360	5.181453	5.181453	5.181453	7.2961907	2.2626193	2.1896954	2.2168345	3.6905780	4.6780334	2.1187356	4.457584	4.335534	2.1964900	5.181453	5.181453	3.788144	4.786701	8.326727	3.7217841	1.647933	5.407448	4.3686357	2.8925641	5.181453	3.8186474	4.7423177	2.331974	2.2801850	2.1665595	2.1170490	3.7435522	4.3191247	5.6984048	2.226064	4.295873	2.3378975	2.2825758	2.3184822	3.5804384	4.0040545	2.5217330	4.254328	2.3519258	2.3171105	5.598934	1.9814105	1.9462047	5.181453	3.7950518	5.181453	1.8171673	1.8294659	1.7027001	5.4705725	1.6479335	3.4970756	3.5334256	3.4901159	3.8746567	3.8871064	3.3927720	5.181453	3.4089572	5.181453	4.3548112	1.9383206	1.647933	5.181453	1.6479335	3.549112	3.932479	11.365495	2.0613317	2.0462437	1.8540301	2.8676822	4.061724	5.910462	1.6479335	5.181453	3.7532151	11.1851416	1.647933	3.8771815	4.3867493	4.1209726	4.3602328	3.6444070	3.5722692	4.3750882	1.647933	1.9221983	5.032903	5.181453	1.9272394	10.9500399	1.6479335	1.9190886	1.8813744	2.1690817	6.9963622	7.1423321	1.6667752	5.546084	1.6687026	3.8751101	3.6501656	5.181453	4.3940792	4.2261124	19.7152996	35.0457602
IgG1_PT	14.1694136	4.3421988	3.264131	5.1102428	7.935424	37.077318	-3.9551163	8.858671	-3.7774277	-3.4758930	4.6776605	45.9702358	39.7062473	33.5415287	13.6924877	8.858671	3.6442137	2.5373869	1.8749819	13.2317400	44.9085789	9.7776203	17.1140242	9.9969053	12.2005787	38.5799732	-2.0539284	-2.0668297	-2.0137053	8.858671	23.6957235	-3.7083139	-3.6658726	-3.6470089	1.8539186	2.2712631	-3.8379974	-3.8387833	-3.7821941	27.9854870	32.1447811	-3.8529310	-3.7994852	-3.8812256	11.3640242	28.805242	-3.8945866	8.389017	-3.9228811	-3.9252396	-0.2936859	-0.5338783	-2.3816147	9.306126	-2.8256817	-3.0394630	-2.5033722	9.353470	0.4949303	-1.8801737	-1.9949236	8.858671	9.829060	8.858671	-3.5094662	-3.5354033	-1.4362984	-3.6014233	2.8477764	14.7762661	22.158904	8.858671	-3.0564518	-2.8451424	-0.6987772	7.8459220	38.506941	-4.0065098	8.858671	8.858671	8.858671	1.9588876	0.0274935	1.4542446	0.4574060	33.8589802	48.5303364	-2.1047297	8.605161	7.925715	-1.0631599	8.858671	8.858671	3.252034	3.292798	2.136792	9.4209938	19.017955	8.870193	-1.2312088	-2.3642902	8.858671	0.8016844	5.2501211	6.886860	-2.4063625	-2.2877588	-2.6901035	16.9901724	23.9712782	16.5320520	7.086555	31.811678	-0.9048357	-1.6678562	1.1603622	28.9207287	44.5618458	-3.6668415	8.998995	-3.9046555	-3.2719316	-2.234603	-0.7787170	-2.4454937	8.858671	51.8917217	8.858671	-4.0042095	-4.0649376	-4.0467749	-3.9379978	-3.9001617	34.5519390	16.4885798	44.6367283	47.7912807	42.4162464	26.6598454	8.858671	28.6850338	8.858671	50.1837292	-4.0634480	14.471905	8.858671	-4.0634480	-4.006929	-3.797621	10.596808	-3.8224716	-3.9880981	-3.8971100	-3.0597038	5.256839	11.384635	-1.1774302	8.858671	-0.1822510	12.2364988	29.946173	3.8975616	2.1057129	17.2945051	19.8037748	20.2629786	30.1760960	37.8737431	28.156500	-3.8671865	8.857524	8.858671	-3.9750385	-1.9258471	44.1506481	-4.0649376	-4.0649376	-4.0649376	-3.9177427	-2.8327446	-4.0649376	8.334029	-4.0649376	-4.0649376	-4.0064163	8.858671	0.9743047	2.6664133	65.8394871	61.7238636
IgG1_PRN	2.3302746	1.5284057	1.312762	1.2526703	2.527934	3.265094	0.0390968	1.746636	0.0821334	0.2282984	1.3172514	1.5933365	1.3866630	1.1822579	0.9813659	1.746636	1.5628612	1.4597620	1.2189074	1.9637827	3.0978541	1.5634849	1.9696230	1.4077816	1.6176031	2.6763709	0.9227962	1.0455732	1.1016991	1.746636	1.9487066	2.8577769	3.2810175	2.9498634	3.3882239	3.0529108	1.5253769	1.5015533	1.6049277	2.6392651	2.6572070	0.2182263	0.2564867	0.2068789	2.4799259	2.494333	1.5344667	1.657471	1.4303497	1.4227452	2.0974631	3.4107299	0.5112984	1.934677	0.4193606	0.3869225	2.0340383	1.705066	3.5069752	1.2563957	1.2282648	1.746636	1.216798	1.746636	0.4487392	0.4134493	3.3707345	0.3858829	2.1754549	2.6102281	3.572792	1.746636	2.8220978	2.8472531	2.9649034	3.2045152	3.831099	2.1541932	1.746636	1.746636	1.746636	2.6545362	0.1403984	0.1881657	0.1485125	3.0984554	3.0723391	0.0238647	1.749896	1.588064	0.0342557	1.746636	1.746636	2.382360	2.349357	2.073078	2.5663626	2.995393	2.068472	2.4784524	2.2797217	1.746636	2.2026844	2.4202809	2.839402	0.3122897	0.3543019	0.3091776	2.3934934	2.7089050	2.4153531	2.852040	3.173893	2.5625560	2.3627980	2.8233752	3.1601453	3.5226333	1.2882680	1.748285	0.9038072	1.6093967	2.937497	0.8032361	0.5019586	1.746636	3.1149359	1.746636	-0.0158889	-0.0234721	-0.0282215	1.4140304	2.2271695	0.2206863	0.1505822	0.3618796	2.9305787	2.8109224	1.9338890	1.746636	1.9741381	1.746636	3.0998769	0.5012581	2.971099	1.746636	0.5231228	2.692166	3.079360	2.771313	0.0864458	0.0155748	0.0531574	1.5565292	2.845892	2.844585	1.2621170	1.746636	1.4683584	2.1933620	3.179812	2.6489718	2.9351909	-0.0074407	0.0104946	0.0202060	0.9641235	1.8341727	3.209408	-0.0229987	2.016411	1.746636	-0.0308378	0.2108313	3.1672692	0.0080699	-0.0081843	-0.0113840	1.1235614	2.1912580	-0.0151275	1.393110	-0.0358613	2.4212725	2.8397934	1.746636	3.0789027	3.2451208	3.7102401	3.6079133
IgG1_FHA	3.9798988	1.3074945	2.775140	2.7162174	2.610007	4.466120	1.1000258	1.790240	1.0654222	1.0155635	3.4627045	3.0779871	2.8695172	2.6667019	1.6127406	1.790240	2.8411001	2.6736001	2.3668500	2.5163184	4.2649156	0.5706491	1.1136346	0.5586939	1.2581203	2.5762559	-0.5095669	-0.4622742	-0.4739250	1.790240	2.7331775	0.5687258	0.6825460	0.7097572	3.9488937	3.7824780	0.1221910	0.1164767	0.2264097	3.3014239	3.6377147	2.0085062	2.0714804	1.8988063	3.7266952	3.673672	0.4374903	1.305859	0.3717171	0.3818241	1.9495915	1.1822249	-0.4264244	1.795413	-0.4754823	-0.5280386	0.6391026	1.520926	1.3707463	0.8531211	0.8179798	1.790240	1.891332	1.790240	1.4763340	1.5068493	0.9277209	1.4310080	2.0294169	3.2295088	3.498641	1.790240	2.6855320	2.7005037	2.8877727	3.5803601	4.011834	-0.2549294	1.790240	1.790240	1.790240	2.6039120	0.0670856	0.2438338	0.0192906	2.5890878	3.6825539	0.5337201	1.595256	1.541354	0.9059110	1.790240	1.790240	2.665057	2.593840	2.381469	2.9708315	3.575980	2.213841	2.4290692	1.9784584	1.790240	1.8701537	2.3277010	1.287699	-0.0170088	-0.0163276	-0.0732607	2.5352401	3.7047502	4.1133262	1.394961	2.350286	0.5799800	0.4024152	1.0056918	2.1257228	4.1480914	0.7853812	1.749422	0.2076826	1.1652544	2.349523	-0.0869789	-0.4098221	1.790240	3.3069779	1.790240	-0.5757385	-0.6567801	-0.6812941	2.3128043	2.4705430	1.2637517	0.6275496	1.8255121	3.0979286	2.9566854	1.7136364	1.790240	1.7709736	1.790240	3.9829599	-0.2913896	3.739290	1.790240	-0.3335057	2.154227	3.761297	3.447976	-0.1668882	-0.4147087	-0.2878429	0.8584273	3.200371	3.664044	2.5303210	1.790240	2.6772844	3.7956842	4.118933	3.5888242	3.6779691	1.4713575	1.5241874	1.6175820	2.0036305	3.4136065	4.128278	0.1555145	1.303303	1.790240	-0.0785934	1.6136301	4.8011452	-0.2238849	-0.3941230	-0.3800651	1.3615639	3.0414838	-0.8278035	1.296769	-0.8607134	-0.4145190	2.2407960	1.790240	2.1766454	2.6932381	4.2304631	5.4043485
IgG1_TT	1.4942536	0.9973521	1.379331	1.4170711	1.796846	1.928070	-0.0360039	1.254039	-0.0140253	0.8696622	1.6822228	1.3199177	1.1930063	1.1088042	1.0161684	1.254039	1.5521569	1.5000563	1.3901588	1.5980810	1.9856684	1.1547807	1.4038718	1.1013944	1.5671514	1.8910602	0.5880247	0.6965237	0.7088141	1.254039	1.1766360	1.4034187	1.4765202	1.4700419	2.1600475	1.9214078	0.5930234	0.5758677	0.6565273	1.7216183	1.7512509	0.4642359	0.5210015	0.4467803	1.7937601	1.714260	1.0216351	1.132771	0.9688684	0.9824650	1.5203966	1.4383706	0.8694136	1.368032	0.8058096	0.7870544	1.6150366	1.178378	1.5098779	1.1033545	1.1304276	1.254039	1.699896	1.254039	1.1550214	1.1358863	1.3819551	1.1000153	1.4653195	1.7304161	1.814931	1.254039	1.1017058	1.1314830	1.7195905	1.9195489	1.920492	1.3685291	1.254039	1.254039	1.254039	1.8979062	1.3870729	1.4858918	1.3220495	1.7977296	1.8494700	0.8648446	1.362192	1.277649	1.0060084	1.254039	1.254039	1.122303	1.140437	1.038616	1.3817737	1.674813	1.257269	1.0800610	0.9513213	1.254039	1.0901306	1.2242125	1.796419	0.6105509	0.6130343	0.5188916	0.9312353	1.0707971	1.5553379	1.808255	1.916253	1.3939984	1.2565922	1.5771793	1.9345006	2.0869474	1.2083480	1.504084	1.0057329	1.3481827	1.700810	1.2990609	1.0670679	1.254039	1.8736036	1.254039	1.0300444	0.9004935	0.9077401	1.8225839	1.8273389	0.4983788	0.4345669	0.6710701	1.2743878	1.2314497	0.7593165	1.254039	0.7942921	1.254039	1.7833189	1.0023959	1.612163	1.254039	0.9866675	1.692658	1.809493	1.538321	0.8573597	0.7265906	0.7662001	1.3665395	1.585387	1.584723	0.4339992	1.254039	0.5158718	1.4445343	1.854678	1.7826650	1.7999294	0.3297647	0.3816224	0.3880637	1.0999972	1.5073663	1.866985	0.8927280	1.392140	1.254039	0.7685731	1.8958782	1.9645802	0.5391436	0.4918815	0.4434524	1.1476929	1.5000563	1.3814638	1.517254	1.1853305	1.7992655	1.9187984	1.254039	0.3416666	0.4411045	1.9997166	1.8982230
IgG1_DT	2.3434473	1.3191507	1.967139	2.0217978	1.448302	2.190173	0.1001073	1.434399	0.1133312	0.1135070	0.8364959	2.1334096	2.0159014	1.9785635	1.3979721	1.434399	1.7488576	1.6337918	1.5996517	1.3674437	2.0013212	0.7906969	1.0359290	0.7867751	0.8694677	1.5003200	0.8892401	0.9939864	1.0110823	1.434399	1.7381734	1.9842561	2.1171759	2.0442573	2.5044523	2.3072540	0.7548269	0.7575287	0.7926793	1.8112890	1.8893015	0.3678318	0.4097087	0.3476251	1.3384576	1.400485	1.0342593	1.241297	0.9687141	0.9999529	1.7721643	1.5257282	0.8041054	1.434233	0.7255582	0.6824430	1.2249607	1.508740	1.6530658	0.7231660	0.7358022	1.434399	1.658798	1.434399	0.3562088	0.3470060	1.4233209	0.3244915	0.9315920	1.6593447	2.216804	1.434399	1.7280039	1.7732770	2.1332287	2.2117122	2.473230	0.8472019	1.434399	1.434399	1.434399	2.1786357	1.7390541	1.9236615	1.6315116	2.3768059	2.4142920	1.0268352	1.478088	1.414208	1.1908612	1.434399	1.434399	1.390063	1.385596	1.244955	1.4559489	1.543484	1.310828	0.8165234	0.6980982	1.434399	1.4334283	1.6656977	2.001878	1.5392001	1.5567998	1.4977481	1.5682048	1.7064838	2.3852795	2.079356	2.189821	2.1406218	1.9774095	2.1734575	2.2627896	2.3363858	1.2771531	1.680567	0.8592744	1.4955554	1.941961	1.7958479	1.1765939	1.434399	1.9039555	1.434399	0.3617325	0.2818867	0.2900679	1.2010574	1.3404667	0.5050049	0.3744705	0.7071598	1.2340919	1.0764753	1.7448933	1.434399	1.7548144	1.434399	1.9618659	1.9965382	2.212156	1.434399	1.8674345	2.208941	2.305980	2.116932	1.3572983	1.1692364	1.2480369	2.0017213	2.121985	2.187739	0.8031629	1.434399	0.8996354	2.3154687	2.664523	2.1147000	2.0957001	0.1778488	0.2135286	0.2190576	1.2919986	2.0239848	2.650579	0.4345306	1.505725	1.434399	0.3361374	2.3671705	2.5688764	0.9100772	0.8241492	0.8021163	1.1866342	1.3459605	0.8795632	1.661371	0.6756541	2.4593159	2.4864339	1.434399	0.1182026	0.1362424	2.3195800	2.3009573
IgG1_OVA	0.8001997	0.5592852	30.350333	39.2089605	19.495072	46.377276	3.0450191	4.658622	4.9479866	0.9771087	5.3863587	0.3327374	0.3227382	0.5148668	0.3227382	4.658622	0.3227382	0.3227382	0.3913817	0.3227382	0.4160786	0.9242792	1.5706654	1.1940355	0.4660830	1.3422179	14.5366144	15.7529440	16.5370760	4.658622	11.5854621	0.3678179	0.4332647	0.3939967	0.6034274	0.5118012	0.4332647	0.4463544	0.4398093	0.5510697	0.6557851	1.9516392	2.7500947	1.5982244	3.2605824	3.240948	1.5262325	3.837010	1.2317204	1.3037121	0.7709053	28.2506132	1.0550129	3.244612	0.7735901	0.6819639	0.7709053	4.046528	33.0687723	1.8076553	1.8207448	4.658622	1.053314	4.658622	0.4463544	0.4201756	20.7148390	0.3809071	0.3227382	0.7277770	15.709687	4.658622	0.4461088	0.4853773	0.4330196	0.4591985	21.319446	0.6555400	4.658622	4.658622	4.658622	0.5461221	0.4199300	0.4461088	0.3547201	0.4782052	0.4288111	9.5039973	4.729602	4.608910	14.9033880	4.658622	4.658622	1.925215	1.912126	1.737754	1.9723763	4.244504	3.037687	1.1005807	0.7995238	4.658622	0.6634331	0.7004786	5.312938	0.3227382	0.3227382	0.3227382	0.3227382	0.3227382	0.8999288	5.624591	4.116189	0.3227382	0.3227382	0.3227382	0.4097004	0.3712020	19.3637133	4.592201	12.7755289	29.4631109	10.502680	0.4738641	0.3227382	4.658622	0.7048540	4.658622	0.4866967	0.3405676	0.4161167	0.4738641	0.5123625	10.5475945	7.7818213	18.1574335	13.2232294	9.8417926	4.6666865	4.658622	4.9713545	4.658622	3.0192235	20.4924898	3.673642	4.658622	21.5870371	19.055196	23.250359	2.526758	2.7427654	1.6764278	2.0487998	2.6279557	2.346533	2.514292	5.2534542	4.658622	5.6089001	0.6443734	2.570390	6.4435258	3.8583624	2.1277878	2.7484076	3.4254472	2.7457607	1.0834026	2.707517	8.9156489	6.600698	4.658622	5.2568402	3.1438317	0.6132078	0.6381402	0.5633435	0.5508771	0.5134788	0.7732472	0.3227382	3.882854	0.3227382	0.3227382	0.3830814	4.658622	0.3956676	0.4334254	0.5341134	0.4963555
IgG2_PT	2.7249775	2.7249775	2.724978	2.7249775	2.724978	2.724978	2.7249775	7.192896	2.7249775	2.7249775	2.7249775	8.0937672	7.2524505	6.8258133	7.0442319	7.192896	2.7249775	2.7249775	2.7249775	2.7249775	2.8942785	3.4116597	3.5495343	2.8214722	4.0591779	3.7679529	2.7249775	2.7249775	2.7249775	7.192896	5.0845618	2.9957771	3.0330763	2.8465776	3.1449757	3.1449757	2.9211774	2.9211774	2.9211774	3.3687742	3.2941747	3.1449757	4.2639675	3.7417715	4.0401692	6.651150	2.7249775	7.311841	2.7249775	2.7249775	2.7249775	2.7249775	2.7249775	6.577191	2.7249775	2.7249775	2.7249775	6.682468	2.7249775	2.7249775	2.7249775	7.192896	2.995777	7.192896	2.7249775	2.7249775	2.7249775	2.7249775	2.7249775	3.2195752	2.724978	7.192896	2.7249775	2.7249775	3.1924257	9.0512962	2.724978	2.7249775	7.192896	7.192896	7.192896	2.7249775	5.1784835	4.7812719	5.0764966	8.2689505	26.0730171	2.9938202	7.859320	8.315373	2.9938202	7.192896	7.192896	33.579109	31.990261	30.616125	29.2245426	32.416996	6.683012	5.1288323	4.9798779	7.192896	4.9946389	6.3862214	5.789866	2.7249775	2.7249775	2.7249775	2.7249775	2.7249775	2.9652586	5.837338	7.973595	2.9494348	3.1024013	2.7249775	5.5498662	8.0738144	4.4791002	7.876591	5.7410746	5.0909667	4.326134	2.7249775	2.7249775	7.192896	2.7249775	7.192896	3.0259180	3.1788845	2.7249775	3.0259180	2.9494348	4.2496505	5.1292081	4.6703086	3.7907510	4.7850337	2.7249775	7.192896	2.7249775	7.192896	2.7249775	2.7249775	7.403926	7.192896	2.7249775	2.724978	2.724978	5.837338	2.7249775	2.7249775	2.7249775	2.7249775	5.706418	5.505031	82.5685291	7.192896	71.4957294	2.7249775	7.285245	112.4533176	135.0982075	8.2341824	10.2696247	7.8678031	8.3092394	9.1768465	7.498871	2.7249775	7.484692	7.192896	2.7249775	2.7249775	2.7991056	2.7249775	2.7249775	2.7249775	2.7249775	2.7249775	2.7249775	6.688796	2.7249775	2.7249775	2.7249775	7.192896	2.7249775	2.7249775	3.2653439	3.1190281

# 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, Batch corrected

2023-09-19

Load libraries

Load data and Generate Mofa object

Plot multiomic missing values

Setting data Options

Setting model Options

Setting training Options

Training Model

Impute Data

Add meta data to Mofa and Imputed objects

Mofa object

Imputed data

Results for Actual Data

Results for Imputed Data