--- a
+++ b/data-raw/hmp_T2D_raw.R
@@ -0,0 +1,565 @@
+
+library(tidyverse)
+library(timeOmics)
+library(lubridate)
+library(lmms)
+
+rm(list=ls())
+
+# RData from:
+# Sailani, M. R., Metwally, A. A., Zhou, W., Rose, S. M. S. F., Ahadi, S., Contrepois, K., ... & Snyder, M. P. (2020). 
+# Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California. Nature communications, 11(1), 1-12.
+load("/home/antoine/Documents/timeomics_analysis/HMP_seasoning/Multi_Omics_Seasonal.RData")
+
+# 0. DATA CLEANING
+
+Gut_annotation_colData <- Gut_annotation_colData %>%
+    mutate(YMD = lubridate::dmy(IRIS)) %>%
+    mutate(Date = IRIS) %>%
+    mutate(Time = yday(YMD)) %>%
+    mutate(omics = "Gut") %>% dplyr::select(-Date, -BMI, -IRIS)
+
+list_lab <- list("RNA" = RNA_annotation_colData,
+                 "Metabo" = Metabolomics_annotation_colData,
+                 #"Gut" =Gut_annotation_colData,
+                 "Clinical" = Clinical_labs_annotation_colData)
+
+list_lab_df <- imap_dfr(list_lab, ~{.x %>% 
+        mutate("omics" = .y) %>% 
+        mutate(YMD = lubridate::ymd(as.Date(Date))) %>% 
+        dplyr::select(-Date)
+})
+
+IRIS_BMI <- list_lab_df[c(1:3)] %>% unique() 
+IRIS_only <- IRIS_BMI %>% dplyr::select(-BMI) %>% unique %>% filter(!is.na(IRIS), !is.na(SubjectID)) %>%
+    unique
+IRIS_1 <- IRIS_only %>% group_by(SubjectID) %>%
+    dplyr::summarise(N = n()) %>%
+    filter(N == 1) %>% pull(SubjectID) %>% as.character()
+IRIS_only <- IRIS_only %>% filter(SubjectID %in% IRIS_1)
+
+# 1. DATA PREPARATION
+# GUT
+###########################
+GUT_sample <- Gut_annotation_colData %>% 
+    mutate(Year = ifelse(year(YMD) < 2000, year(YMD) +2000, year(YMD))) %>%
+    left_join(IRIS_only) %>%
+    mutate(SampleID = paste0(SubjectID, "_", Time, "_",  Year, "_", IRIS, "_", rownames(.)))
+
+GUT <- gut_df_Data
+rownames(GUT) <- GUT_sample$SampleID
+
+# CLINICAL
+###########################
+CLINICAL_sample <- Clinical_labs_annotation_colData %>% 
+    mutate(Year = ifelse(year(Date) < 2000, year(Date) +2000, year(Date))) %>%
+    left_join(IRIS_only) %>%
+    mutate(SampleID = paste0(SubjectID, "_", Time, "_", Year, "_", IRIS, "_", rownames(.))) 
+
+CLINICAL <- clinical_labs_Data
+rownames(CLINICAL) <- CLINICAL_sample$SampleID
+index.na <- CLINICAL %>% lapply(function(x) is.na(x) %>% sum) %>% unlist
+CLINICAL <- CLINICAL[index.na<=11] %>% na.omit
+
+# RNA
+###########################
+RNA_sample <- RNA_annotation_colData %>% 
+    mutate(Year = ifelse(year(Date) < 2000, year(Date) +2000, year(Date))) %>%
+    left_join(IRIS_only) %>%
+    mutate(SampleID = paste0(SubjectID, "_", Time, "_", Year, "_", IRIS, "_", rownames(.))) 
+
+index.NA <- (!is.na(RNA_annotation_colData$Time) & !is.na(RNA_annotation_colData$Time))
+RNA_sample <- RNA_sample[index.NA,]
+RNA <- RNA_df_Data[index.NA,]
+rownames(RNA) <- RNA_sample$SampleID
+
+# NOSE
+###########################
+Nasal_sample <- Nasal_annotation_colData %>% 
+    mutate(Year = ifelse(year(Date) < 2000, year(Date) +2000, year(Date))) %>%
+    left_join(IRIS_only) %>%
+    mutate(SampleID = paste0(SubjectID, "_", Time, "_",  Year, "_", IRIS, "_", rownames(.)))
+
+NASAL <- Nasal_df_Data
+rownames(NASAL) <- Nasal_sample$SampleID
+
+# PROTEIN
+###########################
+PROT_sample <- Proteomics_annotation_colData %>% 
+    mutate(Year = ifelse(year(Date) < 2000, year(Date) +2000, year(Date))) %>%
+    left_join(IRIS_only) %>%
+    mutate(SampleID = paste0(SubjectID, "_", Time, "_",  Year, "_", IRIS, "_", rownames(.)))
+
+PROT <- Proteomics_df_Data
+rownames(PROT) <- PROT_sample$SampleID
+
+# METABOLITE
+###########################
+METAB_sample <- Metabolomics_annotation_colData %>% 
+    mutate(Year = ifelse(year(Date) < 2000, year(Date) +2000, year(Date))) %>%
+    left_join(IRIS_only) %>%
+    mutate(SampleID = paste0(SubjectID, "_", Time, "_",  Year, "_", IRIS, "_", rownames(.)))
+
+METAB <- Metabolomics_df_Data
+rownames(METAB) <- METAB_sample$SampleID
+
+# CYTOKINE
+###########################
+CYTO_sample <- Cytokines_annotation_colData %>% 
+    mutate(Year = ifelse(year(Date) < 2000, year(Date) +2000, year(Date))) %>%
+    left_join(IRIS_only) %>%
+    mutate(SampleID = paste0(SubjectID, "_", Time, "_",  Year, "_", IRIS, "_", rownames(.)))
+
+CYTO <- Cytokines_df_Data
+rownames(CYTO) <- CYTO_sample$SampleID
+
+############################
+
+# DATA: only RNA/CLINICAL/GUT/METAB
+# split by IR/IS
+DATA <- list("RNA.IR" = RNA[str_split(rownames(RNA),"_") %>% map_chr(~.x[[4]]) == "IR",],
+             "GUT.IR" = GUT[str_split(rownames(GUT),"_") %>% map_chr(~.x[[4]]) == "IR",],
+             
+             "CLINICAL.IR" = CLINICAL[str_split(rownames(CLINICAL),"_") %>% map_chr(~.x[[4]]) == "IR",],
+             "RNA.IS" = RNA[str_split(rownames(RNA),"_") %>% map_chr(~.x[[4]]) == "IS",],
+             
+             "GUT.IS" = GUT[str_split(rownames(GUT),"_") %>% map_chr(~.x[[4]]) == "IS",],
+             "CLINICAL.IS" = CLINICAL[str_split(rownames(CLINICAL),"_") %>% map_chr(~.x[[4]]) == "IS",],
+             
+             "METAB.IR" = METAB[str_split(rownames(METAB),"_") %>% map_chr(~.x[[4]]) == "IR",],
+             "METAB.IS" = METAB[str_split(rownames(METAB),"_") %>% map_chr(~.x[[4]]) == "IS",],
+             
+             "PROT.IS" = PROT[str_split(rownames(PROT),"_") %>% map_chr(~.x[[4]]) == "IS",],
+             "PROT.IR" = PROT[str_split(rownames(PROT),"_") %>% map_chr(~.x[[4]]) == "IR",],
+             
+             "CYTO.IS" = CYTO[str_split(rownames(CYTO),"_") %>% map_chr(~.x[[4]]) == "IS",],
+             "CYTO.IR" = CYTO[str_split(rownames(CYTO),"_") %>% map_chr(~.x[[4]]) == "IR",]
+) 
+
+COMBINED <- list("RNA" = RNA, CLINICAL = CLINICAL, GUT = GUT, METAB = METAB, PROT = PROT, CYTO = CYTO)
+save(DATA, COMBINED, file = "/home/antoine/Documents/timeomics_analysis/HMP_seasoning/netomics/RAW_DATA.RDA")
+############################################################
+
+stat_raw_data <- lapply(list(RNA=RNA, GUT=GUT, METAB=METAB, CLINICAL=CLINICAL, PROT = PROT, CYTO = CYTO), dim) %>%
+    as.data.frame() %>% t %>% as.data.frame() %>%
+    setNames(c("sample", "feature"))
+lapply(list(RNA=RNA, GUT=GUT, METAB=METAB, CLINICAL=CLINICAL, PROT = PROT, CYTO = CYTO), function(x){
+    rownames(x) %>% str_remove("_.*") %>% unique %>% length()}) %>% 
+    as.data.frame() %>%  t %>% as.data.frame() %>% setNames("uniqueID") %>%
+    rownames_to_column("omic") %>%
+    left_join(stat_raw_data %>% rownames_to_column("omic")) %>% column_to_rownames("omic") %>% t %>%
+    as.data.frame() %>% knitr::kable()
+
+# 2. DATA FILTERING
+
+# 1. coef. of var
+cv.data <- lapply(DATA, function(X){
+    unlist(lapply(as.data.frame(X), 
+                  function(x) abs(sd(x, na.rm = TRUE)/mean(x, na.rm= TRUE))))
+})
+
+fc.data <- list("RNA.IR"= 1.5, "RNA.IS"=1.5,
+                "CLINICAL.IR"=0, "CLINICAL.IS"=0,
+                "GUT.IR"=1.5, "GUT.IS"=1.5,
+                "METAB.IR"=1.5 , "METAB.IS"=1.5, 
+                "PROT.IR"=1.5 , "PROT.IS"=1.5, 
+                "CYTO.IR" = 1.5, "CYTO.IS"=1.5)
+
+par(mfrow = c(6,2))
+#for(i in c("RNA.IR","CLINICAL.IR", "GUT.IR", "METAB.IR", "RNA.IS","CLINICAL.IS", "GUT.IS", "METAB.IS", "PROT.IR", "PROT.IS", "CYTO.IR", "CYTO.IS")){
+for(i in c("RNA.IR","RNA.IS", "CLINICAL.IR", "CLINICAL.IS", "GUT.IR","GUT.IS", "METAB.IS", "METAB.IR", "PROT.IR", "PROT.IS", "CYTO.IR", "CYTO.IS")){
+    
+    hist(cv.data[[i]], breaks = 20, main =i)
+    abline(v = fc.data[[i]], col = "red")
+    legend("topright", legend = paste0("FC = ",fc.data[[i]]), col = "red", lty = 1) 
+}
+par(mfrow = c(1,1))
+
+
+# 2. Remove low cv features
+remove.low.cv <- function(X, cutoff = 0.5){
+    # var.coef
+    cv <- unlist(lapply(as.data.frame(X), 
+                        function(x) abs(sd(x, na.rm = TRUE)/mean(x, na.rm= TRUE))))
+    return(X[,cv > cutoff])
+}
+
+DATA.filtered <- list("RNA.IR" = remove.low.cv(DATA$RNA.IR, 1.5),
+                      "RNA.IS" = remove.low.cv(DATA$RNA.IS, 1.5),
+                      "GUT.IR" = remove.low.cv(DATA$GUT.IR, 1.5),
+                      "GUT.IS" = remove.low.cv(DATA$GUT.IS, 1.5),
+                      # "CLINICAL.IR" = remove.low.cv(DATA$CLINICAL.IR, 0),
+                      # "CLINICAL.IS" = remove.low.cv(DATA$CLINICAL.IS, 0),
+                      "CLINICAL.IR" = DATA$CLINICAL.IR,
+                      "CLINICAL.IS" = DATA$CLINICAL.IS,
+                      
+                      "METAB.IR" = remove.low.cv(DATA$METAB.IR, 1.5),
+                      "METAB.IS" = remove.low.cv(DATA$METAB.IS, 1.5), 
+                      "PROT.IS" = remove.low.cv(DATA$PROT.IS, 1.5),
+                      "PROT.IR" = remove.low.cv(DATA$PROT.IR, 1.5),
+                      "CYTO.IS" = remove.low.cv(DATA$CYTO.IS, 1),
+                      "CYTO.IR" = remove.low.cv(DATA$CYTO.IR, 1))
+lapply(DATA.filtered, dim)
+
+# 3. scale filtered value (log, scale, CLR)
+
+# scale for OTU
+norm_OTU <- function(DF, AR = F){
+    DF <- DF + 0.0001
+    
+    data.TSS.clr = mixOmics::logratio.transfo(DF, logratio = 'CLR')
+    
+    # reconstrcuct dataframe
+    data.good <- as.data.frame(matrix(ncol = ncol(data.TSS.clr), 
+                                      nrow = nrow( data.TSS.clr)))
+    rownames(data.good) <- rownames(data.TSS.clr)
+    colnames(data.good) <- colnames(data.TSS.clr)
+    for( i in c(1:nrow(data.TSS.clr))){
+        for( j in c(1:ncol(data.TSS.clr))){
+            data.good[i,j] <- data.TSS.clr[i,j]
+        }
+    }
+    return(data.good)
+}
+
+
+DATA.filtered.scale <- list(
+    "RNA.IR" = log(DATA.filtered$RNA.IR + 1) %>% scale,
+    "RNA.IS" = log(DATA.filtered$RNA.IS + 1) %>% scale,
+    
+    "CLINICAL.IR" = log(DATA.filtered$CLINICAL.IR +1)%>% scale,
+    "CLINICAL.IS" = log(DATA.filtered$CLINICAL.IS +1)%>% scale,
+    
+    "GUT.IR" = norm_OTU(DATA.filtered$GUT.IR),
+    "GUT.IS" = norm_OTU(DATA.filtered$GUT.IS),
+    
+    "METAB.IR" = log(DATA.filtered$METAB.IR +1)%>% scale,
+    "METAB.IS" = log(DATA.filtered$METAB.IS +1)%>% scale,
+    
+    # "PROT.IR" = log(DATA.filtered$PROT.IR +1)%>% scale,
+    # "PROT.IS" = log(DATA.filtered$PROT.IS +1)%>% scale
+    
+    "PROT.IR" = DATA.filtered$PROT.IR,  # already scale
+    "PROT.IS" = DATA.filtered$PROT.IS,
+    
+    "CYTO.IR" = log(DATA.filtered$CYTO.IR +1),  
+    "CYTO.IS" = log(DATA.filtered$CYTO.IS +1)
+    
+)
+
+lapply(DATA.filtered, dim) %>%
+    as.data.frame() %>% t %>% as.data.frame() %>%
+    setNames(c("sample", "feature")) %>%
+    rownames_to_column("OMIC") %>%
+    mutate(IRIS = str_extract(OMIC,"..$"), OMIC = str_remove(OMIC, "...$"))  %>%
+    gather(meta, value, -c(OMIC, IRIS)) %>%
+    spread(OMIC, value) %>% arrange(IRIS) %>%
+    dplyr::select(IRIS, meta, RNA, GUT, METAB, CLINICAL, PROT, CYTO)
+
+save(DATA.filtered.scale, DATA.filtered, file = "/home/antoine/Documents/timeomics_analysis/HMP_seasoning/netomics/DATA_FILTERED.RDA")
+############################################################
+
+fc.data.combined <- list("RNA"= 1.5, 
+                         "CLINICAL"=0.2,
+                         "GUT"=1.5,
+                         "METAB"=1.5,
+                         "PROT" = 1.5,
+                         "CYTO" = 1)
+cv.data.combined <- lapply(COMBINED, function(X){
+    unlist(lapply(as.data.frame(X), 
+                  function(x) abs(sd(x, na.rm = TRUE)/mean(x, na.rm= TRUE))))
+})
+fc.color <- list("RNA"= color.mixo(4), 
+                 "CLINICAL"=color.mixo(1),
+                 "GUT"=color.mixo(2),
+                 "METAB"=color.mixo(3),
+                 "PROT"=color.mixo(5),
+                 "CYTO" = color.mixo(6))
+
+par(mfrow = c(3,2))
+for(i in c("RNA","CLINICAL", "GUT", "METAB", "PROT", "CYTO")){
+    hist(cv.data.combined[[i]], breaks = 20, main =i, xlab = paste0("Var. Coef. (", i, ")"), 
+         col = fc.color[[i]])
+    abline(v = fc.data.combined[[i]], col = "red")
+    legend("topright", legend = paste0("CV = ",fc.data.combined[[i]]), col = "red", lty = 1) 
+}
+par(mfrow = c(1,1))
+
+# 3. MODELLING
+
+lmms.func <- function(X, mode = "p-spline"){
+    time <- rownames(X) %>% str_split("_") %>% map_chr(~.x[[2]]) %>% as.numeric()
+    lmms.output <- lmms::lmmSpline(data = X, time = time,
+                                   sampleID = rownames(X), deri = FALSE,
+                                   basis = mode, numCores = 4, 
+                                   keepModels = TRUE)
+    return(lmms.output)
+}
+
+# only one ID/Year
+ID_u <- "ZLZNCLZ"
+Year_u <- 2015
+
+# ID_u <- "ZOZOW1T"
+# Year_u <- 2015
+
+
+tmp <-   imap_dfr(DATA.filtered.scale, ~{
+    .x %>% as.data.frame() %>% rownames_to_column("sample") %>%
+        gather(feature, value, -sample) %>%
+        mutate(ID = str_split(sample, "_") %>% map_chr(~.x[[1]])) %>%
+        mutate(year = str_split(sample, "_") %>% map_chr(~.x[[3]])) %>%
+        mutate(OMIC = str_remove(.y, "...$")) %>% 
+        mutate(IRIS = str_extract(.y, "..$"))
+}) 
+tmp %>%  dplyr::select(-feature, -value) %>% unique() %>% na.omit %>% 
+    group_by(ID, year, OMIC, IRIS) %>% summarise(N = n()) %>% spread(OMIC, N) %>% 
+    na.omit() %>% split(.$year)
+tmp %>%  dplyr::select(-feature, -value) %>% unique() %>% na.omit %>% 
+    group_by(ID, year, OMIC, IRIS) %>% summarise(N = n()) %>% spread(OMIC, N) %>% 
+    filter(ID == ID_u) %>% dplyr::select(-IRIS) %>% na.omit
+
+# just a filter to get only the selected ID/Year
+DATA.GOOD <- imap_dfr(DATA.filtered.scale, ~{
+    .x %>% as.data.frame() %>% rownames_to_column("sample") %>%
+        gather(feature, value, -sample) %>%
+        mutate(ID = str_split(sample, "_") %>% map_chr(~.x[[1]])) %>%
+        mutate(year = str_split(sample, "_") %>% map_chr(~.x[[3]])) %>%
+        mutate(OMIC = str_remove(.y, "...$")) %>%
+        dplyr::filter(ID == ID_u) %>% 
+        dplyr::filter(year == Year_u)
+}) %>% split(.$OMIC) %>%
+    purrr::map(~{
+        .x %>% 
+            dplyr::select(sample, feature, value) %>%
+            spread(feature, value) %>% 
+            column_to_rownames("sample")
+    })
+
+
+# only 1 year
+MODELLED <- lapply(DATA.GOOD, function(x) lmms.func(x))
+
+MODELLED %>% lapply(function(x)x@predSpline %>% dim) %>%
+    as.data.frame() %>% t %>% as.data.frame() %>%
+    setNames(c("sample", "feature")) %>% t %>%as.data.frame() %>%
+    dplyr::select(RNA, GUT, METAB, CLINICAL, PROT)
+
+MODELLED %>% imap_dfr(~.x@modelsUsed %>% table %>% as.data.frame  %>%
+                          column_to_rownames(".") %>% t %>% as.data.frame %>% 
+                          mutate(omic = .y)) %>% remove_rownames() %>%  
+    column_to_rownames('omic') %>% t %>%
+    as.data.frame() %>% dplyr::select(RNA, GUT, METAB, CLINICAL, PROT)
+
+# 4. STRAIGHT LINE FILTERING
+
+filterlmms.func <- function(modelled.data, lmms.output){
+    time = modelled.data %>% rownames() %>% str_split("_") %>% map_chr(~.x[[2]]) %>% as.numeric()
+    #time = rownames(modelled.data) %>% as.numeric()
+    filter.res <- lmms.filter.lines(data = modelled.data,
+                                    lmms.obj = lmms.output, time = time,
+                                    homoskedasticity.cutoff=0.05)$filtered
+}
+
+FILTER <- lapply(names(DATA.GOOD), function(x) filterlmms.func(modelled.data = DATA.GOOD[[x]], lmms.output = MODELLED[[x]]))
+names(FILTER) <- names(MODELLED)
+
+FILTER %>% lapply(dim) %>%
+    as.data.frame() %>% t %>% as.data.frame() %>%
+    setNames(c("sample", "feature")) %>%
+    t %>%as.data.frame() %>%
+    dplyr::select(RNA, GUT, METAB, CLINICAL)
+
+FINAL.FILTER <- FILTER[c("CLINICAL", "GUT", "METAB", "RNA", "PROT")]
+rownames(FINAL.FILTER[["GUT"]]) <- rownames(FINAL.FILTER[["RNA"]]) # change 86 par 85
+
+DATA.LMMS <- lapply(MODELLED, function(x)x@predSpline %>% t %>% as.data.frame)
+rownames(DATA.LMMS[["GUT"]]) <- rownames(DATA.LMMS[["RNA"]]) # change 86 par 85
+
+save(FINAL.FILTER, MODELLED, DATA.GOOD, DATA.LMMS, file = "/home/antoine/Documents/timeomics_analysis/HMP_seasoning/netomics/LMMS.RDA")
+
+lapply(DATA.LMMS, dim)
+# 5. MULTI-OMICS CLUSTERING
+
+block.res <- block.pls(DATA.LMMS, indY = 1, ncomp = 5)
+getNcomp.res <- getNcomp(block.res, X = DATA.LMMS, indY = 1)
+
+# block.res <- block.pls(FINAL.FILTER, indY = 1, ncomp = 3)
+# getNcomp.res <- getNcomp(block.res, X = FINAL.FILTER, indY = 1)
+
+plot(getNcomp.res)
+
+# ncomp = 2
+block.res <- block.pls(DATA.LMMS, indY = 1, ncomp = 1, scale =FALSE) 
+
+# block.res <- block.pls(FINAL.FILTER, indY = 1, ncomp = 1, scale =FALSE) 
+
+plotLong(object = block.res, title = "Block-PLS Clusters, scale = TRUE", legend = TRUE)
+
+getCluster(block.res) %>% group_by(block, cluster) %>% summarise(N = n()) %>%
+    spread(block, N) %>%
+    dplyr::select(cluster, RNA, GUT, METAB, CLINICAL)
+
+save(block.res, file = "/home/antoine/Documents/timeomics_analysis/HMP_seasoning/netomics/timeomics_res_block.rda")
+
+
+# elagage
+test.list.keepX <- list(
+    "CLINICAL" = seq(2,8,by=1),
+    "GUT" = seq(2,10,by=1),
+    "METAB" = seq(2,9,by=1),
+    "RNA" = seq(10,50,by=2)
+)
+
+tune.block.res <- tuneCluster.block.spls(X= FINAL.FILTER, indY = 1,
+                                         test.list.keepX=test.list.keepX, 
+                                         scale=FALSE, 
+                                         mode = "canonical", ncomp = 1)
+tune.block.res$choice.keepX 
+final.block <- block.spls(FINAL.FILTER, indY = 1, ncomp = 1, scale =FALSE, 
+                          keepX = tune.block.res$choice.keepX) 
+plotLong(final.block, legend = TRUE)
+
+getCluster(final.block) %>% group_by(block, cluster) %>% summarise(N = n()) %>%
+    spread(block, N) %>%
+    dplyr::select(cluster, RNA, GUT, METAB, CLINICAL)
+
+library("openxlsx")
+cluster_comp <- getCluster(final.block) %>% dplyr::select(molecule, block, cluster, comp, contribution) %>% 
+    mutate(cluster = ifelse(cluster == -1, "Cluster 1", "Cluster 2")) %>%
+    split(.$cluster)
+write.xlsx(cluster_comp, file = "cluster_composition.xlsx")
+
+
+# final data for netOmics package // shrink
+#   DATA.GOOD -> ! individual, 7 timepoints but no modelisation
+#   DATA RAW -> filter based on DATA.GOOD molecules (for OTU -> sparcc needs RAW)
+# DATA$RNA.IS %>% rownames() %>% str_remove("_.*") %>% str_detect("ZLZNCLZ") %>% any()
+# [1] TRUE
+
+hmp_T2D <- list()
+hmp_T2D$raw <- list() 
+hmp_T2D$data <- list()
+for(i in names(DATA.GOOD)){
+    # hmp_diabetes$raw[[i]] <- DATA[[paste0(i, ".IS")]][str_detect(rownames(DATA[[paste0(i, ".IS")]]), "ZLZNCLZ"), colnames(DATA.GOOD[[i]])]
+    hmp_T2D$raw[[i]] <- DATA[[paste0(i, ".IS")]][rownames(DATA.GOOD[[i]]), colnames(DATA.GOOD[[i]])]
+    rownames(hmp_T2D$raw[[i]]) <- rownames(DATA.GOOD$RNA)
+    hmp_T2D$raw[[i]] <- hmp_T2D$raw[[i]] %>% rownames_to_column("Rownames") %>% 
+        mutate(Rownames = Rownames %>% str_split("_") %>% map_chr(~.x[[2]]) %>% as.numeric()) %>% 
+        arrange(Rownames) %>% column_to_rownames(var = "Rownames")
+    
+    #data
+    hmp_T2D$data[[i]] <- DATA.GOOD[[i]]
+    rownames(hmp_T2D$data[[i]]) <- rownames(hmp_T2D$raw[[i]])
+}
+
+hmp_T2D$data <- DATA.GOOD
+lmms.func <- function(X){
+    # time <- rownames(X) %>% str_split("_") %>% map_chr(~.x[[2]]) %>% as.numeric()
+    time <- rownames(X) %>% as.numeric()
+    lmms.output <- lmms::lmmSpline(data = X, time = time,
+                                   sampleID = rownames(X), deri = FALSE,
+                                   basis = "p-spline", numCores = 4, 
+                                   keepModels = TRUE)
+    return(lmms.output)
+}
+
+MODELLED <- lapply(hmp_T2D$data, function(x) lmms.func(x))
+
+MODELLED %>% imap_dfr(~.x@modelsUsed %>% table %>% as.data.frame  %>%
+                          column_to_rownames(".") %>% t %>% as.data.frame %>% 
+                          mutate(omic = .y)) %>% remove_rownames() %>%  
+    column_to_rownames('omic') %>% t %>%
+    as.data.frame()
+
+data.MODELLED <-  lapply(MODELLED, function(x) x@predSpline %>% t)
+rownames(data.MODELLED$GUT) <- rownames(data.MODELLED$RNA)
+
+
+# .$timeOmics
+# block.res.no.model  <- block.pls(hmp_T2D$data, indY = 1, ncomp = 5, scale =FALSE) 
+# getNcomp.res <- getNcomp(block.res.no.model, X = hmp_T2D$data, indY = 1)
+# plot(getNcomp.res)
+block.res.no.model  <- block.pls(hmp_T2D$data, indY = 1, ncomp = 1, scale =TRUE) 
+hmp_T2D$getCluster.res <- getCluster(block.res.no.model)
+
+block.res.w.model  <- block.pls(data.MODELLED, indY = 1, ncomp = 5, scale =TRUE) 
+getNcomp.res <- getNcomp(block.res.w.model, X = data.MODELLED, indY = 1)
+block.res.w.model  <- block.pls(data.MODELLED, indY = 1, ncomp = 1, scale =TRUE) 
+
+hmp_T2D$getCluster.res <- getCluster(block.res.w.model)
+
+# .$sparse
+test.list.keepX <- list(
+    "CLINICAL" = seq(2,39,by=2),
+    "CYTO" = seq(2,10,b=2),
+    "GUT" = seq(2,50,by=2),
+    "METAB" = seq(2,50,by=2),
+    "PROT" = seq(2,30,b=2),
+    "RNA" = seq(10,100,by=3))
+
+# tune.block.res <- tuneCluster.block.spls(X= hmp_T2D$data, indY = 1,
+#                                          test.list.keepX=test.list.keepX, 
+#                                          scale=FALSE, 
+#                                          mode = "canonical", ncomp = 1)
+# too long
+
+list.keepX <- list(
+    "CLINICAL" = 4,
+    "CYTO" = 3,
+    "GUT" = 10,
+    "METAB" = 3,
+    "PROT" = 2,
+    "RNA" = 34)
+
+sparse.block.res.w.model  <- block.spls(data.MODELLED, indY = 1, ncomp = 1, scale =TRUE, keepX = list.keepX) 
+plotLong(sparse.block.res.w.model, scale = TRUE, legend = TRUE)
+
+
+hmp_T2D$getCluster.sparse.res <-  getCluster(sparse.block.res.w.model)
+timeOmics::getSilhouette(sparse.block.res.w.model)
+timeOmics::getSilhouette(block.res.w.model)
+
+## Biogrid ## DATABASES
+biogrid <- read_tsv("/home/antoine/Documents/TO2/netOmics-case-studies/HeLa_Cell_Cycling/data/BIOGRID-ALL-3.5.187.tab3.txt")
+biogrid.filtered <- biogrid %>% dplyr::select("Official Symbol Interactor A", "Official Symbol Interactor B") %>% unique %>%
+    set_names(c("from", "to"))
+
+biogrid.filtered.tmp <- biogrid.filtered %>% filter(from %in% cluster.info$molecule | to %in% cluster.info$molecule)
+
+hmp_T2D$interaction.biogrid <- biogrid.filtered.tmp
+
+TFome <- readRDS( "~/Documents/TO2/TFome.Rds")
+tf.1 <- TFome %>% filter(TF %in% hmp_T2D$getCluster.res$molecule | Target %in% hmp_T2D$getCluster.res$molecule) %>% 
+    unlist() %>% unique
+hmp_T2D$interaction.TF <- TFome %>% filter(TF %in% tf.1 | Target %in% tf.1)
+
+hmp_T2D$interaction.TF <- TFome.igraph
+
+hmp_T2D$interaction.TF <- TF.interact
+usethis::use_data(hmp_T2D, overwrite = TRUE)
+
+
+medlineranker.res <- read_tsv("/home/antoine/Documents/timeomics_analysis/HMP_seasoning/netomics/res_medlineranker_all.csv")
+tmp <- dplyr::select(medlineranker.res, c("Disease", 'Gene symbols'))  %>% set_names(c("Disease", "symbol")) 
+# separate_rows(tmp, Disease, symbol, sep = "|", convert = TRUE)
+
+library(splitstackshape)
+tmp[c(1,2),] %>% separate_rows(Disease, symbol)
+tmp[c(2,3),] %>% 
+    rownames_to_column("id") %>% 
+    cSplit(., sep = "|", splitCols = 2:ncol(.), direction = "long", makeEqual = T) %>% 
+    as_tibble() %>% 
+    group_by(id) %>% 
+    fill(2:ncol(.)) %>% 
+    unique() %>% 
+    ungroup(id) %>% 
+    select(-id)
+
+medlineranker.res.df <- tmp %>% rownames_to_column("id") %>% 
+    cSplit(., sep = "|", splitCols = 3:ncol(.), direction = "long", makeEqual = T) %>% 
+    as_tibble() %>% group_by(id) %>% fill(2:ncol(.)) %>% unique() %>% ungroup(id) %>% select(-id)
+
+medlineranker.res.df <- left_join(medlineranker.res.df, medlineranker.res) %>% mutate(symbol = as.character(symbol))
+hmp_T2D$medlineranker.res.df <- medlineranker.res.df 
+usethis::use_data(hmp_T2D, overwrite = TRUE)
\ No newline at end of file