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Copy pathIntegration_block_splsda_fonc.R
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Integration_block_splsda_fonc.R
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# La fonction meanSpotRepl remplace les valeurs des spots répliqués par la
# moyenne de leurs intensités.
meanSpotRepl <-function(mat)
{
ProbeName = colnames(mat)
isDup = duplicated(ProbeName)
dupNames = ProbeName[isDup]
for (dups in unique(dupNames))
{
mat[dups,] = apply(mat[which(colnames(mat) == dups), ], 2, mean)
}
res = mat[, -which(isDup)] # On retire de la matrice mat toutes les spots qui sont répliqués.
return(res)
}
# La fonction supprimerVaConst supprime de la matrice mat les variables constantes.
supprimerVaConst <-function(mat)
{
name_mat = deparse(substitute(mat))
cat(paste0("Pour ", name_mat, ", avant suppression des variables constantes, il y a ", dim(mat)[2], " variables."), "\n")
indiceVaConst = sapply(1:dim(mat)[2], FUN = function(i){
col_mat_i = mat[, i]
res = all(col_mat_i == col_mat_i[1])
return(res)
})
if(length(which(indiceVaConst == FALSE)) != 0)
{
res = mat[, which(indiceVaConst == FALSE)]
}else{
res = mat
}
cat(paste0("Pour ", name_mat, ", après suppression des variables constantes, il reste ", dim(res)[2], " variables."), "\n")
return(res)
}
# La fonction supprimerVaNaInd supprime les variables contenant des NA
# (si on garde les NA dans ces variables, les composantes du bloc ne
# sont plus orthogonales).
supprimerVaNaInd <-function(mat)
{
indiceVaNaInd = sapply(1:dim(mat)[2], FUN = function(i){
col_mat_i = mat[, i]
indNA = length(which(is.na(col_mat_i) == TRUE))
if(indNA >= 1)
{
res = FALSE
}else{
res = TRUE
}
return(res)
})
mat2 = mat[, which(indiceVaNaInd == TRUE)]
name_mat = deparse(substitute(mat))
cat(paste0("Pour ", name_mat, ", après suppression des variables contenant des NA, il reste ", dim(mat2)[2], " variables."), "\n")
return(mat2)
}
# La fonction varAnnotation permet de fournir des informations sur les variables
# sélectionnées pour un design.
varAnnotation <-function(variablesSelect,
data_transcripto_col,
annot_metageno_caecum)
{
res_variablesSelect = variablesSelect
noms_blocks = sapply(1:length(variablesSelect), FUN = function(i){
ch = strsplit(names(variablesSelect)[i], split = "_")[[1]]
if(ch[1] == "resBio")
{
res = "resBio"
}else{
res = paste(ch[1:2], collapse = "_")
}
return(res)
})
ind_transcripto_col = which(noms_blocks == "transcripto_col")
if(length(variablesSelect[[ind_transcripto_col]]) != 0)
{
varSelect_transcripto_colTemp = variablesSelect[[ind_transcripto_col]]
varSelect_transcripto_col = sapply(1:length(varSelect_transcripto_colTemp), FUN = function(i){
ch = strsplit(varSelect_transcripto_colTemp[i], split = "_")[[1]]
res = paste(ch[2:length(ch)], collapse = "_")
return(res)
})
dataframe_varSelect_transcripto_col = data.frame(ProbeName = varSelect_transcripto_col)
dataframe_annot_transcripto_col = data.frame(ProbeName = data_transcripto_col$genes$ProbeName,
GeneName = data_transcripto_col$genes$GeneName,
Description = data_transcripto_col$genes$Description,
SystematicName = data_transcripto_col$genes$SystematicName)
tab_transcripto_col = join(x = dataframe_varSelect_transcripto_col, y = dataframe_annot_transcripto_col,
type = "inner",
by = "ProbeName")
res_variablesSelect[[ind_transcripto_col]] = tab_transcripto_col
}
ind_metageno_caecum = which(noms_blocks == "metageno_caecum")
if(length(variablesSelect[[ind_metageno_caecum]]) != 0)
{
varSelect_metageno_caecum_Temp1 = variablesSelect[[ind_metageno_caecum]]
varSelect_metageno_caecum_Temp2 = sapply(1:length(varSelect_metageno_caecum_Temp1), FUN = function(i){
ch = strsplit(varSelect_metageno_caecum_Temp1[i], split = "")[[1]]
if(ch[1] == "X")
{
res = paste(ch[2:length(ch)], collapse = "")
}else{
res = varSelect_metageno_caecum_Temp1[i]
}
return(res)
})
dataframe_varSelect_metageno_caecum = data.frame(taxon = varSelect_metageno_caecum_Temp2)
dataframe_annot_metageno_caecum = data.frame(annot_metageno_caecum)
colnames(dataframe_annot_metageno_caecum)[1] = "taxon"
tab_metageno_caecum = join(x = dataframe_varSelect_metageno_caecum, y = dataframe_annot_metageno_caecum,
type = "inner",
by = "taxon")
res_variablesSelect[[ind_metageno_caecum]] = tab_metageno_caecum
}
ind_metabo_S1 = which(noms_blocks == "metabo_S1")
if(length(variablesSelect[[ind_metabo_S1]]) != 0)
{
res_variablesSelect[[ind_metabo_S1]] = data.frame(variable = variablesSelect[[ind_metabo_S1]])
}
ind_resBio = which(noms_blocks == "resBio")
if(length(variablesSelect[[ind_resBio]]) != 0)
{
res_variablesSelect[[ind_resBio]] = data.frame(variable = variablesSelect[[ind_resBio]])
}
return(res_variablesSelect)
}
# La fonction varAnnotation_gene_6blocks permet de fournir des informations sur les variables
# sélectionnées pour un design.
varAnnotation_gene_6blocks <-function(variablesSelect,
data_transcripto_col,
data_transcripto_tae,
annot_metageno_caecum,
metavar_metaboLC_S1,
metavar_resBio,
metavar_cyto)
{
res_variablesSelect = variablesSelect
noms_blocks = names(variablesSelect)
ind_transcripto_col = which(noms_blocks == "transcripto_col")
if(length(ind_transcripto_col) != 0)
{
varSelect_transcripto_colTemp = variablesSelect[[ind_transcripto_col]]
if(length(varSelect_transcripto_colTemp) != 0)
{
varSelect_transcripto_col = sapply(1:length(varSelect_transcripto_colTemp), FUN = function(i){
variable_i = varSelect_transcripto_colTemp[i]
res = gsub("Colon_", "", variable_i, fixed = TRUE)
return(res)
})
dataframe_varSelect_transcripto_col = data.frame(GeneName = varSelect_transcripto_col)
dataframe_annot_transcripto_col = data.frame(ProbeName = data_transcripto_col$genes$ProbeName,
GeneName = data_transcripto_col$genes$GeneName,
Description = data_transcripto_col$genes$Description,
SystematicName = data_transcripto_col$genes$SystematicName)
tab_transcripto_col = join(x = dataframe_varSelect_transcripto_col, y = dataframe_annot_transcripto_col,
type = "inner",
by = "GeneName")
res_variablesSelect[[ind_transcripto_col]] = tab_transcripto_col
}else{
res_variablesSelect[[ind_transcripto_col]] = ""
}
}
ind_transcripto_tae = which(noms_blocks == "transcripto_tae")
if(length(ind_transcripto_tae) != 0)
{
varSelect_transcripto_taeTemp = variablesSelect[[ind_transcripto_tae]]
if(length(varSelect_transcripto_taeTemp) != 0)
{
varSelect_transcripto_tae = sapply(1:length(varSelect_transcripto_taeTemp), FUN = function(i){
variable_i = varSelect_transcripto_taeTemp[i]
res = gsub("TAE_", "", variable_i, fixed = TRUE)
return(res)
})
dataframe_varSelect_transcripto_tae = data.frame(GeneName = varSelect_transcripto_tae)
dataframe_annot_transcripto_tae = data.frame(ProbeName = data_transcripto_tae$genes$ProbeName,
GeneName = data_transcripto_tae$genes$GeneName,
Description = data_transcripto_tae$genes$Description,
SystematicName = data_transcripto_tae$genes$SystematicName)
tab_transcripto_tae = join(x = dataframe_varSelect_transcripto_tae, y = dataframe_annot_transcripto_tae,
type = "inner",
by = "GeneName")
res_variablesSelect[[ind_transcripto_tae]] = tab_transcripto_tae
}else{
res_variablesSelect[[ind_transcripto_tae]] = ""
}
}
ind_metageno_caecum = which(noms_blocks == "metageno_caecum")
if(length(ind_metageno_caecum) != 0)
{
varSelect_metageno_caecum_Temp1 = variablesSelect[[ind_metageno_caecum]]
if(length(varSelect_metageno_caecum_Temp1) != 0)
{
varSelect_metageno_caecum_Temp2 = sapply(1:length(varSelect_metageno_caecum_Temp1), FUN = function(i){
ch = strsplit(varSelect_metageno_caecum_Temp1[i], split = "")[[1]]
if(ch[1] == "X")
{
res = paste(ch[2:length(ch)], collapse = "")
}else{
res = varSelect_metageno_caecum_Temp1[i]
}
return(res)
})
dataframe_varSelect_metageno_caecum = data.frame(taxon = varSelect_metageno_caecum_Temp2)
dataframe_annot_metageno_caecum = data.frame(annot_metageno_caecum)
colnames(dataframe_annot_metageno_caecum)[1] = "taxon"
tab_metageno_caecum = join(x = dataframe_varSelect_metageno_caecum, y = dataframe_annot_metageno_caecum,
type = "inner",
by = "taxon")
res_variablesSelect[[ind_metageno_caecum]] = tab_metageno_caecum
}else{
res_variablesSelect[[ind_metageno_caecum]] = ""
}
}
ind_metaboLC_S1 = which(noms_blocks == "metaboLC_S1")
if(length(ind_metaboLC_S1) != 0)
{
if(length(variablesSelect[[ind_metaboLC_S1]]) != 0)
{
res_variablesSelect[[ind_metaboLC_S1]] = data.frame(variable = variablesSelect[[ind_metaboLC_S1]])
}else{
res_variablesSelect[[ind_metaboLC_S1]] = ""
}
}
ind_resBio = which(noms_blocks == "resBio")
if(length(ind_resBio) != 0)
{
if(length(variablesSelect[[ind_resBio]]) != 0)
{
res_variablesSelect[[ind_resBio]] = data.frame(variable = variablesSelect[[ind_resBio]])
}else{
res_variablesSelect[[ind_resBio]] = ""
}
}
ind_cyto = which(noms_blocks == "cyto")
if(length(ind_cyto) != 0)
{
if(length(variablesSelect[[ind_cyto]]) != 0)
{
res_variablesSelect[[ind_cyto]] = data.frame(variable = variablesSelect[[ind_cyto]])
}else{
res_variablesSelect[[ind_cyto]] = ""
}
}
return(res_variablesSelect)
}
# La fonction varAnnotation_gene_6blocks permet de fournir des informations sur les variables
# sélectionnées pour un design.
varAnnotation_gene_6blocks <-function(variablesSelect,
data_transcripto_col,
data_transcripto_tae,
annot_metageno_caecum,
metavar_metaboLC_S1,
metavar_resBio,
metavar_cyto)
{
res_variablesSelect = variablesSelect
noms_blocks = names(variablesSelect)
ind_transcripto_col = which(noms_blocks == "transcripto_col")
if(length(ind_transcripto_col) != 0)
{
varSelect_transcripto_colTemp = variablesSelect[[ind_transcripto_col]]
if(length(varSelect_transcripto_colTemp) != 0)
{
varSelect_transcripto_col = sapply(1:length(varSelect_transcripto_colTemp), FUN = function(i){
variable_i = varSelect_transcripto_colTemp[i]
res = gsub("Colon_", "", variable_i, fixed = TRUE)
return(res)
})
dataframe_varSelect_transcripto_col = data.frame(GeneName = varSelect_transcripto_col)
dataframe_annot_transcripto_col = data.frame(ProbeName = data_transcripto_col$genes$ProbeName,
GeneName = data_transcripto_col$genes$GeneName,
Description = data_transcripto_col$genes$Description,
SystematicName = data_transcripto_col$genes$SystematicName)
tab_transcripto_col = join(x = dataframe_varSelect_transcripto_col, y = dataframe_annot_transcripto_col,
type = "inner",
by = "GeneName")
res_variablesSelect[[ind_transcripto_col]] = tab_transcripto_col
}else{
res_variablesSelect[[ind_transcripto_col]] = ""
}
}
ind_transcripto_tae = which(noms_blocks == "transcripto_tae")
if(length(ind_transcripto_tae) != 0)
{
varSelect_transcripto_taeTemp = variablesSelect[[ind_transcripto_tae]]
if(length(varSelect_transcripto_taeTemp) != 0)
{
varSelect_transcripto_tae = sapply(1:length(varSelect_transcripto_taeTemp), FUN = function(i){
variable_i = varSelect_transcripto_taeTemp[i]
res = gsub("TAE_", "", variable_i, fixed = TRUE)
return(res)
})
dataframe_varSelect_transcripto_tae = data.frame(GeneName = varSelect_transcripto_tae)
dataframe_annot_transcripto_tae = data.frame(ProbeName = data_transcripto_tae$genes$ProbeName,
GeneName = data_transcripto_tae$genes$GeneName,
Description = data_transcripto_tae$genes$Description,
SystematicName = data_transcripto_tae$genes$SystematicName)
tab_transcripto_tae = join(x = dataframe_varSelect_transcripto_tae, y = dataframe_annot_transcripto_tae,
type = "inner",
by = "GeneName")
res_variablesSelect[[ind_transcripto_tae]] = tab_transcripto_tae
}else{
res_variablesSelect[[ind_transcripto_tae]] = ""
}
}
ind_metageno_caecum = which(noms_blocks == "metageno_caecum")
if(length(ind_metageno_caecum) != 0)
{
varSelect_metageno_caecum_Temp1 = variablesSelect[[ind_metageno_caecum]]
if(length(varSelect_metageno_caecum_Temp1) != 0)
{
varSelect_metageno_caecum_Temp2 = sapply(1:length(varSelect_metageno_caecum_Temp1), FUN = function(i){
ch = strsplit(varSelect_metageno_caecum_Temp1[i], split = "")[[1]]
if(ch[1] == "X")
{
res = paste(ch[2:length(ch)], collapse = "")
}else{
res = varSelect_metageno_caecum_Temp1[i]
}
return(res)
})
dataframe_varSelect_metageno_caecum = data.frame(taxon = varSelect_metageno_caecum_Temp2)
dataframe_annot_metageno_caecum = data.frame(annot_metageno_caecum)
colnames(dataframe_annot_metageno_caecum)[1] = "taxon"
tab_metageno_caecum = join(x = dataframe_varSelect_metageno_caecum, y = dataframe_annot_metageno_caecum,
type = "inner",
by = "taxon")
res_variablesSelect[[ind_metageno_caecum]] = tab_metageno_caecum
}else{
res_variablesSelect[[ind_metageno_caecum]] = ""
}
}
ind_metaboLC_S1 = which(noms_blocks == "metaboLC_S1")
if(length(ind_metaboLC_S1) != 0)
{
if(length(variablesSelect[[ind_metaboLC_S1]]) != 0)
{
dataframe_varSelect_metaboLC_S1 = data.frame(Variable = variablesSelect[[ind_metaboLC_S1]])
dataframe_metavar_metaboLC_S1 = data.frame(metavar_metaboLC_S1)
dataframe_metavar_varSelect_metaboLC_S1 = join(x = dataframe_varSelect_metaboLC_S1, y = dataframe_metavar_metaboLC_S1,
type = "inner",
by = "Variable")
res_variablesSelect[[ind_metaboLC_S1]] = dataframe_metavar_varSelect_metaboLC_S1
}else{
res_variablesSelect[[ind_metaboLC_S1]] = ""
}
}
ind_resBio = which(noms_blocks == "resBio")
if(length(ind_resBio) != 0)
{
if(length(variablesSelect[[ind_resBio]]) != 0)
{
dataframe_varSelect_resBio = data.frame(Variable = variablesSelect[[ind_resBio]])
dataframe_metavar_resBio = data.frame(metavar_resBio)
dataframe_metavar_varSelect_resBio = join(x = dataframe_varSelect_resBio, y = dataframe_metavar_resBio,
type = "inner",
by = "Variable")
res_variablesSelect[[ind_resBio]] = dataframe_metavar_varSelect_resBio
}else{
res_variablesSelect[[ind_resBio]] = ""
}
}
ind_cyto = which(noms_blocks == "cyto")
if(length(ind_cyto) != 0)
{
if(length(variablesSelect[[ind_cyto]]) != 0)
{
dataframe_varSelect_cyto = data.frame(Variable = variablesSelect[[ind_cyto]])
dataframe_metavar_cyto = data.frame(metavar_cyto)
dataframe_metavar_varSelect_cyto = join(x = dataframe_varSelect_cyto, y = dataframe_metavar_cyto,
type = "inner",
by = "Variable")
res_variablesSelect[[ind_cyto]] = dataframe_metavar_varSelect_cyto
}else{
res_variablesSelect[[ind_cyto]] = ""
}
}
return(res_variablesSelect)
}
# La fonction varAnnotation_gene_7blocks permet de fournir des informations sur les variables
# sélectionnées pour un design.
varAnnotation_gene_7blocks <-function(variablesSelect,
data_transcripto_col,
data_transcripto_tae,
annot_metageno_caecum,
metavar_metaboLC_S1,
metavar_resBio,
metavar_cyto)
{
res_variablesSelect = variablesSelect
noms_blocks = names(variablesSelect)
ind_transcripto_col = which(noms_blocks == "transcripto_col")
if(length(ind_transcripto_col) != 0)
{
varSelect_transcripto_colTemp = variablesSelect[[ind_transcripto_col]]
if(length(varSelect_transcripto_colTemp) != 0)
{
varSelect_transcripto_col = sapply(1:length(varSelect_transcripto_colTemp), FUN = function(i){
variable_i = varSelect_transcripto_colTemp[i]
res = gsub("Colon_", "", variable_i, fixed = TRUE)
return(res)
})
dataframe_varSelect_transcripto_col = data.frame(GeneName = varSelect_transcripto_col)
dataframe_annot_transcripto_col = data.frame(ProbeName = data_transcripto_col$genes$ProbeName,
GeneName = data_transcripto_col$genes$GeneName,
Description = data_transcripto_col$genes$Description,
SystematicName = data_transcripto_col$genes$SystematicName)
tab_transcripto_col = join(x = dataframe_varSelect_transcripto_col, y = dataframe_annot_transcripto_col,
type = "inner",
by = "GeneName")
res_variablesSelect[[ind_transcripto_col]] = tab_transcripto_col
}else{
res_variablesSelect[[ind_transcripto_col]] = ""
}
}
ind_transcripto_tae = which(noms_blocks == "transcripto_tae")
if(length(ind_transcripto_tae) != 0)
{
varSelect_transcripto_taeTemp = variablesSelect[[ind_transcripto_tae]]
if(length(varSelect_transcripto_taeTemp) != 0)
{
varSelect_transcripto_tae = sapply(1:length(varSelect_transcripto_taeTemp), FUN = function(i){
variable_i = varSelect_transcripto_taeTemp[i]
res = gsub("TAE_", "", variable_i, fixed = TRUE)
return(res)
})
dataframe_varSelect_transcripto_tae = data.frame(GeneName = varSelect_transcripto_tae)
dataframe_annot_transcripto_tae = data.frame(ProbeName = data_transcripto_tae$genes$ProbeName,
GeneName = data_transcripto_tae$genes$GeneName,
Description = data_transcripto_tae$genes$Description,
SystematicName = data_transcripto_tae$genes$SystematicName)
tab_transcripto_tae = join(x = dataframe_varSelect_transcripto_tae, y = dataframe_annot_transcripto_tae,
type = "inner",
by = "GeneName")
res_variablesSelect[[ind_transcripto_tae]] = tab_transcripto_tae
}else{
res_variablesSelect[[ind_transcripto_tae]] = ""
}
}
ind_metageno_caecum = which(noms_blocks == "metageno_caecum")
if(length(ind_metageno_caecum) != 0)
{
varSelect_metageno_caecum_Temp1 = variablesSelect[[ind_metageno_caecum]]
if(length(varSelect_metageno_caecum_Temp1) != 0)
{
varSelect_metageno_caecum_Temp2 = sapply(1:length(varSelect_metageno_caecum_Temp1), FUN = function(i){
ch = strsplit(varSelect_metageno_caecum_Temp1[i], split = "")[[1]]
if(ch[1] == "X")
{
res = paste(ch[2:length(ch)], collapse = "")
}else{
res = varSelect_metageno_caecum_Temp1[i]
}
return(res)
})
dataframe_varSelect_metageno_caecum = data.frame(taxon = varSelect_metageno_caecum_Temp2)
dataframe_annot_metageno_caecum = data.frame(annot_metageno_caecum)
colnames(dataframe_annot_metageno_caecum)[1] = "taxon"
tab_metageno_caecum = join(x = dataframe_varSelect_metageno_caecum, y = dataframe_annot_metageno_caecum,
type = "inner",
by = "taxon")
res_variablesSelect[[ind_metageno_caecum]] = tab_metageno_caecum
}else{
res_variablesSelect[[ind_metageno_caecum]] = ""
}
}
ind_metaboLC_S1 = which(noms_blocks == "metaboLC_S1")
if(length(ind_metaboLC_S1) != 0)
{
if(length(variablesSelect[[ind_metaboLC_S1]]) != 0)
{
dataframe_varSelect_metaboLC_S1 = data.frame(Variable = variablesSelect[[ind_metaboLC_S1]])
dataframe_metavar_metaboLC_S1 = data.frame(metavar_metaboLC_S1)
dataframe_metavar_varSelect_metaboLC_S1 = join(x = dataframe_varSelect_metaboLC_S1, y = dataframe_metavar_metaboLC_S1,
type = "inner",
by = "Variable")
res_variablesSelect[[ind_metaboLC_S1]] = dataframe_metavar_varSelect_metaboLC_S1
}else{
res_variablesSelect[[ind_metaboLC_S1]] = ""
}
}
ind_metaboGC_S1 = which(noms_blocks == "metaboGC_S1")
if(length(ind_metaboGC_S1) != 0)
{
if(length(variablesSelect[[ind_metaboGC_S1]]) != 0)
{
res_variablesSelect[[ind_metaboGC_S1]] = data.frame(Variable = variablesSelect[[ind_metaboGC_S1]])
}else{
res_variablesSelect[[ind_metaboGC_S1]] = ""
}
}
ind_resBio = which(noms_blocks == "resBio")
if(length(ind_resBio) != 0)
{
if(length(variablesSelect[[ind_resBio]]) != 0)
{
dataframe_varSelect_resBio = data.frame(Variable = variablesSelect[[ind_resBio]])
dataframe_metavar_resBio = data.frame(metavar_resBio)
dataframe_metavar_varSelect_resBio = join(x = dataframe_varSelect_resBio, y = dataframe_metavar_resBio,
type = "inner",
by = "Variable")
res_variablesSelect[[ind_resBio]] = dataframe_metavar_varSelect_resBio
}else{
res_variablesSelect[[ind_resBio]] = ""
}
}
ind_cyto = which(noms_blocks == "cyto")
if(length(ind_cyto) != 0)
{
if(length(variablesSelect[[ind_cyto]]) != 0)
{
dataframe_varSelect_cyto = data.frame(Variable = variablesSelect[[ind_cyto]])
dataframe_metavar_cyto = data.frame(metavar_cyto)
dataframe_metavar_varSelect_cyto = join(x = dataframe_varSelect_cyto, y = dataframe_metavar_cyto,
type = "inner",
by = "Variable")
res_variablesSelect[[ind_cyto]] = dataframe_metavar_varSelect_cyto
}else{
res_variablesSelect[[ind_cyto]] = ""
}
}
return(res_variablesSelect)
}
# Integration 6 blocs norm sonde ------------------------------------------
# La fonction plotVarZoom permet de zoomer sur le cercle de corrélation et de récupérer les variables
# contenues dans ce rectangle.
plotVarZoom <-function(res_block_splsda,
comp = 1:2,
blocks,
block_Y = NULL,
vec_col,
cutoff,
rad.in = 0.5,
min.X = -1,
max.X = 1,
min.Y = -1,
max.Y = 1,
cex = 0.7,
cex_legend = 0.8,
pos = c(1.2, 0),
pch = 20,
inset = c(-0.25, 0))
{
if(class(res_block_splsda)[1] == "block.splsda")
{
circle = list()
circle[[1]] = ellipse(0, levels = 1, t = 1)
circle[[2]] = ellipse(0, levels = 1, t = rad.in)
circle = data.frame(do.call("rbind", circle), "Circle" = c(rep("Main circle", 100), rep("Inner circle", 100)))
MainCircle = circle[grep("Main circle", circle[, 3]), ]
InnerCircle = circle[grep("Inner circle", circle[, 3]), ]
if(length(blocks) > 1)
{
noms_bloc = names(res_block_splsda$variates)
mat_comp1 = sapply(blocks, FUN = function(i){
res = res_block_splsda$variates[[i]][, 1]
return(res)
})
colnames(mat_comp1) = noms_bloc[blocks]
mat_cor_comp1 = cor(mat_comp1)
mat_comp2 = sapply(blocks, FUN = function(i){
res = res_block_splsda$variates[[i]][, 2]
return(res)
})
colnames(mat_comp2) = noms_bloc[blocks]
mat_cor_comp2 = cor(mat_comp2)
} # Fin if(length(blocks) > 1).
# Pour chaque bloc, calcul des corrélations entre la première
# composante et les variables sélectionnées et les corrélations entre
# la deuxième composante et les variables sélectionnées. Pour la réponse,
# calcul de la corrélation entre les variables de la réponse et la première composante
# du premier bloc sélectionné et de la corrélation entre les variables de
# la réponse et la deuxième composante du premier bloc sélectionné.
liste_matCor_comp_var = list()
varSelect_comp1 = selectVar(res_block_splsda,
comp = comp[1])
varSelect_comp2 = selectVar(res_block_splsda,
comp = comp[2])
vec_nom_blockEtReponse = c()
blocksEtReponse = c(blocks, which(res_block_splsda$names$blocks == "Y"))
for(i in 1:length(blocksEtReponse))
{
indice_block_i = blocksEtReponse[i]
nom_blockEtReponse_i = res_block_splsda$names$blocks[indice_block_i]
if(nom_blockEtReponse_i == "Y")
{
if(!is.null(block_Y))
{
block_i = block_Y
comp1 = res_block_splsda$variates[[blocks[1]]][, comp[1]]
comp2 = res_block_splsda$variates[[blocks[1]]][, comp[2]]
vecCor_comp1_var = sapply(1:dim(block_i)[2], FUN = function(j){
cor(comp1, block_i[, j], use = "complete.obs")
})
vecCor_comp2_var = sapply(1:dim(block_i)[2], FUN = function(j){
cor(comp2, block_i[, j], use = "complete.obs")
})
}else{
cat("La réponse n'est pas saisie comme paramètre d'entrée", "\n")
}
}else{
comp1 = res_block_splsda$variates[[indice_block_i]][, comp[1]]
comp2 = res_block_splsda$variates[[indice_block_i]][, comp[2]]
varSelect_comp1_i = varSelect_comp1[[indice_block_i]][[1]]
varSelect_comp2_i = varSelect_comp2[[indice_block_i]][[1]]
varSelect_i = unique(c(varSelect_comp1_i, varSelect_comp2_i))
block_i = res_block_splsda$X[[indice_block_i]][, varSelect_i]
if(i == 1)
{
vecCor_comp1_var = sapply(1:dim(block_i)[2], FUN = function(j){
cor(comp1, block_i[, j], use = "complete.obs")
})
vecCor_comp2_var = sapply(1:dim(block_i)[2], FUN = function(j){
cor(comp2, block_i[, j], use = "complete.obs")
})
}else{
signeCor_comp1 = sign(mat_cor_comp1[1, indice_block_i])
vecCor_comp1_var = sapply(1:dim(block_i)[2], FUN = function(j){
res = signeCor_comp1*cor(comp1, block_i[, j], use = "complete.obs")
return(res)
})
signeCor_comp2 = sign(mat_cor_comp2[1, indice_block_i])
vecCor_comp2_var = sapply(1:dim(block_i)[2], FUN = function(j){
res = signeCor_comp2*cor(comp2, block_i[, j], use = "complete.obs")
return(res)
})
}
}
matCor_comp_var = rbind(vecCor_comp1_var,
vecCor_comp2_var)
colnames(matCor_comp_var) = colnames(block_i)
liste_matCor_comp_var[[i]] = matCor_comp_var