diff --git a/inst/scripts/multivar_mine.R b/inst/scripts/multivar_mine.R
index dc3efe6..0154b30 100755
--- a/inst/scripts/multivar_mine.R
+++ b/inst/scripts/multivar_mine.R
@@ -15,18 +15,21 @@ if( Sys.getenv('DEGHUNTER_MODE') == 'DEVELOPMENT' ){
   root_path <- file.path(main_path_script, '..', '..')
   # Load custom libraries
   custom_libraries <- list.files(file.path(root_path, "R"), full.names = TRUE)
+    # template_folder <- file.path(root_path, 'inst/templates')
+  template_folder <- file.path(root_path, 'inst/templates', 'htmlreport_migration')
 
   invisible(sapply(custom_libraries, source)) # source(file.path(root_path, 'R', lib))
   
-  template_folder <- file.path(root_path, 'inst', 'templates')
 }else{
   require('ExpHunterSuite')
   root_path <- find.package('ExpHunterSuite')
-  template_folder <- file.path(root_path, 'templates')
+  # template_folder <- file.path(root_path, 'templates')
+  template_folder <- file.path(root_path, 'templates', 'htmlreport_migration')
 }
 
 
 
+
 # Prepare command line input 
 option_list <- list(
   optparse::make_option(c("-i", "--input_file"), type="character", default=NULL,
@@ -43,10 +46,10 @@ option_list <- list(
    optparse::make_option(c("-t", "--transpose"), type="logical", action = "store_true", 
     default=FALSE,
     help="If activated, rows are treated as variables and columns as samples."),
-   optparse::make_option(c("-S", "--add_cualitative_vars"), type="character", 
+   optparse::make_option(c("-S", "--add_qualitative_vars"), type="character", 
     default=NULL,
     help="Variables in the input to be treated as supplementary QUALITATIVE variables"),
-  optparse::make_option(c("-N", "--add_cuantitative_vars"), type="character", 
+  optparse::make_option(c("-N", "--add_quantitative_vars"), type="character", 
     default=NULL,
     help="Variables in the input to be treated as supplementary QUANTITATIVE variables"),
   optparse::make_option(c("-s", "--add_samples"), type="character", 
@@ -58,12 +61,12 @@ opt <- optparse::parse_args(optparse::OptionParser(option_list=option_list))
 
 input_file <- read.table(file.path(opt$input_file), header = TRUE, row.names = 1)
 
-if (!is.null(opt$add_cuantitative_vars)) {
-	opt$add_cuantitative_vars <- split_str(opt$add_cuantitative_vars, ",")
+if (!is.null(opt$add_quantitative_vars)) {
+	opt$add_quantitative_vars <- split_str(opt$add_quantitative_vars, ",")
 }
 
-if (!is.null(opt$add_cualitative_vars)) {
-	opt$add_cualitative_vars <- split_str(opt$add_cualitative_vars, ",")
+if (!is.null(opt$add_qualitative_vars)) {
+	opt$add_qualitative_vars <- split_str(opt$add_qualitative_vars, ",")
 }
 
 if (!is.null(opt$add_samples)) {
@@ -76,16 +79,30 @@ if(!file.exists(opt$output_files))
 if (opt$analysis_type == "pca") {
 	pca_res <- compute_pca(pca_data = input_file,
 							transpose = opt$transpose,
-							string_factors = opt$add_cualitative_vars, 
-							numeric_factors = opt$add_cuantitative_vars,
+							string_factors = opt$add_qualitative_vars, 
+							numeric_factors = opt$add_quantitative_vars,
 							add_samples = opt$add_samples)
 	pca_output <- file.path(opt$output_files, "PCA_results")
 	dir.create(pca_output)
 
 	write_general_pca(pca_res, pca_output)
 
-    rmarkdown::render(input = file.path(template_folder, 'main_PCA.Rmd'),
-            intermediates_dir = file.path(opt$output_files, "tmp"),
-                      output_file = file.path(opt$output_files, "PCA_report.html"))
+  devtools::load_all("/mnt/home/users/bio_267_uma/josecordoba/software/htmlreportR")
+  source_folder <- find.package('htmlreportR')
+
+  if( Sys.getenv('HTMLREPORTER_MODE') == 'DEVELOPMENT' )
+    source_folder <- file.path(source_folder, "inst")
+
+    pca_res$string_factors <-opt$add_qualitative_vars
+    pca_res$numeric_factors <- opt$add_quantitative_vars
+    plotter <- htmlReport$new(title_doc = "PCA report", 
+                          container = pca_res, 
+                          tmp_folder = file.path(opt$output_files, "tmp"),
+                          src = source_folder,
+                          compress_obj = FALSE,
+                          type_index = "menu")
+  
+  plotter$build(file.path(template_folder, 'main_PCA.txt'))
+  plotter$write_report(file.path(opt$output_files, "PCA_report.html"))
 
 }
\ No newline at end of file
diff --git a/inst/templates/htmlreport_migration/facto_miner.txt b/inst/templates/htmlreport_migration/facto_miner.txt
new file mode 100644
index 0000000..27cbda6
--- /dev/null
+++ b/inst/templates/htmlreport_migration/facto_miner.txt
@@ -0,0 +1,385 @@
+
+<!--begin.rcode setup
+
+### plotting_functions
+inertia_plot <- function(ggplot_obj) { 
+								ggplot_obj + 
+								ggplot2::geom_bar(ggplot2::aes(x = names, y = var, fill = sig), stat = "identity") + 
+								ggplot2::geom_line(ggplot2::aes(x = names, y = cum_var), stat = "identity", group = 1) + 
+								ggplot2::labs(x = "", y = "Percentage of total variance") + 
+								ggplot2::scale_y_continuous(sec.axis = ggplot2::sec_axis(~./100, 
+																						name = "Cumulative percentage of variance", 
+																						labels = scales::percent)) + 
+								ggplot2::theme(legend.position = "bottom", 
+															 axis.text.x = ggplot2::element_text(angle = 45, hjust = 1))
+}
+
+pairs_plot_pca <- function(data_frame) {
+
+	pca_scatter <- function(data, mapping,...){
+		 p <- ggplot2::ggplot(data, mapping)  
+		 p$mapping$alpha <- NULL
+		 p$mapping$color <- p$mapping$fill
+		p + ggplot2::geom_point() + 
+		ggplot2::geom_vline(xintercept = 0,  linetype="dashed")+ 
+		ggplot2::geom_hline(yintercept = 0,  linetype="dashed")
+	}
+	p <- GGally::ggpairs(data_frame, columns = seq(2, dim_to_keep +1),  
+		mapping = ggplot2::aes(fill = .data[[cual_var]], alpha = 0.5),
+		 lower = list(continuous = pca_scatter),
+		upper = list(continuous = "blank"), 
+		switch = "both") + 
+		ggplot2::theme_bw() 
+		leg <- ggpubr::get_legend(p[1,1])
+		leg <- ggpubr::as_ggplot(leg)
+		p <- GGally::putPlot(p, leg, 1,dim_to_keep)
+	p[dim_to_keep, dim_to_keep] <- p[dim_to_keep, dim_to_keep] +ggplot2::coord_flip()
+  p
+}
+
+plot_pca <- function(pca_data) {
+	factoextra::fviz_pca_ind(pca_data, repel = TRUE)
+}
+
+plot_pca_hab <- function(pca_data) {
+	factoextra::fviz_pca_ind(pca_data, repel = TRUE, habillage = "Task")
+}
+
+plot_pca_qual <- function() {
+	plot(FactoMineR::plot.PCA(data_frame, title = ""))
+}
+
+plot_pca_quant <- function(pca_data) {
+	factoextra::fviz_pca_var(pca_data, axes = c(1, 2),select.var = list(name=plotter$hash_vars$numeric_factors), col.var="steelblue", title="") +
+		ggplot2::theme_minimal()
+}
+
+plot_biplot_quant <- function(pca_data) {
+	factoextra::fviz_pca_biplot(pca_data, repel = TRUE,select.var = list(name=plotter$hash_vars$numeric_factors), col.var="steelblue", title="") +
+			ggplot2::theme_minimal()
+
+}
+
+plot_hcpc <- function(){
+ plot(data_frame , choice="tree", title="")
+}
+
+plot_hcpc_map <- function(){
+	plot(data_frame , axes=c(1,2), choice="map", draw.tree= FALSE, title="")
+}
+
+plot_quant_heat <- function(data_frame){
+	gg_heatmap(data_frame, x_axis="dimension", y_axis="factor",fill = "correlation", 
+		text_plot="text_corr", text_size = 4,
+		col = c("#3b71ff", "#f1f1f1", "#ff3b3b"))
+}
+
+plot_qual_heat <- function(data_frame){
+	gg_heatmap(data_frame, x_axis="dimension", y_axis="factor",fill = "R2", text_plot="text_adjust", text_size = 4,
+		col = c("#3b71ff", "#f1f1f1", "#ff3b3b"))
+}
+
+plot_cat_heat <- function(data_frame){
+	gg_heatmap(data_frame, x_axis="dimension", y_axis="factor",fill = "Estimate", text_plot="text_estimate", text_size = 4,
+		col = c("#3b71ff", "#f1f1f1", "#ff3b3b") )
+}
+
+
+## inertia_plot
+inertia <- as.data.frame(plotter$hash_vars$pca_data$eig)[,c(2,3)]
+inertia$names <- factor(gsub("comp ", "PC", rownames(inertia)), levels= gsub("comp ", "PC", rownames(inertia)))
+colnames(inertia) <- c("var", "cum_var", "names")
+inertia$sig <- ifelse(rownames(inertia) %in% paste("comp", seq(1, plotter$hash_vars$dim_to_keep)), "significant", "no_significant")
+plotter$hash_vars[["inertia"]] <- inertia
+
+## pca_all
+dims <- as.data.frame(plotter$hash_vars$pca_data$ind$coord)
+colnames(dims) <- gsub("Dim.", "PC", colnames(dims))
+inertia$text <- paste(inertia$names, "\n(", round(inertia$var, 2),"%)", sep = "")
+colnames(dims)[colnames(dims) %in% inertia$names] <- inertia$text[match(colnames(dims), inertia$names)]
+dims$names <- rownames(dims)
+rownames(dims) <- NULL
+
+quali_sup <- plotter$hash_vars$pca_data$call$quali.sup$quali.sup
+quali_names <- as.list(colnames(quali_sup))
+names(quali_names) <- paste("dims",colnames(quali_sup), sep = "_")
+quali_sup$names <- rownames(quali_sup)
+all_dims <- lapply(quali_names, function(var) {
+
+	curr_dims <- merge(dims, quali_sup[,c("names",var)], by = "names")
+
+})
+plotter$hash_vars <- c(plotter$hash_vars, all_dims)
+
+
+## HCPC 
+plotter$hash_vars[["res_hcpc"]] <- FactoMineR::HCPC(plotter$hash_vars$pca_data, graph = FALSE)
+all_factor_clusters <- get_cluster_string_assoc(plotter$hash_vars$res_hcpc, plotter$hash_vars$string_factors)
+all_factor_clusters <- all_factor_clusters[all_factor_clusters$FDR <= 0.05,]
+
+if (nrow(all_factor_clusters) > 0 ) {
+	all_factor_clusters$FDR <- format(all_factor_clusters$FDR, scientific = TRUE, digits = 3)
+	all_factor_clusters$fisher.test.pval <- format(all_factor_clusters$fisher.test.pval, scientific = TRUE, digits = 3)
+	all_factor_clusters <- all_factor_clusters[,c("Cluster","Category","fisher.test.pval", "FDR")]
+}
+plotter$hash_vars[["all_factor_clusters"]] <- all_factor_clusters
+
+## dim data quant
+
+dim_data_quant <- plotter$hash_vars$dim_data_merged$quantitative
+dim_data_quant <- dim_data_quant[dim_data_quant$factor %in% plotter$hash_vars$numeric_factors,]
+if (nrow(dim_data_quant) > 0 ) {
+	dim_data_quant$dimension <- gsub("Dim.", "PC", dim_data_quant$dimension)
+	dim_data_quant$correlation <- round(dim_data_quant$correlation, digits = 2)
+	dim_data_quant$significance <- ifelse(dim_data_quant$p.value < 0.001, "***",
+					                ifelse(dim_data_quant$p.value < 0.01, "**",
+				                    ifelse(dim_data_quant$p.value < 0.05, "*", "")))
+	dim_data_quant$text_corr <- paste(as.character(dim_data_quant$correlation),as.character(dim_data_quant$significance), sep = " ")
+
+}
+plotter$hash_vars[["dim_data_quant"]] <- dim_data_quant
+
+##dim data qual
+dim_data_qual <- plotter$hash_vars$dim_data_merged$qualitative
+if (nrow(dim_data_qual) >0 ) {
+	dim_data_qual$dimension <- gsub("Dim.", "PC", dim_data_qual$dimension)
+	dim_data_qual$R2 <- round(dim_data_qual$R2, digits = 3)
+	dim_data_qual$significance <- ifelse(dim_data_qual$p.value < 0.001, "***",
+					                ifelse(dim_data_qual$p.value < 0.01, "**",
+				                    ifelse(dim_data_qual$p.value < 0.05, "*", "")))
+
+	dim_data_qual$text_adjust <- paste(as.character(dim_data_qual$R2),as.character(dim_data_qual$significance), sep = " ")
+}
+plotter$hash_vars[["dim_data_qual"]] <- dim_data_qual
+
+
+## dim data cat
+dim_data_cat <- plotter$hash_vars$dim_data_merged$qual_category
+if (nrow(dim_data_cat) > 0 ) {
+	dim_data_cat$dimension <- gsub("Dim.", "PC", dim_data_cat$dimension)
+
+	dim_data_cat$significance <- ifelse(dim_data_cat$p.value < 0.001, "***",
+					                ifelse(dim_data_cat$p.value < 0.01, "**",
+				                    ifelse(dim_data_cat$p.value < 0.05, "*", "")))
+	dim_data_cat$text_estimate <- paste(round(dim_data_cat$Estimate, digits = 2),dim_data_cat$significance, sep = " ")
+}
+plotter$hash_vars[["dim_data_cat"]] <- dim_data_cat
+
+
+end.rcode-->
+
+<!--begin.rcode overview
+###################################################################################################################
+#######################################    OVERVIEW  ##############################################################
+cat(plotter$create_title("<b>Overview</b>", "overview", 
+			hlevel = last_title + 1, indexable = TRUE, clickable = FALSE, t_id = "overview", clickable_text =""))
+cat("This section encompases a general overview of PCA application.")
+end.rcode-->
+
+<!--begin.rcode inertia_plot, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', fig.width = 5, fig.height = 4 
+	cat(plotter$create_title("<b>1 Inertia plot exploring variance explained by dimensions</b>", "inertia_plot", 
+			hlevel = last_title + 2))
+	cat("Graphical representation of PCA dimensions. The bars represent the percentage of total variance that summarize each dimension. <br>
+The line measures the percentage of total variance accumulated in previous dimensions. The color distinguishes between significan or no significant dimensions. <br>
+Only significant dimensions will be considered in the following plots.<br>")
+
+	cat(plotter$static_ggplot_main(id = "inertia", header = NULL, row_names = NULL,resizable = TRUE, plotting_function = inertia_plot))
+
+end.rcode-->
+
+
+<!--begin.rcode main_pca, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'
+	
+	cat(plotter$create_title("<b>2 Representation of the samples in the two first dimension of PCA</b>", "main_pc", 
+		hlevel = last_title + 2))
+	
+	cat(plotter$static_plot_main(id = "pca_data",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_pca))
+
+end.rcode-->
+
+
+<!--begin.rcode main_pca_hab, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = !is.null(target) 
+	cat("This is a simplificated plot of the samples displayed in the two main principal components. The color of the samples indicates their experimental condition.")
+	cat(plotter$static_plot_main(id = "pca_data",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_pca_hab))
+
+end.rcode-->
+
+
+
+<!--begin.rcode cat_vars_main, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'
+
+###################################################################################################################
+#######################################    CATEGORICAL VARIABLES  #################################################
+
+cat(plotter$create_title("<b>Categorical variables</b>", "cat_var", 
+			hlevel = last_title + 1, indexable = TRUE, clickable = FALSE, t_id = "cat_var", clickable_text =""))
+
+cat("This section explore the relationship between supplementary categorical variables (-S option), samples and principal components.")
+end.rcode-->
+
+
+<!--begin.rcode qual_pca , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis',fig.width = 8, fig.height = 8, eval = !is.null(plotter$hash_vars$string_factors)
+
+	cat(plotter$create_title("<b> 3 Representation of the samples and the categories of qualitative variables in the two first dimension of PCA</b>", "main_pc_qual", 
+			hlevel = last_title + 2))
+
+	cat(plotter$static_plot_main(id = "pca_data",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_pca_qual, plot_type = "autoplot" ))
+
+
+end.rcode-->
+
+
+<!--begin.rcode PCA_all, echo = FALSE,warning =FALSE, message=FALSE, results = 'asis', fig.width = 8, fig.height = 8, eval = !is.null(plotter$hash_vars$string_factors)
+	
+	cat(plotter$create_title("<b> 4 Comparison of significant dimensions significant dimensions</b>", "all_pc", 
+			hlevel = last_title + 2))
+	
+	cat("This plot compare the position of samples and their distribution in the significant dimensions. The color differenciate between the control (red) and treat (blue) samples.")
+	dim_to_keep <- plotter$hash_vars$dim_to_keep 
+	for (cual_var in unlist(quali_names)) {
+		cat(plotter$static_plot_main(id = paste("dims", cual_var, sep = "_"), custom_format = TRUE ,resizable = TRUE, plot_type = "print", plotting_function = pairs_plot_pca))
+	}
+
+
+end.rcode-->
+
+
+<!--begin.rcode dim_qual , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', fig.width = 5, fig.height = 5, eval = !is.null(plotter$hash_vars$string_factors)
+
+	cat(plotter$create_title("<b>5 Representation of R2 and P value of qualitative factors and PCA dimensions</b>", "main_pc_quant", 
+			hlevel = last_title + 2))
+
+
+	if (nrow(plotter$hash_vars$dim_data_qual) >0 ) {
+		cat(plotter$static_plot_main(id = "dim_data_qual",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_qual_heat))
+	} else {
+		cat("None of the factors were significantly associated with any dimension")
+	}
+end.rcode-->
+
+
+<!--begin.rcode dim_cat , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', fig.width = 5, fig.height = 5, eval = !is.null(plotter$hash_vars$string_factors)
+
+	cat(plotter$create_title("<b>6 Representation of estimated coordinated from barycentre and P value of qualitative factors and PCA dimensions</b>", "main_pc_quant", 
+			hlevel = last_title + 2))
+
+	if (nrow(plotter$hash_vars$dim_data_cat) > 0 ) {
+		cat(plotter$static_plot_main(id = "dim_data_cat",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_cat_heat))
+	} else {
+		cat("None of the categories were significantly associated with any dimension")
+	}
+
+end.rcode-->
+
+
+
+
+
+
+<!--begin.rcode quant_vars_main, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = !is.null(plotter$hash_vars$numeric_factors)
+
+###################################################################################################################
+#######################################    NUMERIC VARIABLES  #################################################
+
+cat(plotter$create_title("<b>Numerical variables</b>", "num_var", 
+			hlevel = last_title + 1, indexable = TRUE, clickable = FALSE, t_id = "num_var", clickable_text =""))
+
+cat("This section explore the relationship between supplementary quantitative variables (-N option), samples and principal components.")
+end.rcode-->
+
+
+
+<!--begin.rcode quant_pca , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = !is.null(plotter$hash_vars$numeric_factors)
+	
+	cat(plotter$create_title("<b>7 Representation of the variable contribution to the PCA axis 1 and 2</b>", "main_pc_quant", 
+			hlevel = last_title + 2))
+	
+	cat(plotter$static_plot_main(id = "pca_data",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_pca_quant))
+
+end.rcode-->
+ 
+
+
+
+<!--begin.rcode biplot, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = !is.null(plotter$hash_vars$numeric_factors)
+
+	cat(plotter$create_title("<b>8 Representation of the individuals and the variable contribution to the PCA axis 1 and 2</b>", "main_pc_quant", 
+			hlevel = last_title + 2))
+
+	cat(plotter$static_plot_main(id = "pca_data",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_biplot_quant))
+
+end.rcode-->
+
+<!--begin.rcode dim_quant , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', fig.width = 5, fig.height = 5, eval = !is.null(plotter$hash_vars$numeric_factors)
+
+	cat(plotter$create_title("9 Representation of correlation and P value of numeric factors and PCA dimensions", "main_pc_quant", 
+		hlevel = last_title + 2))
+
+	if (nrow(plotter$hash_vars$dim_data_quant) > 0 ) {
+		cat(plotter$static_plot_main(id = "dim_data_quant",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_quant_heat))
+	} else {
+		cat("None of the factors were significantly associated with any dimension")
+	}
+
+end.rcode-->
+
+
+<!--begin.rcode hcpc_main, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'
+
+###################################################################################################################
+#######################################    HCPC  ##################################################################
+
+cat(plotter$create_title("<b>HCPC</b>", "hcpc", 
+			hlevel = last_title + 1, indexable = TRUE, clickable = FALSE, t_id = "hcpc", clickable_text =""))
+
+cat("This section explore the groups of samples based on Hierarchical Clustering on Principal Components (HCPC) and the relationship of the clusters with supplementary variables.")
+end.rcode-->
+
+
+
+<!--begin.rcode HCPC , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'
+
+	cat(plotter$create_title(paste0("<b>10 Hierarchical clustering of individuals using first", plotter$hash_vars$dim_to_keep, "significant PCA dimensions</b>"), "main_pc_quant", 
+			hlevel = last_title + 2))
+
+	cat(plotter$static_plot_main(id = "res_hcpc",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_hcpc, plot_type = "autoplot" ))
+
+end.rcode-->
+
+
+<!--begin.rcode HCPC_map , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'
+
+	cat(plotter$create_title(paste0("<b>11 PCA representation of 1 and 2 axis with individuals coloured by its cluster membership. The first ", plotter$hash_vars$dim_to_keep, "significant PCA dimensions are used for HCPC</b>"), "main_pc_quant", 
+			hlevel = last_title + 2))
+
+	cat(plotter$static_plot_main(id = "res_hcpc",custom_format = TRUE ,resizable = TRUE, plotting_function = plot_hcpc_map, plot_type = "autoplot" ))
+
+end.rcode-->
+
+
+
+<!--begin.rcode cluster_string , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'
+
+	cat(plotter$create_title("12 Relationship between HCPC clusters and experiment design", "main_pc_quant", 
+			hlevel = last_title + 2))
+
+	cat("Fisher's exact test is computed between clusters and experimental treats. Fisher's exact test P values and FDR are showed.<br>")
+
+	if (nrow(plotter$hash_vars$all_factor_clusters) > 0 ) {
+
+		cat(plotter$table("all_factor_clusters", header = NULL, row_names = NULL, styled = "dt"))
+
+	} else {
+		cat("None of the clusters were significantly associated with any experimental group<br>")
+
+	}
+end.rcode-->
+
+
+
+
+
+
+<br><br><br><br><br><br><br><br>
+<br><br><br><br><br><br><br><br>
+<br><br><br><br><br><br><br><br>
\ No newline at end of file
diff --git a/inst/templates/htmlreport_migration/main_PCA.txt b/inst/templates/htmlreport_migration/main_PCA.txt
new file mode 100644
index 0000000..c5f6a37
--- /dev/null
+++ b/inst/templates/htmlreport_migration/main_PCA.txt
@@ -0,0 +1,30 @@
+
+<style>
+.content {
+    display: flex;
+    justify-content: center; /* Center horizontally */
+}
+.subcontent {
+    width: 50%; /* Set a width for the content */
+    justify-content: center; /* Horizontally center contents */    
+}
+</style>
+<div class="content">
+<div class="subcontent">
+
+<!--begin.rcode general_multivariate_config
+cat(plotter$create_title("<b>Principal Component Analysis Report</b>", "main_title", hlevel = 1))
+last_title <- 1
+
+target <- plotter$hash_vars$target
+end.rcode-->
+
+
+<!--begin.rcode child=file.path(template_folder,"facto_miner.txt"), eval = opt$analysis_type == "pca"
+
+end.rcode-->
+
+
+
+</div>
+</div>