Factominer applied for DEGs

inst/templates/dea_results.Rmd

@@ -175,6 +175,18 @@ plot(evp)
 ```
 
 
+```{r config_PCA_DEA, echo = FALSE, results = "asis", warning = TRUE, message = TRUE}
+
+pca_data <- as.data.frame(t(all_data_normalized[["default"]]))
+prevalen_degs <- rownames(DE_all_genes[DE_all_genes$genes_tag == "PREVALENT_DEG",])
+pca_data <- pca_data[,prevalen_degs]
+
+```
+
+```{r child=file.path(template_folder,"facto_miner.Rmd")}
+
+```
+
 
 The complete results of the DEgenes Hunter differential expression analysis can be found in the "hunter\_results\_table.txt" file in the Common_results folder
 

inst/templates/expression_qc.Rmd

@@ -41,8 +41,6 @@ PerformanceAnalytics::chart.Correlation(raw_filter, histogram=TRUE, pch=19, log=
 
 ```
 
-
-
 ### **Heatmap and clustering showing correlation between replicates**
 **BROWN: higher correlation; YELLOW: lower**
 
@@ -52,84 +50,16 @@ res <- cor(raw_filter)
 gplots::heatmap.2(x = res, col = col, symm = TRUE, margins = rep(max(nchar(colnames(res))), 2), scale = "none", trace = "none")
 ```
 
-### **Principal Component Analysis**
-This is a PCA plot of the count values normalized following the default method and then they are scaled:
-
-```{r pca_plot_FM, echo = FALSE, results = "asis", warning = TRUE, message = TRUE}
+```{r config_PCA, echo = FALSE, results = "asis", warning = TRUE, message = TRUE}
 pca_data <- as.data.frame(t(all_data_normalized[["default"]]))
-std_pca <- FactoMineR::PCA(pca_data, scale.unit=TRUE, graph = FALSE)                                                     
-dim_to_keep <- get_PCA_dimensions(std_pca)
-
-rownames(target) <- as.character(target$sample)
-string_factors <- final_main_params$string_factors
-numeric_factors <- final_main_params$numeric_factors
-
-pca_data <- merge_factors(pca_data, target, string_factors)
-pca_data <- merge_factors(pca_data, target, numeric_factors)
-
-full_pca <- FactoMineR::PCA(pca_data,ncp = dim_to_keep, scale.unit=TRUE, graph = FALSE, quanti.sup = numeric_factors, quali.sup=string_factors)
-``` 
-#### **Representation of the samples in the two first dimension of PCA** 
-
-```{r PCA_plot, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'} 
-FactoMineR::plot.PCA(full_pca, invisible = "quali" , title = "")
-``` 
 
-#### **Representation of the samples and the categories of qualitative valiables in the two first dimension of PCA** 
-
-```{r PCA_extra_factors, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'} 
-
-FactoMineR::plot.PCA(full_pca, title = "")
 ```
 
-#### **Representation of the variable contribution to the PCA axis 1 and 2**
-
-```{r PCA_num_var_1_2, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = !is.null(numeric_factors)} 
-factoextra::fviz_pca_var(full_pca, axes = c(1, 2),select.var = list(name=numeric_factors), col.var="steelblue", title="") +
-		ggplot2::theme_minimal()
-
-``` 
-
-```{r PCA_num_var_3_4, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = (dim_to_keep > 3 & !is.null(numeric_factors)) } 
-cat("#### **Representation of the variable contribution to the PCA axis 3 and 4**")
-
-factoextra::fviz_pca_var(full_pca, axes = c(3, 4),select.var = list(name=numeric_factors), col.var="steelblue", title="") +
-		ggplot2::theme_minimal()
-
-``` 
-
-
-```{r biplot, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = !is.null(numeric_factors)} 
-cat("#### **Representation of the individuals and the variable contribution to the PCA axis 1 and 2**")
-factoextra::fviz_pca_biplot(full_pca, repel = TRUE,select.var = list(name=numeric_factors), col.var="steelblue", title="") +
-		ggplot2::theme_minimal()
-
-```
-
-```{r HC, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'}
-cat(paste("#### **Hierarchical clustering of individuals using first", dim_to_keep, "significant PCA dimensions**",sep=' '))
-res.hcpc <- FactoMineR::HCPC(full_pca, graph = FALSE)
-plot(res.hcpc , choice="tree", title="")
-
-```
-
-```{r HC_2, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'}
-cat(paste("#### **PCA representation of 1 and 2 axis with individuals coloured by its cluster membership. The first ", dim_to_keep, "significant PCA dimensions are used for HCPC**",sep=' '))
-plot(res.hcpc , axes=c(1,2), choice="map", draw.tree= FALSE, title="")
-```
-
-```{r print_tables, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'}
-
-dim_data <- FactoMineR::dimdesc(full_pca, axes=seq(1, dim_to_keep))
-
-
-dim_data_simp <- dim_data[grepl("Dim",names(dim_data))]
-test <- lapply(names(dim_data_simp), parse_dim_table, dim_data = dim_data_simp, tag = "####")
+```{r child=file.path(template_folder,"facto_miner.Rmd")}
 
 ```
 
 
-The samples are shown in the 2D plane and distributed by their first two principal components. This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects. It is also useful for identifying outlier samples. Control and treatment samples respectively may cluster together.
 
 ### **Visualizing normalization results**
 

inst/templates/facto_miner.Rmd

@@ -0,0 +1,78 @@
+
+
+
+
+### **Principal Component Analysis**
+This is a PCA plot of the count values normalized following the default method and then they are scaled:
+
+```{r , echo = FALSE, results = "asis", warning = TRUE, message = TRUE}
+std_pca <- FactoMineR::PCA(pca_data, scale.unit=TRUE, graph = FALSE)                                                     
+dim_to_keep <- get_PCA_dimensions(std_pca)
+
+rownames(target) <- as.character(target$sample)
+string_factors <- final_main_params$string_factors
+numeric_factors <- final_main_params$numeric_factors
+
+pca_data <- merge_factors(pca_data, target, string_factors)
+pca_data <- merge_factors(pca_data, target, numeric_factors)
+
+full_pca <- FactoMineR::PCA(pca_data,ncp = dim_to_keep, scale.unit=TRUE, graph = FALSE, quanti.sup = numeric_factors, quali.sup=string_factors)
+``` 
+#### **Representation of the samples in the two first dimension of PCA** 
+
+```{r , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'} 
+FactoMineR::plot.PCA(full_pca, invisible = "quali" , title = "")
+``` 
+
+#### **Representation of the samples and the categories of qualitative valiables in the two first dimension of PCA** 
+
+```{r , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'} 
+
+FactoMineR::plot.PCA(full_pca, title = "")
+```
+
+#### **Representation of the variable contribution to the PCA axis 1 and 2**
+
+```{r , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = !is.null(numeric_factors)} 
+factoextra::fviz_pca_var(full_pca, axes = c(1, 2),select.var = list(name=numeric_factors), col.var="steelblue", title="") +
+		ggplot2::theme_minimal()
+
+``` 
+
+```{r , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = (dim_to_keep > 3 & !is.null(numeric_factors)) } 
+cat("#### **Representation of the variable contribution to the PCA axis 3 and 4**")
+
+factoextra::fviz_pca_var(full_pca, axes = c(3, 4),select.var = list(name=numeric_factors), col.var="steelblue", title="") +
+		ggplot2::theme_minimal()
+
+``` 
+
+
+```{r , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = !is.null(numeric_factors)} 
+cat("#### **Representation of the individuals and the variable contribution to the PCA axis 1 and 2**")
+factoextra::fviz_pca_biplot(full_pca, repel = TRUE,select.var = list(name=numeric_factors), col.var="steelblue", title="") +
+		ggplot2::theme_minimal()
+
+```
+
+```{r , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'}
+cat(paste("#### **Hierarchical clustering of individuals using first", dim_to_keep, "significant PCA dimensions**",sep=' '))
+res.hcpc <- FactoMineR::HCPC(full_pca, graph = FALSE)
+plot(res.hcpc , choice="tree", title="")
+
+```
+
+```{r , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'}
+cat(paste("#### **PCA representation of 1 and 2 axis with individuals coloured by its cluster membership. The first ", dim_to_keep, "significant PCA dimensions are used for HCPC**",sep=' '))
+plot(res.hcpc , axes=c(1,2), choice="map", draw.tree= FALSE, title="")
+```
+
+```{r , echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'}
+
+dim_data <- FactoMineR::dimdesc(full_pca, axes=seq(1, dim_to_keep))
+
+
+dim_data_simp <- dim_data[grepl("Dim",names(dim_data))]
+test <- lapply(names(dim_data_simp), parse_dim_table, dim_data = dim_data_simp, tag = "####")
+
+```

inst/templates/main_report.Rmd

@@ -30,19 +30,19 @@ output:
 </style>
 
 
-```{r child="expression_qc.Rmd"}
+```{r child=file.path(template_folder,"expression_qc.Rmd")}
 ```
 
-```{r child="dea_results.Rmd", eval = any(grepl("[DENL]",opt$modules))}
+```{r child=file.path(template_folder,"dea_results.Rmd"), eval = any(grepl("[DENL]",opt$modules))}
 ``` 
 
-```{r child="WGCNA_results.Rmd", eval = grepl("W", opt$modules)}
+```{r child=file.path(template_folder,"WGCNA_results.Rmd"), eval = grepl("W", opt$modules)}
 ``` 
 
-```{r child="diffcoexp_results.Rmd", eval = grepl("X", opt$modules)}
+```{r child=file.path(template_folder,"diffcoexp_results.Rmd"), eval = grepl("X", opt$modules)}
 ``` 
 
-```{r child="PCIT_results.Rmd", eval = grepl("P", opt$modules)}
+```{r child=file.path(template_folder,"PCIT_results.Rmd"), eval = grepl("P", opt$modules)}
 ``` 
 
 ### **Values of options used to run DEGenesHunter**